esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method for Rapid Optimization Design of a Subsonic Tandem Blade
## 3 Analysis of the Aerodynamic Interaction between the Forward and Aft Blades
### The FB Flow Characteristic Influenced by AB  
The baseline FB was simulated in single-blade and tandem configurations, and its behavior, influenced by the AB, was analyzed. Comparison of FB blade performance in two configurations is shown in Figure 7 (the reference plane was kept in the same position). The FB blade loss increases by approximately 8% in the tandem configuration. Nevertheless, the incidence range is almost the same, i.e., the incidence range of tandem blades is mainly decided by FB.  
To further understand the reason that leads to the increase of blade loss in the tandem configuration, the comparison of isentropic Mach number distributions in three typical operating conditions is shown in Figure 8. The distributions in the single-blade configuration are similar to the CD airfoil. However, in the tandem configuration, the airflow decelerates near the PS trailing edge and the aerodynamic load increases near the FB PS trailing edge, which makes the isentropic Mach number distributions deviate from the ideal profile of CD airfoils. As shown in Figure 9, the stagnation effect of AB leads to a change in the static pressure field near the AB leading edge, and it can be regarded as an additional pressure field induced by AB. In the present relative position of FB and AB, the FB trailing edge is located in a high-pressure region caused by AB, which influences the FB isentropic Mach number distributions and causes a further increase of blade loss. It should be emphasized that static pressure changes rapidly near the AB leading edge; therefore, the effect of the additional pressure field is obviously influenced by FB and AB's relative position. Hence, it is necessary to select a proper relative position before the elaborate optimization.  
Figure 8: Comparison of the FB isentropic Mach number in single-blade and tandem configurations. (**a**) Incidence = \(-5^{\circ}\); (**b**) incidence = \(-1^{\circ}\); (**c**) incidence = \(+5^{\circ}\).  
Figure 7: Forward blade (FB) loss coefficients for single-blade and tandem configurations.
## 5 Experimental Setup
### Evaluation Metrics  
The quality of ElliottAgents outputs was assessed using a framework focused on its pattern recognition capabilities, predictive accuracy, and usability for end-users. Our evaluation methodology revolved around the system's ability to deliver actionable insights based on identified Elliott wave patterns while maintaining alignment with theoretical constructs like Fibonacci ratios [4]. We designed the evaluation to measure:  
* Pattern recognition: Verifying the system accuracy in detecting Elliott wave structures such as impulsive and corrective waves.
* Actionable insights: Evaluating the relevance and clarity of trading recommendations, including specific entry, exit, stop-loss, and take-profit levels and visualization of current market situation on a chart.
* Predictive accuracy: Comparing forecasted price movements and targets to actual outcomes using historical data.
* Adaptability: Testing the system across diverse financial instruments and market conditions.
* Backtesting impact: Quantifying the effect of DRL on improving pattern identification and subsequent predictions.  
The experimental framework focused on validating ElliottAgents' performance through a set of specific use cases. Each use case represented a common scenario in financial analysis. The Table 2 summarizes these use cases and their objectives.  
Although the EWP identifies thirteen distinct wave patterns in market movements, including various complex formations such as triangles, flats, and zigzags [4], our experimental framework concentrated on the most fundamental and frequently occurring patterns.
# A High Full Quality CMOS Image Sensor for Space Applications
## 2 Sensor Architecture
### Readout Circuit Design and Operation Procedure  
Figure 6a describes a readout circuit of a single channel and its control signals. One set of capacitors consisting of two sampling capacitors (C\({}_{\text{SIG}}\) and C\({}_{\text{RST}}\)) is used to sample the pixel signal and reset values, respectively, from one-pixel column. Three hundred sets of sampling capacitors in one channel process the pixel values that are generated from 300 pixel columns. A switched-capacitor amplifier holds the sampled pixel values for the output buffer while also working as a variable gain amplifier (VGA). A two-phase non-overlapped clock generator generates the clock signals (P\({}_{1}\) and P\({}_{2}\)) for the switched-capacitor amplifier. In order to sequentially transfer the sampled charges on the 300 sets of sampling capacitors, the clock signals are controlled by the column shift register (CSR), which is aligned with the CSR\({}_{\text{IN}}\).  
The circuit operation is as follows. As explained in Section 2.2, the pixel signal and reset values are sampled on the C\({}_{\text{SIG}}\) and C\({}_{\text{RST}}\), respectively, while the P\({}_{\text{SIG}}\) and P\({}_{\text{RST}}\) are high. This is illustrated in Figure 6b,c. After sampling is completed, the output enable signal (O\({}_{\text{EN}}\)) becomes high and this shorts the bottom plates of the C\({}_{\text{SIG}}\) and C\({}_{\text{RST}}\), as shown in Figure 6d. Then, the C\({}_{\text{SEL}}\) signal bus consisting of 300 control signals sequentially connects the top plates of the C\({}_{\text{SIG}}\) and C\({}_{\text{RST}}\) from the first set of sampling capacitors up to the 300 th set to the input nodes of the amplifier. This makes the sampled charges on the C\({}_{\text{SIG}}\) and C\({}_{\text{RST}}\) move to the feedback capacitor (C\({}_{\text{F}}\)) and generate differential voltage outputs as follows.  
Figure 6: \(Cont.\)  
\[\mathrm{V_{OUTP}}=\frac{\mathrm{V_{RST}}-\mathrm{V_{SIG}}}{2},\ \mathrm{V_{OUTN}}=- \frac{\mathrm{V_{RST}}-\mathrm{V_{SIG}}}{2}. \tag{2}\]  
Finally, the differential outputs are buffered by the two unit-gain amplifiers and routed to the off-chip ADC.  
Notice that the proposed switched-capacitor amplifier performs a single-ended to differential signal conversion. The differential signaling is extremely important for space application because the off-chip ADCs are often placed far from the CIS in a shielded location due to the radiation damage effect. Therefore, the output signal traces become very long and are susceptible to common noises and interferences. The differential signaling helps to remove common noises and interferences effectively and maintain the signal integrity.
# A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing
## 4 The Extreme-LT Routing Protocol
### Preliminary Tests  
#### 4.1.1 Range Tests  
Before testing the performance of the nodes using the protocol, outdoor tests have been carried out to determine the transmission power of the Cookie platform and the maximum acceptable range of communication. The setup for the tests consisted on a sender node, deployed in a fixed position at ground level, sending packets periodically to a receiver node. The transmission power was set at 20 dBm, which is the maximum power gain of the antenna. The distance between the nodes was initially 1.5 m from which the receiver node was moved away, increasing the separation until the sink node was eventually unable to receive messages. This end condition was met at an approximate  
Figure 10: Message exchange in a normal operation under Extreme-LT.  
distance of over 130 m, beyond that distance the RSSI of the incoming messages was \(-85\) dBm or lower and some of the packets were lost.
## 3 Results and Discussion
### Image Restoration of Degraded Images
In Table 4, the PSNR results further validate the hybrid model's superiority. While the degraded images had a median PSNR of 26.704 dB, the hybrid domain learning cGAN improved this to 37.558 dB, outperforming the standard cGAN's 33.061 dB. Additionally, the hybrid model demonstrated a notable advantage in handling difficult cases, with a minimum PSNR of 20.625 dB compared to the standard cGAN's 18.712 dB. are integrated into the model. The computational complexity of the entire model is analyzed in terms of floating-point operations (FLOPs), calculated using the PyTorch library. The generator has a total complexity of 18.25 GFLOPs, while the discriminator contributes 968.43 MFLOPs. The FFT and IFFT operations, required for domain transitions, each add 31.5 MFLOPs. Combining these, the computational complexity of the frequency domain unit, which includes the generator, discriminator, and FFT, amounts to 19.25 GFLOPs. Similarly, the spatial domain unit, comprising the generator, discriminator, and IFFT, also totals 19.25 GFLOPs. Figure 10: Image restoration result: (**a**) Visual comparison of pristine images (green), degraded images (red), images restored by the cGAN (yellow), and images restored by the hybrid domain learning cGAN (blue) in cases of validation using an experimentally obtained degraded dataset. Quantified results of the image restoration quality based on (**b**) SSIM index and (**c**) PSNR index for experimentally obtained degraded dataset. By summing the complexities of both units, the total computational complexity of the proposed hybrid frequency-spatial domain cGAN is calculated as  
\[\text{Total FLOPs}=19.25\times 10^{9}+19.25\times 10^{9}=38.5\times 10^{9}\text{ FLOPs }(38\.5\ \text{GFLOPs}). \tag{8}\]  
The detailed theoretical calculations and methodology for deriving the computational complexity can be found in Appendix A.3. Based on the total complexity of 38.5 GFLOPs, processing the model on a high-performance GPU, such as NVIDIA RTX 3060, requires approximately 3.02 milliseconds. It is important to note that this timing is specific to the given hardware and resolution.
# Construction Progress and Aviation Flight Test of BDSBAS
## 2 Overview of BDSBAS
### Tropospheric Correction  
Use the tropospheric delay model to calculate the tropospheric correction \(TC_{i}\), as shown in:  
\[TC_{i}=-\Big{(}d_{hyd}+d_{wet}\Big{)}\times m(E_{i}) \tag{10}\]  
with  
\[m(E_{i})=\frac{1.001}{\sqrt{0.002001+sin^{2}(E_{i})}} \tag{11}\]  
where \(m(E_{i})\) is the mapping function of the tropospheric delay. \(d_{hyd}\) and \(d_{wet}\) denote the dry and wet components of the troposphere, respectively, which are jointly calculated by the meteorological parameters such as air pressure, temperature, temperature change rate, water vapor pressure, and water vapor pressure change rate.  
At last, similar to the calculation of the ionospheric correction, the residual \(\sigma_{i,tropo}\) of the corrected tropospheric delay must be estimated at the same time to calculate the protection level (PL), as shown in:  
\[\sigma_{i,tropo}=\sigma_{TVE}\times m(E_{i}) \tag{12}\]  
where \(\sigma_{TVE}\) represents the vertical tropospheric delay error, which can generally be taken as 0.12 m.
# Mobility Classification of LoRaWAN Nodes Using Machine Learning at Network Level
## 2 An Overview on LoRa and LoRaWAN
### LoRaWAN  
In contrast to the proprietary PHY layer LoRa, the remaining part of the stack protocol, known as LoRaWAN, is open, and it is developed and maintained by the LoRa Alliance. The LoRaWAN network is typically deployed in a star-of-stars topology, where the EDs are connected through a single-hop link to one or many gateways, which, in turn, forward the packet traffic toward a common network server via standard IP protocols. The gateways act as a simple bridge between the end nodes and the network server; in fact, after decoding and adding some information regarding the quality of reception to the packets, they forward every message to their network server.  
The MAC of the LoRaWAN networks is essentially an ALOHA protocol controlled by the network server, which is in charge of assigning the transmission parameters to the end node by means of an adaptive data rate (ADR) mechanism.  
It must be highlighted that although the architecture of LoRaWAN resembles that of a cellular network, the LoRaWAN networks are \"cell-free\". An ED is not belonging to any \"cell\" identified by a GW, and there is no such a concept as \"handover\". Any packet sent by an ED is picked up by any GW that is able to decode it and all of the GW decoding the packet are sending it to the NS, which deduplicates the packets, selecting the one received with the best quality.  
A final remark on this Section is in order: the LoRa modulation and the LoRaWAN system are quite active areas of research and development, in academia and the industry. We would like to mention the paper [21] for a recent survey and the paper [22], which testifies to the increasing interest for LoRa modulation and LoRaWAN system for satellite communications in low Earth orbit, something hardly predictable a few years ago.
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 4 Methods and Models
### Taxi Passenger-Seeking Route Recommendation Model
Constraint (1) represents a non-duplicated, non-bifurcated path from the starting point to the end point, and the meaning of the left-hand side is the difference between the number of paths with node \(i\) as the starting point and the number of paths with node \(i\) as the end point. Constraint (2) represents that the problem is an open-ended path planning problem, where there is no need to go back to the starting point; here, we need to pay attention to \(j\in\{1,2,\cdots,n\}\). Constraint (3) represents that each node only needs to be served by 1 taxi; that is, the search for passengers is successful. Constraint (4) represents that the pathway \(A_{ij}\) is 1 if it exists in the shortest path from the starting point to the ending point; otherwise, it is 0. Constraint (5) represents that the distance for searching for passengers can not be more than 48 km. It is worth noting that in the taxi passenger search problem modeling research, the shortest distance model, minimum cost model, and taxi driver driving experience model have been based on the taxi driver driving experience model. This paper is based on the consideration of the maximum probability of carrying passengers, and due to the dense road traffic network in Chengdu, most of the passenger hotspot nodes are not directly connected to each other. There are many small hotspots between the nodes, the Manhattan distance is chosen for simple calculations, and many paths similar to the Manhattan distance can be found on the actual road, which is of strong practical application significance. Therefore, this section chooses to use Manhattan distance between nodes, which can ensure to find the passenger-seeking driving path with the maximum probability of carrying passengers under the relatively shortest distance. The NSGA-II algorithm is used to solve the above optimization model. The main difference between the NSGA algorithm and the classical genetic algorithm is the fast, non-dominated sorting during the selection operation, which increases the probability that the excellent individuals are retained. However, the NSGA algorithm still has the disadvantages of high algorithmic complexity, not using elite strategy, and needing to set the sharing radius artificially. In order to improve the shortcomings of the NSGA algorithm, the NSGA-II algorithm adopts fast, non-dominated sorting, which reduces the time complexity; it adopts the method of calculating congestion instead of the adaptation of the sharing radius, which guarantees the diversity of group individuals.
# Expanding Horizons: Taking Advantage of Graphene's Surface Area for Advanced Applications
## 4 Surface Area in Supercapacitors and Lithium-Ion Batteries
### Reduced Diffusion Path Length
The binding energies of lithium atoms atop hexagons were sensitive to their proximity to the side corners, but their activation energies remained fairly consistent. This resulted in the formation of a direction conducive to lithium conduction along the length of the ribbon [67]. The rate of lithium movement along these paths was only about six times higher than in graphene at room temperature. Moreover, the energy obstaclestoward the boundary regions were significantly lesser than those for pathways along the ribbon's length, indicating the possibility of creating high-power lithium anodes from these materials. Nevertheless, carbon electrodes tend to develop a solid electrolyte interface, potentially impacting the battery's capacity. If this interface forms predominantly at the edges, it may have a more substantial impact on the battery's energy and power density compared to traditional graphite electrodes. Nevertheless, the possible enhancement in diffusion rates along particular pathways and the increased reactivity of edges for lithium attachment in comparison to graphite necessitate experimental exploration. Conducting experiments is crucial to investigate potential enhancements in the diffusion coefficient toward specific pathways and the increased reactivity of edges for lithium adsorption, as opposed to graphite. Figure 8 presents a computational analysis of lithium atom movement and adsorption sites within graphene and GNRs. In graphene, movement direction B is analyzed with specific adsorption positions H, M, and T, where lithium can attach above a hexagon, between two carbon atoms, or directly on top of a carbon atom, respectively. The crisscross GNR illustration in Figure 7b demonstrates the B movement toward the side corners and the corresponding barriers to energy for this movement in the nanoribbon and graphene, highlighting the influence of atomic positioning and structural features on lithium mobility and binding energy in these materials. Figure 7: **(a)** Figure showing the graphene cell utilized in computations and depicting movement direction A within graphene. **(b)** Diagram showing the form A crisscross graphene nanoribbon (GNR) cell and several movement directions, designated A1 through A4. **(c)** Illustration showing a crossing graphene nanoribbon cell and a movement direction denoted as A\({}^{\prime}\), directed toward the side corners. **(d)** An illustration of the power boundaries for pathways A1 through A4 in the nanoribbon and path A within graphene. **(e)** The energy barriers for the A direction in graphene and the A\({}^{\prime}\) direction in the crisscross nanoribbon are shown. The figure was taken from [62] with permission. American Chemical Society, 2010. All rights reserved.
# Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors
## 4 Algorithmic Implementation Steps  
The framework of the human behavior recognition algorithm based on foreground trajectory and second-order motion difference descriptors is shown in Figure 1.  
The algorithm steps are as follows:  
(1) Video streams were inputted, and dense optical flow fields were calculated. Sampling candidates with large amounts of information were initially selected.  
(2) According to the magnitude of optical flow in each frame, the foreground region closely relating to behavior was determined, and the sample candidate points unrelated to the foreground were eliminated to obtain the starting point of the trajectory.  
(3) The foreground trajectory starting point was tracked in eight scale spaces. The foreground trajectory was formed, and the scaling factor between each two scale spaces was \(1/\sqrt{2}\). In this paper, the maximum length \(L\) of the trajectory was set to 15 to avoid the phenomenon of feature point drift.  
(4) A space-time block with a size of \(3\times 3\times 2\) was constructed along the foreground trajectory, and feature descriptors were extracted. HOG, HOF, MBH, and MDD proposed in this paper were used as human behavior descriptors, and a 492-dimensional feature vector was obtained, which contained 96-dimensional HOG features (\(2\times 2\times 3\times 8\)), 108-dimensional HOF features (\(2\times 2\times 3\times 9\)), 192-dimensional MBH features (\(2\times 2\times 3\times 8\times 2\)), and 96-dimensional MDD features (\(2\times 2\times 3\times 8\)).  
(5) The 492-dimensional global eigenvector was optimized, and dimensionality reduction was carried out. Principal component analysis (PCA) was introduced. The ratio of feature dimensions before and after dimensionality reduction was set to 2:1, and the final feature length was \(d=246\).  
(6) In order to further improve the ability of global features to represent video behavior, the Gauss mixture model (GMM) and FV were used to re-encode the features. In this paper, the number of Gauss kernels \(K=256\) was selected. Finally, an expectation maximization (EM) algorithm was used to solve the final feature dimension of \(2\times d\times K\) (i.e., 125,952 dimension).  
(7) Encoded features were fed into the SVM for classification.
# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 3 Advanced Clustering Approach
### Clustering Efficiency: Analysis Study
The results indicate that such a cluster is inefficient unless the distance exceeds 358 m. We even tested an unrealistic scenario where all WSN nodes are gathered in a single cluster, which is impossible due to the limited neighbor range of 20 m. However, in this case, clustering would only be more efficient than individual sending if the distance exceeds 810 m.  
In conclusion, clustering is efficient for nodes that are at least 75 m away from the BS. The efficiency of clustering is directly related to both the distance to the BS and the density of the cluster.