esa-sceva/satcom-chunk-collection huggingface dataset:
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 3 System Architecture and Key Technologies
### Orbital Analysis  
Frozen orbits are special orbits [39; 40; 41; 42; 43] that allow a satellite to minimize natural drifting due to central body disturbances. This is carried out by carefully choosing orbital parameters, and allows the satellite to maintain its orbital parameters unchanged by rarely requiring orbital corrections. Frozen orbits are particularly useful for long-term missions to the Moon, as they allow satellites to remain in a stable and constant orbit without the need for constant orbital corrections. However, frozen orbits require extreme precision in their planning and maintenance, as even a small deviation from the desired trajectory can cause the satellite to leave the frozen orbit. Several studies have been conducted, particularly on lunar frozen orbits, finding that most low lunar orbits (LLOs) are unstable [44; 45]. In 2006 NASA [46] claimed to have only found four frozen orbits, at the following inclinations: 27\({}^{\circ}\), 50\({}^{\circ}\), 76\({}^{\circ}\) and 86\({}^{\circ}\). However, every year, different studies demonstrate the existence of new lunar frozen orbits [47]. The studies [48; 49; 50] discovered low elliptical lunar frozen orbits with a semi-major axis of about 1800 km.  
The determination of the lunar gravity field is based on tracking data from previous Moon missions. In [51], a model of the lunar gravity field using the Lunar Prospector mission from 1998-1999 can be found. Lunar mascons make most low lunar orbits unstable,and the main disturbance to frozen orbits is studied and modeled in [52]. Frozen orbits have been used in several lunar space missions, including NASA's Lunar Reconnaissance Orbiter (LRO) mission [53] launched in 2009, which studied the lunar surface with advanced scientific instruments and took high-resolution images of the surface. Additionally, frozen orbits have been used for positioning communication satellites, such as the Lunar Atmosphere and Dust Environment Explorer (LADEE), which studied the lunar atmosphere and dust, and the Lunar Reconnaissance Orbiter Communication System (LCRS), which provides communications support for the LRO mission and other future missions to the Moon. Furthermore, ref. [54] presented a new explicit transformation from mean to osculating elements that enables better behavior in terms of long-term stability about the mean values of eccentricity and periapsis, especially for high orbits.
## 4 Discussion  
### Plant Height  
Different studies have observed that blue light acts to limit the elongation of the petiole, stem, and hypocotyl in various horticultural species, such as chrysanthemum, lettuce, pepper, and soybean [25; 26; 27; 28; 29; 30; 31; 32]. Normally, increasing blue light decreases the stem length to a maximum threshold level [32]. [PERSON] et al. [30] observed that blue light for 12 h a day plays a role in changing the stem anatomy, inhibiting the growth, and determining the morphogenetic characteristics of pepper plants. Furthermore, [PERSON] and [PERSON] [33] found that B light during the photoperiod in low-light environments resulted in reduced plant heights. Cryptochromes are also known to influence the stem elongation, and various of plants exhibit suppressed shoot elongation in response to B light in a 12-h day [26]. However, these photomorphogenic responses are different for different species. Previous studies used B light during the photoperiod to control the morphogenesis, while the current study used B light as a supplement or for NI to control morphogenesis and flowering.  
All the photoperiodic light treatments considered in this study resulted in greater plant heights than that observed in the SD10 control (Figure 3A). This indicates that blue light may be used in the production of cut chrysanthemum flowers, as longer stems are considered to be of better quality. [PERSON] et al. [34] stated that the increased elongation growth of plants in response to B light is linked to lower phytochrome activity, and is a shade-avoidance response, where different species have different sensitivities. These results agree with those of [PERSON] et al. [20], where it was found that an extended photoperiod with blue light promoted stem elongation of chrysanthemum. Longer photoperiods are known to be associated with the presence of higher gibberellin levels, which enhance stem elongation in chrysanthemums [20; 35]. In many species, including saliva and marigold, B light was more effective than R light in increasing the shoot elongation [36]. [PERSON] and [PERSON] [37] reported that internode extension on the stem leader in apple was inhibited by B LED, which determined the lowest values among all the light qualities tested. The differing responses of different plants to B light indicates that a species' responses to a specific light quality cannot necessarily be predicted on the basis of responses of other species.
## 2 Analysis of the Reverse Torque and Vibration in the Mud Pump
### Working Principle and Structure of the Mud Pump Module
#### 2.2.1 Theoretical Calculations of the Reverse Torque for a Single-Stage Mud Pump
In this figure, the red dots represent the impeller and the black dots represent the guide shell. Similarly, the inlet-outlet pressure difference \(\Delta p\) at different speeds can be obtained, as shown in Table 2. Therefore, the relationship between the useful work per unit time and the single-stage mud \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Density \(\rho\) (kg/m\({}^{3}\)) & 1200 \\ Volume flow \(Q\) (L/min) & 2000 \\ Flow rates \(\varpi\) (m/s) & 1.88628 \\ Viscosity \(\mu\) (Pa-s) & 0.12681 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mud parameters. Figure 4: (**a**) The cutaway view of the three-dimensional model of the single-stage pump flow channel, (**b**) meshing of the three-dimensional model of the single-stage pump flow channel, and (**c**) pressure distribution diagram of the single-stage mud pump flow path at 1500 rpm. pump rotational speed, n, can be determined, as shown in Columns 1 and 2 in Table 3. Based on Equation (5) [39] of the relationship between the torque exerted by mud on the single-stage mud pump and the useful work per unit time, and according to the data of the useful work and rotational speed shown in Columns 1 and 2 in Table 3, the relationship between the calculated torque T and the rotational speed n can be obtained, as shown in Columns 1 and 3 in Table 3.  
\[W_{e}=Tn\eta \tag{5}\]
# Metabolic Rate Measuring with Indirect Calorimetry for Thermal Comfort Evaluation
## 3 Results and Discussion
### Predictive Equations for MET Based on Walking Speed
In addition to linear correlation, polynomial, exponential, and logarithmic fitting are commonly used. The logarithmic fitting results are poor, and the accuracy change caused by increasing the power in polynomial fitting is not significant. In order to be able to simplify the formula, the quadratic polynomial fitting is selected in this study. Figure 10 shows the linear relationships between walking speed and MET in this investigation. The exponential relationship is fitted in Figure 10a, and the quadratic polynomial relationship is fitted in Figure 10b. The R\({}^{2}\) of them is lower than the linear relationship, and the forms are more complex. This shows that the linear relationship can better represent the relevance between MET and walking speed.
# Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding
## 3 Methodology
### Relation Fusion Module  
In this section, we present a network layer that leverages an attention mechanism for refining relation representations. This improvement is achieved through the aggregation of vector representations from other relations. Prior research has predominantly focused on entity embedding learning, often considering relation embedding learning as relatively straightforward. Common operations like weight matrices have been employed to update relation embeddings. Therefore, our study delves into methods to enrich the expressiveness of relation embeddings and suggests that relations should incorporate pertinent information from other relations to enhance their semantics. The schematic diagram is shown in Figure 4.  
Ref. [36] conducted an analysis of the capabilities of various existing models in handling the aforementioned relations. They pointed out that these methods were unable to simultaneously model the upper two relations' patterns. To address this limitation, our study explores the potential of simultaneously modeling two relations by merging them together. Drawing inspiration from the graph attention network, which has demonstrated expressive capabilities [37; 38; 39], we employ a fusion operation in this paper.  
If the relations \(r_{1}\) and \(r_{2}\) satisfy \(r_{2}(e_{1},e_{2})\Rightarrow r_{1}(e_{2},e_{1})\), then these relationships are considered a pair of inverse relations, where \(e_{1}\) and \(e_{2}\) represent two entities. If the relations \(r_{1}\), \(r_{2}\), and \(r_{3}\) satisfy \(r_{2}(e_{1},e_{2})\wedge r_{3}(e_{2},e_{3})\Rightarrow r_{1}(e_{1},e_{3})\), then these relationships are a composed relation.  
In order to obtain the attention coefficient between two relations as \(j\) and \(i\), we quote Formula (5):  
\[e_{ij}=LeakyReLU[\phi(r_{i},r_{j})] \tag{8}\]  
Figure 4: Relation fusion.  
After establishing the semantic meaning shared between relations, the aggregation of related relation embeddings is performed based on the attention coefficient, as follows:  
\[r_{i}=\sigma(\sum_{r_{j}\in R_{i}}e_{ij}r_{j}) \tag{9}\]  
The aggregated embeddings in this step are used to update the previous relational embeddings. \(\sigma(\cdot)\) represents _sigmoid_ and \(i\) represents the sequence number of a relationship selected for fusion, which contains the two special relation patterns mentioned above.
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 2 Materials and Methods
### Experimental Data
#### 2.5.1 Data Normalisation  
The set of SCADA variables present a very different dynamic range between them. Therefore, a standardisation step is required. For this purpose, a _z-score_ normalisation is applied to the data, according to which the mean \(m_{i}\) from each variable \(s_{i}\) is subtracted from that variable and the result is divided by that variable's standard deviation \(\sigma_{i}\) so that: \(x_{i}=(s_{i}-m_{i})/\sigma_{i}\). The set of means and standard deviations are stored in the vectors \(\mathbf{m}\) and \(\mathbf{s}\) respectively since they will be necessary to perform normalisation and denormalisations in the test step.
# Detecting Grounding Grid Orientation: Transient Electromagnetic Approach
## 4 Performance Evaluation and Results' Analysis
### Grounding Grid with a Diagonal Branch  
Grounding grids exist in different configurations depending on the substation layout. A diagonal branch often exits in grounding grids. To check the feasibility of the proposed method for the orientation detection of the grounding grid with a diagonal branch, conductor \(C_{13}\) was added to Figure 5. The model with a diagonal branch is shown in Figure 8. \(C_{13}\) connected Nodes 1 and 5. TEM was applied by moving the transmitter-receiver pair in a circle from \(P_{1}\) to \(P_{8}\), and the result of equivalent resistivity \(\rho_{r}\) is shown in Figure 9. This time, \(\rho_{r}\) was high at \(P_{6}\) as eddy currents \(I_{1}\) and \(I_{2}\) opposed each other in \(C_{13}\), validating the presence of diagonal conductor \(C_{13}\). Contrarily, \(\rho_{r}\) at \(P_{5}\) and \(P_{7}\) decreased although conductors \(C_{5}\) and \(C_{7}\) existed beneath them. This was due to unequal magnetic coupling as the mesh size had changed due to the presence of diagonal conductor \(C_{13}\). Moreover, the average magnetic field \(\overline{H_{z}}\) graph related to Figure 8 is demonstrated in Figure 10. Diagonal conductor \(C_{13}\) was represented by low \(\overline{H_{z}}\) at \(P_{6}\).  
Figure 8: Grounding grid with diagonal conductor \(C_{13}\). \(C_{13}\) connects Nodes 1 and 5 while carrying \(I_{1}-I_{2}\).  
Figure 7: Average magnetic field intensity \(\overline{H_{z}}\) along the circle from \(P_{1}\) to \(P_{8}\). \(\overline{H_{z}}\) is low at \(P_{1}\), \(P_{3}\), \(P_{5}\), and \(P_{7}\), confirming the presence of \(C_{4}\), \(C_{10}\), \(C_{3}\) and \(C_{9}\), and the parallel orientation of the grid in the plane of the Earth.
# High-Efficiency Quantum Dot Lasers as Comb Sources for DWDM Applications
## 1 Introduction
### Semiconductor MLLs  
Semiconductor MLLs output a high frequency pulse train with fixed frequency-spacing between the longitudinal cavity modes in a compact, efficient, reliable, and robust device, making them a natural candidate for optical sampling [21], clock distribution [22], and as DWDM sources [23]. Quantum well MLLs have been investigated for more than three decades; however, the introduction of InAs quantum dot (QD) active region has improved their performance. QDs have been shown to have higher gain [24], a lower threshold [25], a smaller linewidth enhancement factor [26], they benefit from inhomogeneous broadening [27], improved temperature-induced threshold increase (T\({}_{0}\)) [28], and improved carrier dynamics [29]. Implementing InAs QDs has led to the development of efficient high-output-power MLLs, which are suitable for comb sources [8]. They output dozens of comb lines, resulting in multi-terabit single device data transmission capacities [23]. Additionally, these devices do not require initiation routines or feedback loops, and they only use voltage and current sources.
# Study of the Diffusion of Rejuvenators and Its Effect on Aged Bitumen Binder
## 3 Results and Discussion
### Molecular Calculation Results
#### 3.1.2 Diffusion Coefficient  
MSD values are then used to calculate the diffusion coefficient (D) via [PERSON]'s formula as follows:  
\[MSD=\lim_{t\rightarrow\infty}\frac{d}{dt}\sum_{i=1}^{Na}\left\langle\left[r_{i} (t)-r_{i}(0)\right]^{2}\right\rangle \tag{1}\]  
where \(r_{i}(t)\) is the displacement of particle \(i\) at time \(t\), and \(r_{i}(0)\) is the displacement at the starting time. In this formula, \(N\) represents the diffused particles of the system. When the MSD curve presents a linear trend, the equation can be simplified as follows:  
\[D=\frac{1}{6T}MSD \tag{2}\]  
where \(T\) is the total time of atoms or molecule movements. \(D\) is the \(1/6\) slope of the simplified linear curve.  
In the R-1-treated bitumen, the calculated D of the rejuvenator in RTFOT, PAV, and UV aged bitumen is 2.26 \(\times\) 10\({}^{-10}\), 2.82 \(\times\) 10\({}^{-10}\), and 3.09 \(\times\) 10\({}^{-10}\), respectively. The calculated D of Rejuvenator R-2 in RTFOT, PAV, and UV aged bitumen is 1.18 \(\times\) 10\({}^{-10}\), 1.35 \(\times\) 10\({}^{-10}\), and 3.05 \(\times\) 10\({}^{-10}\). Firstly, this illustrates that the severity level of aging has a significant influence on the diffusion of rejuvenators. Investigated rejuvenators diffuse easier and faster in the long-term aged binder, such as the UV aged  
Figure 2: Mean squared displacement (MSD) curves of rejuvenators in aged bitumen at 150 \({}^{\circ}\)C.  
binder, but diffuse slower in the short-term aged bitumen binder. Secondly, R-1 presents a higher diffusion coefficient in aged bitumen than does R-2, which indicates that R-1 has a better diffusion coefficient in the aged bitumen.
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
###### Abstract  
As an emerging network architecture, Information-Centric Networking (ICN) is considered to have the potential to meet the new requirements of the Fifth Generation (5G) networks. ICN uses a name decoupled from location to identify content, supports the in-network caching technology, and adopts a receiver-driven model for data transmission. Existing ICN congestion control mechanisms usually first select a nearby replica by opportunistic cache-hits and then insist on adjusting the transmission rate regardless of the congestion state, which cannot fully utilize the characteristics of ICN to improve the performance of data transmission. To solve this problem, this paper proposes a two-level congestion control mechanism, called 2 LCCM. It switches the replica location based on a node state table to avoid congestion paths when heavy congestion happens. This 2 LCCM mechanism also uses a receiver-driven congestion control algorithm to adjust the request sending rate, in order to avoid link congestion under light congestion. In this paper, the design and implementation of the proposed mechanism are described in detail, and the experimental results show that 2 LCCM can effectively reduce the transmission delay when heavy congestion occurs, and the bandwidth-delay product-based congestion control algorithm has better transmission performance compared with a loss-based algorithm.  
5G; Information-Centric Networking; congestion control; receiver-driven 2021 27 April 2021 4 June 2021 7 June 2021 7 June 2021 7
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 4 Methods
### Improved Matrix Divide-and-Conquer-Based FFT Algorithm (MV-2 FFT)
#### 5.1.2 Software Flow of the Edge Filter  
The combination of high-pass filtering and the Fourier Transform is a common usage. Specifically, the software edge filter uses the high-pass filter to extract contour images. The extraction results can be visualized through the reverse Fourier Transform, which facilitates validating the correctness of algorithms. The software execution stream of the edge filter is shown in Figure 3.  
We prepared six types of PGM format images with specifications of 64 \(\times\) 64, 128 \(\times\) 128, 256 \(\times\) 256, 512 \(\times\) 512, 1024 \(\times\) 1024, and 2048 \(\times\) 2048 as the input for the Fourier Transform algorithms.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
## 2 Related Works  
This section of the article examines previous work from the standpoint of network architecture, as well as the multiple performance measures that support the concept of extending network life.  
[PERSON] et al. investigated a universal wireless sensor network (UWSN) using a grid topology. This study investigates energy usage across a variety of energy modalities as well as network performance.  
[PERSON] et al. Investigate optimizing packet size in order to maximize the longevity of UWSNs. This is done to determine how to obtain the most out of UWSNs. To maximize the longevity of a network, it is critical to consider not only the length of time it takes for a packet to transmit but also its total size. Furthermore, we mimic the connection layer in a realistic manner to reduce the amount of power consumed by the network.  
Here, an optimal, collaborative, and resource-saving strategy is being examined [29]. In order to create a UWSN that consumes less energy, a relay node is chosen. The indices of dead and end-to-end delays, dead packet delivery ratio, and energy usage are investigated. The nodes are organized in a two-dimensional environment with a regular distribution. In the case of UWSN, both interference and noise are considered. [PERSON] et al. examine interference-free localization routing for ultra-wideband sensor networks (UWSNs), with the goal of minimizing the energy hole [30]. It specifies the total number of dropped packets, the total number of dead nodes, a packet received at the sink, the total amount of energy used, and the total number of dropped packets.  
A MAC protocol study was carried out at the UWSN ([PERSON] and [PERSON] 2007). It was also determined that the protocol's scalability, energy efficiency, distributed nature, and capacity to function regardless of propagation latency or UWSN medium length. The simulation contains no propagation delay as well as a significant amount of propagation delay. In the event of a collision, the transmitted energy waste is analyzed in relation to the number of nodes and the duty cycle.  
UWSN performance parameters include packet delivery ratio, network lifetime, energy utilization, and transmission latency. In terms of mobility, [PERSON] et al. investigate a grid-based routing technique for UWSN [31]. Energy usage, network density, packet delivery ratio, and latency are all discussed. EAVARP, a void-avoidant and energy-conscious routing system, is being investigated by [PERSON] et al. for wireless sensor networks [32]. UWSN is impervious to transmission, vacancy, and flooding cycles.
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 3 SOH Prediction Based on Transformer-LSTM Fusion Model
### Optimizer and Loss Function  
This article utilizes the Adam optimizer for hyperparameter tuning of the transformer-LSTM model to enhance predictive performance through minimizing the mean square error (_MSE_) loss function. The Adam optimizer adaptively adjusts the learning rate, combining the advantages of the momentum method and the RMSprop algorithm to render the update of each parameter more accurate and efficient. It can efficiently process high-dimensional data and accelerate convergence during training, especially for complex deep-learning models. The _MSE_ loss function evaluates model performance by minimizing the difference between predictions and ground truth, thereby helping the model optimize and enhance prediction accuracy. The calculation formula for the MSE loss function is as follows:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2} \tag{7}\]  
where \(N\) is the total number of samples, \(y_{i}\) is the true value of the \(i\)-th sample, and \(\hat{y}_{i}\) is the predicted value of the \(i\)-th sample.
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
## 2 Background and Related Works
### Related Works  
[PERSON] et al. [12] introduced a lightweight secure aggregation and transmission scheme (SATS) for secure and lightweight data computation and transmission. SATS utilizes lightweight \(XOR\) operations to obtain batch keys instead of expensive multiplication oper  
Figure 1: The encryption and decryption of the pixel in visual cryptography.  
ations. Traditional resizing methods, such as linear and cubic interpolation, are common in image preprocessing. [PERSON] et al. [13] explored a learnable resizer to enhance network performance. However, this emphasis on recognition neglects the visual quality, resulting in significantly distorted resized images. The Voronoi-based VC [14] leverages Voronoi tessellation to minimize the data encoded and transmitted, offering advantages over traditional VC schemes. This technique is especially beneficial for secret image transmission in bandwidth-limited and memory-restricted networks because it increases information redundancy and enhances transmission reliability.  
VC simplifies encryption by eliminating complex computations and key dependencies, making it a suitable alternative for low-computing power environments. [PERSON] et al. [15] apply the adaptive brightness adjustment to reduce all-white pixel frequency in unencryptable all-white blocks, which addresses secret-patch correspondence and mitigates size expansion and contrast loss in the restored image. However, the increase in shares increases the transmission bandwidth. By selecting the number of colors in the reconstructed image, [PERSON] et al. [16] eliminated the halftoning preprocessing by probabilistic techniques and color channel separation, failing to perfectly recover white secret pixels and potentially sacrificing contrast, with a contrast ceiling of \(1/4\). [PERSON] et al. [17] proposed a lightweight privacy-preserving system for remote sensing images with VC, combined with denoising neural networks to improve decryption quality and recognition accuracy.