esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 1 Introduction
Section 2 describes the proposed adaptive noise cancellation algorithm based on MCC. Section 3 details the FPGA implementation of the proposed algorithm. Section 4 evaluates the performance of the proposed system using simulation and experiments. We present conclusions in Section 5. Notation: Lower-case (resp. Upper-case) bold letters denote column vectors, with \(\mathbf{W}(k)\) being the \(k\)th results obtained in the iteration process; \(E\left[\cdot\right]\), \(\left(\cdot\right)^{T}\), \(\exp\left(\cdot\right)\), \(k_{\sigma}\left(\cdot\right)\) and \(G_{\sigma}\left(\cdot\right)\) denote expectation, transpose, natural exponential function, shift-invariant Mercer kernel function with kernel size \(\sigma\) and Gaussian kernel function with kernel size \(\sigma\), respectively; \(j\) denotes the imaginary unit; \(\
abla J\left(k\right)\) denotes the gradient of \(J\left(k\right)\), while \(J\left(k\right)\) denotes the cost function at \(k\) moment. Figure 1: Adaptive noise cancellation with correlated noise at the primary and reference input.
## 3 Results
### Climate Change in the SAM
#### 3.2.2 Shrinking SFG  
The currently available observations from frozen ground sites are located mainly in the SFG regions, and there are no permafrost observation sites in the SAM. Therefore, only the SFG change was investigated based on the CMA-derived dataset. Across the SAM, the SFG has followed a declining trend due to the continuous temperature rise. The mean SFG duration was 151 days, with a declining trend observed during the period 1961-2015 (Figure 3b). There was a 33-day decrease from 169 to 136 days during the study period that we attribute mainly to the ground thawing earlier. The initial ground freezing occurred slightly later, by about 5 days, whereas ground thawing occurred about 28 days sooner. This result was different from that found on Qinghai-Tibet Plateau during the period 1967-1997, where the lag in the initial ground freezing induced mainly a reduction in the freezing duration [44]. Obvious declines in the maximum freezing depth were also observed. However, the maximum freezing depth was stable before the 1980s, declined significantly from the late 1980s to the 1990s, and then increased in the 2000s and 2010s (Figure 3b). The mean maximum SFG freezing depth was 119 cm, varying from 82 cm in 1998, to 161 cm in 1984, and the mean shrinking rate was 5.8 cm/10 yr.  
The warming trends in the monthly soil temperature are shown in Figure 4. There was an increase in soil temperature in all months, but the increase was most dramatic in March and April. The universal abrupt rise of soil temperature mainly occurred in the late 1990s except for in winter, whereas the maximum freezing depth began to increase. Another study indicated that snow cover was the dominant factor governing the SFG regime, whereas air temperature and snow cover were the primary and secondary factors controlling SFG degradation during the 1961-2015 study period on the edge of the Altai Mountains [25].  
Figure 4: Variations in the monthly soil temperature anomaly (\({}^{\circ}\)C) during the period 1961-2015 observed by CMA in the SAM.
## 5 Experimental
To illustrate the advantages of the proposed LC/S compensated IPT system, a summary is given below:  
1. The proposed LC/S compensated IPT system does not require redundant AC switches and complex control algorithms, and shifts between two fixed operating frequencies to realize CCO and CVO with ZPA operation, which not only makes the system simpler in structure, but also makes control of the system more convenient. In addition, the total number of compensation elements of the system is minimized, with only one compensation capacitor on the receiving side, ensuring a compact and low-cost receiver. 2. The proposed LC/S compensated IPT system adopts a coil decoupling design, which eliminates the coupling mutual inductance between the compensation inductor \(L_{1}\) and the LCT, maximizing space utilization.
## 4 Results and Discussion
### Effect of Impact Velocity  
The contact velocity has a significant impact on the S33 stress exerted on the LEP surface, and this pressure seems to increase linearly while increasing the velocity. The greater the velocity, the higher the S33 stress. It can reach up to 130 MPa for an impact velocity of 160 m/s, as shown in Figure 7. This simulation validates the important impact of operational methods, such as the safe-erosion method, to reduce blade surface erosion. Indeed, significantly reducing the speed of the wind turbine during heavy rain events will reduce the stress exerted and internal wave propagation through the blades' thickness.  
Figure 6: (**a**) von Mises and (**b**) S33 Stress for different friction coefficients (from frictionless up to \(\mu=0.5\)).  
Figure 7: S33 Stress for different impact velocities (from 80 m/s to 160 m/s).  
The results presented in Figure 8 help validate the model for impact speeds between 80 m/s and 160 m/s. Indeed, the S33 peak pressure approximately matches the theoretical values obtained with the modified water hammer pressure, especially for impact speeds from 100 m/s to 160 m/s, where the difference is less than 5%. The greatest difference, around 10%, is observed at an impact velocity of 80 m/s. This difference may result from the application of a smoothing function, which reduces noise and fluctuations in the data, leading to a clearer analysis. FEM and theoretical calculations are complementary, with theory providing fundamental insights and validation, while FEM refines the analysis for complex geometries, enables local stress-strain evaluation, and facilitates model optimisation through iterative testing, ensuring a complete and accurate solution.
## 3 Results and Discussion
### Temperature Measurements
At the end of the test, the bearing with the 25.9 mm inner diameter sleeve reached a temperature of 25.6 \({}^{\circ}\)C and the bearing with a larger diameter reached a temperature of 26.3 \({}^{\circ}\)C. The temperature difference was only 0.7 \({}^{\circ}\)C. A slight increase in temperature in both bearings over the last few test cycles indicated that the bearing node was approaching thermal equilibrium. The use of a cooling system had a positive effect on stabilizing the temperature rise. This was also the case for the second material variant of the foil bearing. This can be seen in the graphs obtained for the bearings whose foils were made of the CuAI7/Inconel material combination. When the cooling system was not activated, the temperature rose almost linearly to 37.7 \({}^{\circ}\)C for the bearing with a larger sleeve and 33.2 \({}^{\circ}\)C for the other bearing (see Figure 11). Activating the cooling system resulted in the reduction of the bearing temperature to approximately the same level (see Figure 12). With the cooling system, the temperature difference obtained after 200 start/stop cycles was only 1.1 \({}^{\circ}\)C, while without the cooling system, it was 4.5 \({}^{\circ}\)C (after 200 start/stop cycles). The beneficial effect of external cooling on the operation of the foil bearing was therefore confirmed. An increased number of start/stop cycles would increase this temperature difference due to the additional phase change of the low-boiling medium. Figure 12: Temperature rise curve for CuAI7/Inconel material pair (top/bump foils) with a cooling system for two bearing housing diameters. The measurement results obtained for the second pair of materials also confirmed the necessity for additional research. A greater temperature increase in the bearing with a larger diameter sleeve was unexpected, as theoretically, there should have been a lower assembly preload in this bearing which should have resulted in less heat generation during operation. Therefore, the tested material and geometric variants were further investigated, as discussed in the following part of the article.
# Anti-Jamming Power Control Algorithm for Wireless Communication Systems Based on MPC
## 6 Conclusions  
To address the challenge of ensuring reliable communication transmission in the presence of malicious jamming, this paper proposes an anti-jamming power control algorithm for wireless communication systems based on model predictive control. Simulation results show that the algorithm effectively balances input energy consumption and system transmission reliability when faced with unknown dynamic jamming. Through rolling optimization, the system dynamically adjusts and quickly restores the bit error rate to the desired level. In comparison to alternative techniques such as integral state feedback control, the MPC state controller has been observed to enhance the BER convergence speed of the communication system by approximately 8.5% and to diminish the fluctuation range by approximately 3.2%. This has the effect of improving the reliability of wireless communication and demonstrates that the MPC state controller exhibits a more pronounced anti-jamming capability.  
The MPC power control algorithm, as employed in this study, comprises a multitude of intricate parameters, exhibiting intricate interdependencies. The tuning process itself is a complex undertaking. In future research, an in-depth analysis and verification of parameter tuning for the MPC power controller, based on diverse interference scenarios, can be undertaken to further substantiate its efficacy.
## 2 Materials and Methods
### The Construction of MSWDD2024
#### 2.1.1 The Acquisition of Data  
As shown in Figure 1, the multispectral remote sensing imagery used in this study was captured from wheat experimental fields at the North Campus of Northwest A&F University and the Cao Xinzhuang Wheat Experimental Base in Yangling, Xianyang, Shaanxi Province, China. Data acquisition was conducted on 17 April and 23 April 2024. To ensure high image quality and minimize the impact of illumination variation, data collection was performed at midday (11:30-13:30) under stable lighting conditions and low wind speeds. The imaging system utilized a DJI Phantom 4 Pro multispectral UAV, which is equipped with an RGB camera and five multispectral sensors covering five spectral bands: the blue, red, near-infrared (NIR), red-edge, and green bands. During data acquisition, the UAV was operated at a flight altitude of 45 feet, with a fixed nadir camera angle of \(-90^{\circ}\), yielding a ground sampling distance (GSD) of 0.896 cm/pixel. To maintain spatial consistency and ensure sufficient image overlap, both longitudinal and lateral overlap rates were set to 80%, and the UAV followed a predefined flight path for autonomous image capture. Additionally, under expert guidance, field surveys were conducted to document the spatial distribution of healthy and diseased wheat regions, providing precise reference information for subsequent data annotation.
## 1 Introduction
Porous solids such as metal-organic frameworks (MOFs) and porous-organic polymers (POPs) have been used in various applications and particularly for gas separation and storage [35; 36], primarily because of their high surface area [37; 38]. In general, porous solids can be synthesized from molecular building blocks to produce well-designed frameworks [39]. The CO\({}_{2}\) uptake can be scaled up by increasing the surface area of the porous MOFs [40]. In addition, the inclusion of polar moieties on the surface of the POPs could enhance the CO\({}_{2}\) storage capacity [41; 42]. The interaction between MOFs and CO\({}_{2}\) is strong due to the hydrogen bonding and dipole-quadrupole interaction between the functional groups of POPs and CO\({}_{2}\)[43]. For example, POPs containing nitrogen-functionalized pores can efficiently capture CO\({}_{2}\)[44]. POPs are potential sorbents for CO\({}_{2}\) because they have high stability (chemical and thermal), large surface area, low density, tunable structures, and pore size and can accommodate different functionality [30]. The CO\({}_{2}\) capture can be enhanced efficiently through the introduction of various heteroatoms to the POPs skeleton via the improvement of the materials chemisorption and physisorption [30]. Inorganic ions and organic functional groups can be used to change the surface polarity of POPs, and therefore, increase the interaction between CO\({}_{2}\) and the adsorbents. Some progress has been made in the capture of CO\({}_{2}\) using POPs; however, further developments are still needed [30]. The most common remaining issue associated with the use of POPs as sorbents for CO\({}_{2}\) is the development of an efficient synthetic procedure that does not involve the use of metal catalysts, production of high surface area POPs that have 3D structures, the use of efficient and moderate reaction conditions within the post-synthetic procedures, and the use of POPs that have multiple adsorption sites to the capacity of CO\({}_{2}\) adsorption at very low pressures [30]. Polyphosphates are highly stable and have excellent mechanical and physical properties [45,46]. They have been used as catalysts, fire retardants, reagents for surface adhesion, and tooth preservers [47, 48, 49]. Recently, different polyphosphates [50] and organotin complexes [51] have been reported as efficient media for CO\({}_{2}\) storage. Polyphosphates containing benzidine are highly porous, have a high surface area, tunable pore structures, and showed excellent efficiency in the capture of CO\({}_{2}\). Therefore, the aim of the current work was to synthesis novel POPs containing phosphate units using a simple and general procedure to be used as potential media for CO\({}_{2}\) storage. The polyphosphate-based POPs could be synthesized easily and could reduce off the damage caused to the environment due to the increased CO\({}_{2}\) emission.
## 2 Materials and Methods  
### Analysis of Photovoltaic Output Characteristics  
#### 2.1.1 Influencing Factors of Photovoltaic Output  
The factors affecting photovoltaic output mainly include two aspects: one is internal factors, including the power generation performance and conversion efficiency of photovoltaic arrays, the installation angle of PV panels and its operation mode, the type of system components and geographical location, etc.; the other is external factors, which mainly refer to the climatic conditions of the location of the photovoltaic power generation grid [19; 20]. Considering that the geographical locations of the users selected for the identification of electricity stealing in this paper are adjacent to each other, it is assumed that their power generation efficiency is similar, and only the influence of meteorological factors is considered. Meteorological factors that affect photovoltaic output mainly include weather type, actual irradiance, temperature, humidity, wind speed, pressure, etc. [21]. However, in addition to weather type and irradiance, which have obvious effects on PV output, the effect of other factors on PV output are often indirect and unclear. In order to improve the prediction effect of the model, further feature mining is carried out on the meteorological features, and some new features are constructed.
## 1 Introduction
For instance, Particle Swarm Optimization dynamically adjusts routes within clusters, improving communication efficiency and reducing CH load [10]. Ant Colony Optimization optimizes path selection by finding the shortest, most energy-efficient routes [11]. Genetic Algorithms [12] and Bee Colony Optimization [13] also improve resource allocation and management, thereby reducing energy use and increasing network life and efficiency [14]. This study addresses a research gap in WSN optimization, inspired by [PERSON] et al. [15] on energy optimization. Despite progress, there is potential for improvement in using swarm algorithms for transmission power optimization. While many strategies exist to save energy and extend node life in WSNs [16], few focus on swarm algorithms. The main contribution of this study is the development of an Enhanced Grey Wolf Optimization (EGWO) using a non-linear decreasing exploitation parameter that allows more precise exploration in an area. The proposed EGWO is further evaluated in six different topologies with three other swarm optimization methods, namely standard GWO, Bi-directional Evolutionary Structural Optimization (BESO), and Particle Swarm Optimization (PSO). The performance of swarm algorithms is assessed using the Friis transmission power model, a standard for determining distance and signal strength in wireless communications. This model evaluates how each algorithm optimizes transmission power in WSNs to maximize energy efficiency while maintaining high communication quality. This paper has four main sections: Section 1: Introduction, outlining the WSN background and challenges; Section 2: Methodology, describing the technical framework; Section 3: Results, presenting experimental findings; and Section 4: Conclusions, evaluating the results and future research directions.
## 4 Model Establishment and Instability Evaluation Method
### Impact Instability Energy Evaluation Method  
Rock failure is a phenomenon of instability driven by energy. Evaluating the safety and stability of engineering rock masses can be effectively achieved by describing the strength and deformation behavior of rock from an energy perspective [29; 30]. Consequently, elastic strain energy serves as a measure for evaluating the safety and stability of rock masses in response to vibrational wave activity.  
The elastic deformation energy of the surrounding rock in a roadway is a scalar quantity that characterizes the energy of the surrounding rock within a specific range. It is a function of the density of elastic deformation energy. The elastic strain energy released by the surrounding rock of the roadway can be calculated using the following formula [26]:  
\[E_{0}=\frac{1}{2\overline{E}}\Big{[}\sigma_{1}^{2}+\sigma_{2}^{2}+\sigma_{3}^{ 2}-2\overline{v}(\sigma_{1}\sigma_{2}+\sigma_{2}\sigma_{3}+\sigma_{1}\sigma_{ 3})\Big{]} \tag{18}\]In the formula, \(\overline{E}\) represents the unloading elastic modulus, while \(\overline{v}\) denotes the average Poisson's ratio.  
According to the theory of pressure bumping low pressure starting proposed by [PERSON] et al. [31], pressure bumping occurs when the elastic strain accumulated in the limit equilibrium zone exceeds the minimum energy required to fracture the coal and rock in that area, as indicated in Formula (19).  
\[E_{0}-E_{c}>0 \tag{19}\]  
In the formula, \(E_{0}\) is the released elastic energy, and \(E_{c}\) is the minimum energy required for the destruction of coal.  
The minimum energy principle related to the dynamic failure of coal and rock masses, as described in reference [32], asserts that the energy required for the dynamic failure of a rock mass--irrespective of whether it is subjected to a one-dimensional, two-dimensional, or three-dimensional stress state--remains consistently equal to the energy utilized during failure under a one-dimensional stress state.  
Therefore, the minimum energy required for the destruction of the 4-5 coal seam can be calculated. The corresponding energy consumption criterion is as follows:  
\[E_{c}=\sigma^{2}/(2E) \tag{20}\]  
In the formula, \(\sigma\) is the body's uniaxial compressive strength, and \(E\) is the elastic modulus of coal.  
The uniaxial compressive strength of the 4-5 coal seam is 13.30 MPa, and its elastic modulus is 1.96 GPa. The minimum energy required for the failure of 4-5 coal is 45 kJ/m\({}^{3}\).  
When the accumulated energy exceeds the maximum load-bearing capacity of the rock mass, it will lead to impact failure.  
In order to describe the energy accumulated during the propagation of vibrational waves, the formula for calculating rock elastic strain energy is incorporated into the static and dynamic models of FLAC\({}^{3\text{D}}\) using Fish language.  
By analyzing the variations in elastic strain energy levels near the roadway before and after excavation, an assessment is conducted to evaluate whether the roadway is at risk of experiencing impact instability.
The principle of DHC is based on the creation of interference patterns as light is transmitted through the cells. A laser beam is divided into a reference and a sample beam, and both beams are later merged again to create the raw image. The changes in interference pattern are assessed after capture on a CCD camera. Digital autofocus and reconstruction of the 3D-image of the sample is provided by computer algorithms [32; 33]. In this study, we aim to evaluate the impact of sialic acid-molecularly imprinted polymers (SA-MIPs) on the motility and viability of two breast cancer cell lines MCF-7 and MDAMB231. Flow cytometry was used to evaluate the level of SA using MIPs and the lectins MALI (from Maackia amurensis) and SNA (from Sambucus nigra). The lectins MALI bind specifically to \(\alpha\)-2,3 SA and SNA bind specifically to \(\alpha\)-2,6 SA. DHC was used to quantify cell morphology and motility to reveal the metastatic potential of the cell lines. Viability assays were performed on the MCF-7 and MDAMB231 cells lines treated or not treated with SA-MIPs. Here, we show that MDAMB231 cells had higher motility compared to MCF-7 cells and that SA-MIPs treatment increased the motility and slightly decreased the viability for MCF-7 cells, while for MDAMB231 cells the motility decreased and the viability increased.
# EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks
## 3 Proposed Protocol
### Proposed Solution  
To detect and prevent blackhole nodes, we propose an Enhanced Blackhole Resistance (EBR) algorithm in mobile networks. Each node examines its neighbors and determines their confidence degree to identify the blackhole nodes. EBR protocol provides a solution for both single and collaborative attacks. EBR employs a combination of time to live (TTL) and round trip time (RTT) to determine the existence of the blackhole node(s) in every available path. This combination is called the TTL/RTT (TR) method. In the EBR algorithm, each node can send a RREQ test message to a dummy destination node. If the receiving node(s) reply with an RREP message, this will be considered as a blackhole node(s) and the confidence level will be low, and, therefore, the route will not be selected. TTL should have a small value to only examine the neighboring nodes and, also, to avoid overwhelming the network with overload traffic. RTT in case of a blackhole attacker is a short time because this attacking node wouldn't consume processing time, as the fake route is always available to receive the data packets and hence drop them. If the RTT is long, this indicates that the route nodes are possibly congested and should be avoided even if the path is safe.