esa-sceva/satcom-chunk-collection huggingface dataset:
# Sources of Extraterrestrial Rare Earth Elements: To the Moon and Beyond
## 4 Lunar Differentiation
### The Lunar Magma Ocean
This sequence of differentiation events accounts for the old anorthositic lunar highlands samples (_c_. 4.57-4.34 Ga; summary of ages presented in [55]), which have been interpreted as representing primary plagioclase-rich flotation crust, and the younger mare basalts (the majority at \(c\). 4.0-2.0 Ga: [76], with a recent study suggesting magmatism may have occurred as recently as ~100 Ma: [77]), which were derived from source reservoirs in the lunar interior which had experienced previous melt (plagioclase) extraction (olivine and pyroxene cumulates, Figure 3b). This petrogenetic model for the anorthositic lunar highlands, and mare basalts, is further supported by the positive europium anomalies in the lunar highlands samples, complementary negative europium anomalies in the mare basalts, and the absence of plagioclase on the mare basalt liquidus [78; 79; 80; 81; 82; 83; 84]. At ~99% solid (e.g., [51; 69; 85; 86]), the remaining LMO liquids would have been relatively enriched in incompatible trace elements (ITEs), as these elements are not easily incorporated into the crystal structures of the major silicate phases: olivine, pyroxene (\(\pm\)Fe-Ti oxides), and plagioclase feldspar. These elements would have included potassium (K), the REEs, and phosphorous (P, collectively referred to as \"KREEP\": Figure 3b) and are hypothesized to have formed a late-stage reservoir between the solidifying lunar mantle and crust. This reservoir, a residuum from LMO differentiation, is referred to as urKREEP, with the Germanic prefix \"ur\" meaning primeval [85]. The source of KREEP signatures in lunar rocks has thus been associated with an urKREEP origin. It is noted here that there is currently debate regarding both the timing, and the duration, of LMO crystallization. Ages of lunar crustal rocks characterized by high proportions of anorthitic feldspar, and thus interpreted as representing primary lunar crust, range from 4.57 to 4.36 Ga ([87; 88] for a review see [55]) while models of solidification range from 10 to 200 Myrs depending on whether tidal eating is invoked ([69; 89] respectively). Ages associated with formation of the KREEP reservoir range from 4.48 Ga (from Apollo 14 zircons) [90] to 4.36 Ga (Lu-Hf isotopic systematics on KREEP basalts) [91].
###### Abstract
To reflect the spatial heterogeneity of the system, we divide the domain \(\Omega\) into two disjoint subdomains, \(\Omega=\Omega_{1}\cup\Omega_{2}\), and define a space-dependent mean field accordingly. In each subdomain, the governing equation is formulated by incorporating the corresponding average value of the phase variable. Specifically, the evolution of \(\phi(\mathbf{x},t)\) in \(\Omega_{1}\) is governed by  
\[\frac{\partial\phi(\mathbf{x},t)}{\partial t}=\Delta\Big{(}F^{\prime}(\phi( \mathbf{x},t))-\epsilon^{2}\Delta\phi(\mathbf{x},t))\Big{)}-\sigma\big{(} \phi(\mathbf{x},t)-\overline{\phi}_{1}\big{)},\quad\mathbf{x}\in\Omega_{1},\; \;t>0, \tag{2}\]  
where \(\overline{\phi}_{1}=\int_{\Omega_{1}}\phi(\mathbf{x},0)d\mathbf{x}/|\Omega_{1}|\) represents the spatial average of the initial phase variable over \(\Omega_{1}\). Similarly, in the subdomain \(\Omega_{2}\), we consider  
\[\frac{\partial\phi(\mathbf{x},t)}{\partial t}=\Delta\Big{(}F^{\prime}(\phi( \mathbf{x},t))-\epsilon^{2}\Delta\phi(\mathbf{x},t))\Big{)}-\sigma\big{(} \phi(\mathbf{x},t)-\overline{\phi}_{2}\big{)},\quad\mathbf{x}\in\Omega_{2},\; \;t>0, \tag{3}\]  
where \(\overline{\phi}_{2}=\int_{\Omega_{2}}\phi(\mathbf{x},0)d\mathbf{x}/|\Omega_{2}|\) denotes the average over \(\Omega_{2}\). By introducing a piecewise-defined mean value \(\phi(x)\), the equations on the two subdomains can be unified into a single equation as follows:  
\[\frac{\partial\phi(\mathbf{x},t)}{\partial t}=\Delta\Big{(}F^{\prime}(\phi( \mathbf{x},t))-\epsilon^{2}\Delta\phi(\mathbf{x},t))\Big{)}-\sigma\big{(} \phi(\mathbf{x},t)-\overline{\phi}(\mathbf{x})\big{)},\quad\mathbf{x}\in\Omega,\;\;t>0, \tag{4}\]where \(\bar{\phi}(\mathbf{x})\) is given by the piecewise definition  
\[\overline{\phi}(\mathbf{x})= \begin{cases}\overline{\phi}_{1}&\text{if }\mathbf{x} \in\Omega_{1},\\ \overline{\phi}_{2}&\text{if }\mathbf{x}\in\Omega_{2}.\end{cases}  \tag{5}\]  
It is important to emphasize that the proposed method is not restricted to cases with only two subdomains. The underlying framework allows straightforward extension to configurations that consist of multiple subdomains. This extension is achieved by applying the same principles to each additional subdomain without requiring fundamental changes to the overall algorithmic structure, which ensures the method's applicability to a wide range of problems involving spatial heterogeneity. This paper is organized as follows. Section 2 describes the computational scheme based on the Fourier spectral method combined with a stabilized splitting approach for solving the nonlocal CH equation with space-dependent parameters. Section 3 presents computational experiments illustrating the coarsening dynamics and phase separation behaviors under various initial conditions. Finally, conclusions and future perspectives are summarized. The corresponding implementation code, which supports the numerical methods and simulations discussed in the main text, is provided in Appendix A for reference and reproducibility.
## 4 Discussion
Further analysis such as Western blotting or ELISA should be performed to specifically determine ES effects on protein expressions. In addition, if BMPs induce osteo-differentiation of hDPSCs, relative outcome markers, such as Runx2, collagen I, alkaline phosphatase activity/expression, osteoclastin, and osteonectin, should thus be up-regulated [33]. Therefore, our future study will also focus on exploring the profiles of these outcome markers of osteogenic differentiation. As shown in Figures 4 and 5, when the assay was performed on day 21, i.e., the late stage of osteo-differentiation, there was no difference between the experimental and positive control groups, suggesting that ES plays a role in accelerating but not increasing the level of mineralization. Similarly, it has been reported that the mineralization of rat bone marrow stromal cells may only be improved when ES is applied at early stage of osteo-differentiation [33]. However, [PERSON] et al. have applied ES to pre-osteoblasts (MC3T3-E1), and their results showed that the level of Runx2 expression remains unchanged during the early stage [27]. Because pre-osteoblasts are committed cells, their results implied that ES may mainly promote stem cell differentiation to therefore accelerate mineralization. The promotion effect of ES treatment on osteogenesis has also been reported by [PERSON] et al. [53]. They seeded adipose-derived mesenchymal stem cells (AD-MSCs) to electrically conductive scaffold, and DCEF was applied to treat these seeded cells. Blockers of different voltage-gated ion channels were applied before ES treatment, and their results showed that the promotion effect of ES on AD-MSCs highly related to voltage-gated calcium channels. According to this study, we speculate that ES may promote the influx of calcium to bind calmodulin, by which CaM kinase is activated to regulate transcription factor of BMPs [54; 55].
## 4 Algorithms to Determine the Diameter of the DG Tree and Minimum Delay for Data Aggregation  
In this section, we describe algorithms to determine the diameter of a data gathering (DG) tree and the minimum delay for data aggregation spanning across all the nodes of the DG tree. We also discuss the time complexity of the benchmarking algorithm to determine minimum aggregation delay as well as present its proof of correctness. Finally, we illustrate examples to compute the minimum aggregation delay and illustrate the relationship between aggregation delay and metrics such as the tree diameter and the number of child nodes per intermediate node (further analyzed through simulations, Section 5).  
The diameter of a DG tree (also referred to as the height of the tree) is the maximum distance from any leaf node to the root of the tree [26]. We first identify the level of each node in the DG tree, with the root node (leader node) set to be at level 0, its immediate child nodes at level 1 and so on. Two intermediate nodes at a particular level or different levels can simultaneously communicate with their respective child nodes at the same time using different CDMA (Code Division Multiple Access) codes [14] to avoid any interference. An intermediate node could only communicate sequentially with its immediate downstream nodes using TDMA (Time Division Multiple Access) time slots (one data aggregation per time unit).
## 2 SAR Data, Field Data and Study Sites
### SAR Data
Further details on the acquisitions for each site are included in the Supplemental Information section. CSK, COSMO-SkyMed; TSX, TerraSAR-X. dynamic of the radar backscatter is larger and (iii) the spatial resolution is higher. The image acquisition dates, locations, mode, pixel size, polarization and incidence angles are shown in Table 1. Image mode, extent, pixel size, polarization and incidence angles were constant for each footprint. Full details of the acquisitions for each of the 13 sites are included in the Supplemental Information section (Table S1). CSK data are available from four X-band HH-SAR satellites with a 3.12-cm wavelength and a 16-day revisit period for the same satellite with the same observation angle. We used Stripmap mode (3-m resolution) at 10 sites with a footprint of 40 \(\times\) 40 km and ScanSAR Wideregion mode (15-m resolution) at one site with a footprint of 100 \(\times\) 140 km. Acquisition plans were made using one primary satellite from the constellation for each site with backup plans in place for the second, third and fourth satellites in the constellation in the event of a cancellation. These backup acquisitions are one, four and eight days after the primary acquisition pass. Rapid notification of a cancelation is required to change planned field visits to the backup date, but this proved to be challenging to implement consistently through the monitoring campaign whenever backups were required. TSX is provided by one X-band HH SAR satellite with a 3.11-cm wavelength and 11-day revisit period with the same observation angle. We used Stripmap mode (3 m resolution) at one site with a footprint of 30 \(\times\) 50 km and ScanSAR mode (10 m resolution) at one site with a footprint of \(100\times 150\) km. No backup plan was implemented for TSX.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 3 Experimental Results and Discussion
### Evaluation and Discussion
#### 3.2.3 Model Computational Efficiency and Computational Complexity Comparison  
We use the floating-point operations (FLOPs) with an input size of \(224\times 224\) to measure the computational cost of the proposed model and the other classification models listed in Section 3.1.1, as shown in Table 7. While the actual time cost may be affected by other factors, such as the GPU bandwidth and coding quality, the computational cost shows the speed upper bound [42]. From the results, it can be seen that the proposed model consumes the lowest FLOP of all models, 25% less than ResNet18, which proves that the proposed model effectively reduces the computational complexity. In addition, with the same 300 Epoch runs, the proposed model has the shortest running time. This fully proves the computational efficiency of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Sensitive** & **Specificity** & \(F_{1}\) **Score** \\ \hline Proposed Model & 96.37\% & 78.11\% & 100\% & 95.83\% & 0.8771 \\ Resnet18 & 91.59\% & 60.62\% & 100\% & 90.34\% & 0.7548 \\ Resnet34 & 92.00\% & 61.81\% & 100\% & 90.81\% & 0.7640 \\ Resnet50 & 89.45\% & 55.09\% & 100\% & 87.88\% & 0.7104 \\ Vgg16 & 84.67\% & 45.77\% & 100\% & 82.39\% & 0.6395 \\ Vgg19 & 85.41\% & 47.00\% & 100\% & 83.24\% & 0.6280 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of several models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Epochs** & **Flops** & **Running Time** \\ \hline Proposed Model & 300 & 1.36 G & 286 m 33 s \\ Resnet18 & 300 & 1.82 G & 317 m 24 s \\ Resnet34 & 300 & 3.67 G & 466 m 30 s \\ Resnet50 & 300 & 4.12 G & 534 m 7 s \\ Vgg16 & 300 & 15.5 G & 567 m 33 s \\ Vgg19 & 300 & 19.67 G & 661 m 19 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model computational efficiency and computational complexity comparison.
## 5 Numerical Investigations  
To quantify the possible gains of the proposed SCF relaying scheme we solve problem (P1) described in Section 4.3 to optimality for different values of weighting parameter \(\lambda\). For the communication model described in Section 4 we assume that the transceiver fixed circuit power dissipation is \(P_{c}=50\) mW (similar to [30]) and the received power threshold for successful communication is \(P_{r}=-52\) dBm [31]. The data rate for all links is \(B=1\) Mbps and the antenna height for the transceiver and the receiver is \(h_{t}=h_{r}=1.5\) m. For these values the following parameters can be deduced: The fixed circuit energy consumption for both the transmitter and receiver is \(e_{t}=e_{r}=\frac{P_{c}}{B}\). The energy consumed by the power amplifier is  
\[e_{d}=\frac{P_{r}(4\pi)^{2}}{B\
u^{2}} \tag{12}\]  
with pathloss exponent \(\eta=2\) for ranges less than the threshold distance  
\[d_{brake}=\frac{4\pi h_{t}h_{r}}{\
u} \tag{13}\]while for larger distances,  
\[e_{d}=\frac{P_{r}}{Bh_{t}^{2}h_{r}^{2}} \tag{14}\]  
with pathloss exponent \(\eta=4\).  
For the network model we assume that \(M=\{10,20,30\}\) active users request the uplink of a file of size \(F=4\) Mbit to the BS of radius \(R=400\) m. Further \(N=\{10,20,30,40\}\) vehicles travel along each direction of the stretch of road with maximum speed \(v_{max}=13\) m/s. Figure 5a shows the energy consumption of the average forwarding path against different message delivery delays. Importantly, for delay insensitive services, the SCF relaying scheme achieves approximately 30 times savings in energy compared to the direct link. Clearly, these energy gains increase with the cell radius as in this case, users have to transmit on average over longer distances on the direct link.  
Looking into the performance of the SCF scheme for varying number of candidate forwarding nodes in Figure 5b, it interesting to note that significant savings are achieved even for low vehicle densities. However as the number of active users increases so is the load carried within the buffers of relay nodes which in turn have to start transmitting to the BS from a longer distance; reducing in effect the energy gains. On the other hand, when the number of relays is substantially large, the full potential of SCF relaying can be realized. As seen in Figure 5b the effect on the energy gains of considering increasingly many vehicles within the cell, reduces considerably above a threshold value.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 2 Related Work
### Broadcast Storm in Ad-Hoc NDN  
The storm issue of broadcast has been deeply studied in routing protocols in traditional MANETs [16]. The consequences of the broadcast storm include: redundant broadcast generates much transmission overhead and costs much network bandwidth; contention and collision lead to packet loss. Although ad-hoc NDN bears similarity with traditional MANETs [16] when interests are flooded, fundamental differences exist between the two. First, their purposes and broadcast actions are different. Routing discovery in MANETs only uses broadcast in routing request flooding to find the unique destination. The destination then replies via unicast to the source node to set up a routing path. Only after this phase, is the source able to forward data packets to the destination along the path via unicast. In ad-hoc NDN, broadcasting is used in flooding an interest from a consumer to find matching data. The matching data can exist at the providers as well at the caching nodes. The matching data are returned to the consumer along the reverse path via broadcast. This allows the nodes on the path or close to the path (via overhearing) to cache the data for future interests. Second, MANETs and ad-hoc NDNs deal with the broadcast issue with a different layering consideration in the protocol stack. Flooding route request in MANET is part of routing protocol, handled at the network layer. In NDN architecture, forwarding data chucks is the network layer. NDN is a universal overlay that can run over strategies such as IP, UDP, P2P and broadcast [17]. The wireless ad hoc network is a broadcast strategy at the strategy layer below the network layer. Methods to handle the broadcast problem are thus implemented below the network layer. Implementation-wise, ad-hoc NDN uses the _network-face-over-ad-hoc_ in NDN architecture when sending interest and receiving data. While the native NDN forwarding daemon is in the network layer, the implementation of the proposed work will interface both the network and MAC layers.
# Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize
## 2 Materials and Methods  
### Proposed UAS Data Ferrying Designs  
#### 2.1.1 Criteria  
The wireless communication system was designed to develop a communication channel that sufficiently covers the 50-ha research area at the University of Nebraska's Eastern Nebraska Research and Extension Center (ENREC) near Mead, Nebraska. The maximum permissible UAS flight height (as per Federal Aviation Administration) is 122 m above ground, thus limiting the testing for successful wireless communication to a 122 m height. Signals between the transceiver (primary) radio and the secondary radio must overlap within their emitted signal footprints to establish communication. Thus, the line of sight (clear communication path) between the transceiver radio and the secondary radio must be considered for a typical US mid-west maize farm (area > 50 ha, i.e., diameter > 800 m). In addition, communication depending on the location of the node station radio could be affected by the vegetative canopy itself, which could cause signal propagation absorption, reflection, attenuation, and scattering [23]. Therefore, the desired range of distance between the radios could be approximately 800 m (considering the size of the farm is 50 ha, and UAS is maneuvering at a height of 122 m) to establish the communication.  
The basic scheme is to retrofit the UAS with a wireless transceiver as a data ferry to retrieve datasets from the wireless sensor network (WSN) as the UAS flies across a typical US mid-west maize field. The design should consider a scalable network size scenario where more radios are needed. The transmission time from each node station for the study field should be approximately two minutes to accommodate the battery charge needed by the UAS while it maneuvers over the sensor node stations and downloads data. Thus, the transmission rate is expected to be a few hundred kilobits per second with the minimum data transfer of approximately 2-3 megabits per flight since each sensor node station will record measurements from five sensors frequently.
## 4 Stray Node Detection Protocol and Method of Avoiding Message Collision
### Autonomous Response-Type Stray Node Detection Protocol  
#### 4.1.1 Processing Logic  
Each transmission node performs the following and a flowchart for this logic is shown in Figure 5.  
1. The area within which a message from the leader node can reach a node is adjusted by varying the transmission power of each node.
2. The normal node sends a message at intervals of T. 1. A node that has received the message examines the group ID in IE1 in the message to see if the message was sent by a node belonging to the same group. If so, go to Step 3. 2. If the node finds that the group ID in the message is different from its own group ID, it discards the message, go to Step 2-1
3. The receiving node checks IE2 and IE4 to see if it has received the same message before. 1. If it has, it discards the message, go to Step 2-1. 2. If it has not, go to Step 4.
4. Branch depending on the node type. 1. If it is the leader node, go to Step 5. 2. If it is a normal node, it checks IE5. 3. Number of hops < hop limit, the node adds one to IE5 and forwards the message. 4. Number of hops = hop limit, it discards the message, go to Step 2-1.
5. The leader node examines IE5 in the message returned by each node. 1. If the number of hops is 1, it determines that the sending node is not a stray node, go to Step 2-1. 2. If the number of hops is 2 or greater, it determines that the sending node is a stray node, and alerts the instructor, go to Step 2-1.  
Figure 4: Assumed protocol stack.
## 4 System Performance Discussion
### Parameter Discussion on System Performance  
Compared with the traditional MIMO radar, single RF link MIMO radar has its disadvantages. For example, it uses too many SPDT switches, which will affect the overall detection probability of the system, such as the bit error rate of the control signal. Although the ideal single RF MIMO radar has excellent performance (although in nonideal circumstances), the switching time, isolation, switch loss, and other parameters of the SPDT switch will exert influence on the performance of single RF link MIMO radar to some extent, such as the radar dynamic range and the target detection probability.  
This section is based on the radar simulation platform established in the Section 3, and the theoretical analysis and simulation verification are discussed. Different parameters that influence the performance of the single RF link MIMO radar system are analyzed. For the simulation described in  
Figure 12: Receiver operating characteristic (ROC) curves of two radar systems. The points on the curve reflect the sensitivity of the two different radar systems. The areas under curve (AUC) of the single link MIMO radar is larger than the traditional MIMO radar and has the better detection ability.  
this paper, the initial simulation system parameter setup for single RF link MIMO radar is shown in Table 4.
# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
###### Abstract  
Device identification based on radio frequency fingerprinting is widely used to improve the security of Internet of Things systems. However, noise and acquisition inconsistencies in raw radio frequency signals can affect the effectiveness of classification, identification and authentication algorithms used to distinguish Bluetooth devices. This study investigates how the RF signal preprocessing techniques affect the performance of a support vector machine classifier based on radio frequency fingerprinting. Four options derived from an RF signal preprocessing technique are evaluated, each of which is applied to the raw radio frequency signals in an attempt to improve the consistency between signals emitted by the same Bluetooth device. Experiments conducted on raw Bluetooth signals from twentyfour smartphone radios from two public databases of RF signals show that selecting an appropriate RF signal preprocessing approach can significantly improve the effectiveness of a support vector machine classifier-based algorithm used to discriminate Bluetooth devices.  
RF device identification; IoT Authentication; RF fingerprints; Bluetooth devices; support vector machine