esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The transmission of data in the presence of water, particularly saline water, presents a widely recognized challenge that significantly impacts the adoption of wirelessly connected devices in a variety of applications [1]. The effects of saline water not only restrict the deployment of underwater devices but also impose stringent limitations on the positioning of data transmission tools in areas with high water salinity [2, 3]. This scenario is particularly relevant for floating structures where successful transmissions necessitate antennas to be elevated at specific heights above saline water to overcome its attenuating effects and ensure reliable communication [4]. However, the challenges become even more pronounced when data transmission needs to occur underwater in environments with high salinity. The combination of water and high salinity levels introduces additional complexities and attenuating effects, significantly impacting the range and quality of wireless communication [5]. The presence of high-salinity water further limits the effective deployment of communication systems and necessitates the development of specialized techniques and equipment to mitigate the detrimental effects and ensure reliable data transmission in these challenging underwater environments [6]. Underwater communication can be enhanced by utilizing optical signals instead of sound-based communication methods [7; 8]. Underwater optical wireless communication (UWOC) in water offers several advantages over acoustic communication, including higher data rates, increased bandwidth, and reduced signal attenuation [9].  
Figure 1 shows the general scenario of the UWOC link. It shows that the UWOC link serves as a crucial means of connecting divers, submarines, and autonomous underwater vehicles in underwater environments. This advanced communication technology uses modulated light signals to establish real-time, high-speed data exchange between divers and submerged submarines. Consequently, the high transmission rate characteristic of UWOC systems has garnered considerable attention in recent years. The rapid advancements in UWOC systems have resulted in increasing demands for long-range and high-speed underwater links. Achieving these requirements poses a challenge for system designers. Establishing such links is crucial for various applications, including oceanography, environmental monitoring, and underwater surveillance. Furthermore, UWOC has emerged as an attractive alternative to traditional RF and acoustic systems for underwater communication [10; 11]. With its advantages of high data rates, low latency, short-range wireless links, and low power consumption, UOWC provides a compelling solution to meet communication needs in underwater environments. UWOC technology finds utility in challenging and remote locations, including deep-sea environments, where it enables communication for remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) [12; 13]. This is especially important as these vehicles often face limitations in range and maneuverability.
# Rapid Parametric Modeling and Robust Analysis for the Hypersonic Ascent Based on Gap Metrics
###### Abstract  
This paper investigates a rapid modeling method and robust analysis of hypersonic vehicles using multidisciplinary integrated techniques. First, the geometrical configuration is described using parametric methods based on the class-shape technique. Aerodynamic forces and moments are estimated for the specific configuration using engineering methods. Moreover, the nonlinear model is simplified by the polynomial fitting expressions, and the linear variable parameter model is obtained for the tracking control design and dynamic characteristic analysis with the aid of the sensitivity analysis and gap metric methods. A velocity-driven trajectory design method is deduced for hypersonic ascent, and the tracking control law is developed to realize the flight process from the initial point to the cruise point. Furthermore, a robust analysis process based on gap margin is proposed for climb trajectory tracking. Simulation results are provided to verify the feasibility of the proposed modeling method and show that the flight control of a hypersonic vehicle is more sensitive to altitude variation.  
hypersonic aerospace vehicle; modeling; track control; parametric design method +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Evaluation of Pretrained Alarm Vectors  
Obtaining word vectors for a domain-specific corpus requires fine-tuning of model parameters. The key parameters of the Skip-gram model are word vector dimension \(d\) and window size \(w\). To adjust the model parameters, the evaluation of the obtained alarm vectors is needed. Word vector training is an unsupervised process. When evaluating the obtained vectors, word embedding can be used as input features to a downstream task and measure changes in performance metrics specific to that task. As mentioned above, the S-ECNN-static model can be regarded as a downstream classification task of alarm vector embedding, so it is used to fine-tune the parameters of the Skip-gram model. The accuracy is used to evaluate the performance of classification, which is defined as:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}, \tag{14}\]  
where \(TP\) represents the number of positive instances, which are classified correctly; \(FP\) represents the number of negative instances, which are misclassified; \(TN\) represents the number of negative instances, which are classified correctly; \(FN\) represents the number of positive instances, which are misclassified [35].  
The accuracies of the S-ECNN-static model under different word vector dimensions and window sizes are shown in Figure 7. When the word vector dimension \(d\) is reduced from 100 to 75, 50, 25, and 10, the accuracy decreases. However, increasing the dimension \(w\) from 100 to 150 and 200, the accuracy does not increase significantly. When the word vector dimension \(d\) is 100 and the window size \(w\) is 4, the accuracy is the highest, and the pretrained alarm vectors are adopted in this paper.  
Figure 7: The accuracies of the S-ECNN-static model with different parameters.  
To visualize the alarm vectors, we reduced the model dimension to a three-dimensional space using t-distributed stochastic neighbor embedding (t-SNE) [36], which is known to be one of the very powerful tools for dimensionality reduction and data visualization. The visual display of alarm vectors is shown in Figure 8. Three types of pitch system alarms are highlighted as examples. As we can see, the pitch system alarms of the same type show a tendency for aggregation. The more complex relationship between alarms cannot be reflected in the three-dimensional visual image. Further algorithm analysis is required.
###### Abstract
By determining the CSI and VSI contribution to the harmonic filter of the load current, the size of the passive elements on the DC and AC sides, as well as the switching frequency, are determined. This design is based on the resulting equivalent circuit and simple KVL principles as well as the commonly accepted criteria. The results of the analysis show how and under what conditions the size of the passive elements in the proposed structure is reduced compared to the conventional ones. However, although the theoretical foundations of the size of the passive elements in the DC and AC sides of the converters in the integrated APF structure are explained in [14], it is necessary to calculate and evaluate the impact of this reduction on the overall filter losses by mathematical analysis. The concept of current compensation by APFs has been drawn intuitively for better understanding in Figure 1. It is assumed that a non-linear or unbalanced load is going to be fed through an AC power source. Since the AC source should not generate the pulsating current due to the safety and lifetime procedures, the sinusoidal current waveform is just allowed to be injected to the grid according to power quality standards. Therefore, the non-linearity components of the required current are supplied by the AFP [15; 16; 17; 18]. In order to decrease the output voltage ripples using only passive filters, the switching frequency is increased, which results in incremental switching losses a reduction in the system's efficiency. Other disadvantages of these filters are their strong dependence on the resonant frequency to the equivalent impedance of the grid. Reducing the switching loss and switching noise requires the soft-switching circuit and implementing the dynamic reactive power compensation capability, which has been presented in [19]. Therefore, a quasi-DC link voltage-controlled loop in the APF control method is added. However, the proposed adaptive DC control algorithm excludes current harmonic consideration. In this paper, a hybrid APF with current harmonic compensation is proposed to maintain the AC power source sinusoidal. The SMC approach is utilized for load voltage support and appropriate reference signal tracking. Briefly, the contributions of this manuscript are listed below:  
* An L-C passive filter is connected in series with an APF to compensate the current harmonic. It improves the system efficiency with an extra opportunity in conditions of whether the APF is participating in the compensation process or not. * The SMC approach makes the convergence more achievable, especially in non-linear systems such as the APF injection current. * The control approach is capable of overcoming the voltage unbalancing. * The coding and implementation of the proposed strategy have removed the complexity of the conventional approach. * The power-factor correction (PFC) and reactive power control are accessible.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 3 Results
### Common Wearable Sensors Used in Gait, Balance, and RoM Analysis
#### 3.1.1 Inertial Measurement Units and Magnetometers  
Inertial measurement units (IMUs) are devices that typically contain an accelerometer, a gyroscope, and sometimes, a non-inertial sensor called a magnetometer [78]. There are numerous types of IMUs developed by different companies and the size and weight of these devices are similar. The primary difference between sensors developed by different companies is in the software, in the algorithms used to analyze the data and the housing in which they are mounted. The housing varies depending on the battery and on-board storage. The information collected from these devices depends on the subject's movements performed while wearing the devices.  
Accelerometers are the most common sensor used in gait, balance, and RoM research using IMU devices. Accelerometers are embedded within wearable sensors and the data is often given in three dimensions, acceleration forces in the X, Y, and Z axes. These forces may be caused by the constant force of gravity pulling at the feet or caused by moving or vibrating the accelerometer. Some researchers prefer a single signal of acceleration in order to be orientation invariant, thus, avoiding misalignment issues [79]. To achieve this, the magnitude of the acceleration using three-dimensional data is calculated using Equation (1), where \(a_{x}\), \(a_{y}\), and \(a_{z}\) are the accelerations in the X, Y, and Z axes, respectively.  
\[a_{m}=\sqrt{a_{x}^{2}+a_{y}^{2}+a_{z}^{2}} \tag{1}\]  
A gyroscope is a sensor that uses the Earth's gravity to help determine the orientation and angular velocity. Usually, the design consists of a freely-rotating disk mounted into a spinning axis in the center of a larger and more stable wheel. When the axis turns, the disk remains stationary to indicate central gravitational pull. The main difference between the accelerometer and gyroscope is that one can sense rotation and the other cannot. In a stationary scenario, the accelerometer can determine orientation with relation to Earth's surface, but when acceleration is applied to the device, the accelerometer is unable to differentiate between that movement and the acceleration provided through gravitational pull.  
Magnetometer is a non-inertial sensor that measures magnetic fields. A simple type of magnetometer is a compass, which provides a simple orientation in relation to the Earth's magnetic field. Magnetometers, in ubiquitous computing applications, are often used to improve measurements regarding orientation, especially heading. A challenge in the application of magnetometer is that magnetic disturbances limits the accuracy of their measurements. Fortunately, there are ways to compensate for these errors, which will be discussed in Section 4.11.
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 3 Experimental Measurements
### Mean Square Error of Wavefront Distortion
The magnitude of the MSE of wavefront distortion decreases in the following order: heavy rain, dust storm, snowy, light rain, sunny, and cloudy conditions. Figure 16: MSE of wavefront distortion in measured samples. In link 2, the MSE of the wavefront distortion remains relatively stable and low under sunny conditions compared to other conditions, with less wavefront distortion. The MSE under sunny conditions is slightly higher than under cloudy conditions, indicating more wavefront distortion on sunny days. Compared to sunny and cloudy conditions, the MSE of the wavefront distortion increases significantly under light rain, leading to more severe wavefront distortion. The MSE is highest under heavy rain, indicating the most severe wavefront distortion. The magnitude of the MSE of wavefront distortion decreases in the following order: heavy rain, light rain, sunny, and cloudy conditions. In link 3, the MSE of wavefront distortion is consistently higher under sunny conditions than under cloudy conditions, with greater variability, indicating more wavefront distortion on sunny days. Under cloudy conditions, the MSE of the wavefront distortion shows some fluctuations, but it generally remains at a lower level compared to sunny conditions. This suggests that cloudy conditions cause less wavefront distortion, maintaining better optical quality. The magnitude of the MSE of the wavefront distortion decreases in the following order: sunny and cloudy conditions.
# Study on Temperature Correction of Asphalt Pavement Deflection Based on the Deflection Change Rate
## 2 FWD Test  
To study the deflection of asphalt pavement under FWD loads and its relationship with the road temperature, as well as to establish the temperature correction relationship of the deflections, this paper selected an in-service highway asphalt pavement with a service life of 12 years in Shanghai province and conducted FWD tests under different temperatures and seasons. The traffic volume is 499 pcu/h. The test section is a semi-rigid base asphalt pavement, and the specific structure is shown in the Table 1.  
The test section is 1.5 km long and has three lanes in total. Deflection measurement points were arranged in each lane at intervals of 20 m. The measurement points were marked to ensure that each detection was performed at the same location. In principle, the FWD test were to be conducted once a month, but if there is a large temperature difference in that month, two tests were conducted. FWD testing schematic is shown in Figure 1a. The whole test lasted one year. The radial distance of each measurement point is 0, 20, 30, 45, 60, 90, 120, 150 and 180 cm.  
To determine the temperature distribution in the structural layer of the asphalt pavement, two temperature monitoring points were arranged in the test road section, and eight platinum thermistor sensors were embedded in each monitoring point, which were buried 2, 4, 6, 8, 10, 12, 14 and 16 cm away from the surface. The platinum thermistor sensors' distribution diagram is shown in Figure 1b. The temperature of the structural layer was observed throughout the day, with temperature data being collected every 5 min and the prevailing air temperature being recorded at the same time.
## 6 Discussion  
The previous results show that the MLP-PSO model performs worse for binary and multi-class classification, with accuracy scores of 96.25% and 95.32%. Conversely, the MLP-BP obtains better accuracy scores (98.97% and 98.41%) for binary and multi-class classification. Furthermore, the MLP-BP obtains better results for FPR, with a value of 0.13% for binary classification and 0.45% for multi-class classification. Furthermore, FNR scores 1.20% and 1.15% for binary and multi-class classification, respectively. Although both types of errors are detrimental to IDSs, FNR (type II error) is more damaging and dangerous since it incorrectly classifies the intrusion attacks as benign. Table 19 summarizes the overall performance scores of the proposed models.  
We point out that these results may differ according to the dataset or the strategy. Through our experiments in this study, we find that TensorFlow and Keras have sufficient tools to achieve perfect results without needing an external optimization algorithm. In other situations, it may be helpful to use an optimization algorithm to achieve the best results.  
Furthermore, the PSO does not achieve better scores and requires a great deal of training time and hardware resources. Therefore, we suggest building a DNN with TensorFlow and Keras and evaluating the performance before using an external optimization algorithm. After that, according to the outcomes, it can be determined if an optimization algorithm is required.  
Additionally, we advise adjusting the learning rate and trying several times to achieve excellent scores and converge toward the minimum in a less noisy manner. TensorFlow and Keras provide several methods to achieve this, such as LearningRateScheduler and ExponentialDecay.  
The best performance scores of the proposed MLP-BP and MLP-PSO models compared with those in related works are given in Table 20.
# A Blockchain Solution for Remote Sensing Data Management Model
## 4 Experiment
### Security and Availability  
The remote sensing data trusted and traceable storage system designed in this paper is more secure than the traditional system. As mentioned above, traditional remote sensing data storage is a centralized technology based on cloud computing or cluster computing. Once the central server is destroyed, the data in it will be tampered with and destroyed. Although the public chain is a completely decentralized technology, its upload and download performance is slow, meaning that it is difficult to meet the throughput of large-scale data in global monitoring applications. At the same time, participation in the consensus process by people unrelated to the production process of a particular remote  
Figure 5: We read remote sensing data information using Remix (own elaboration).  
sensing product is redundant. Although the upload and download speed of the alliance chain is greatly improved compared with the public chain, incomplete decentralization means that it is constrained by the central node, and the network also has hidden dangers, such as witch attacks.  
Therefore, this paper considers a hybrid chain design method, and the multi-chain structure, IPFS storage network, and Ethereum technology jointly build a more reliable remote sensing data sharing system. This is mainly because: (1) The availability of the prototype system benefits from the ability to store various remote sensing data. Remote sensing images are stored separately on IPFS, and their stored hash values are stored in blocks together with metadata, which not only protects the data privacy, but also ensures user interaction with the system. (2) The designed RSIC and RSOC hybrid chain transmits data through IPFS and is applied to the remote sensing data sharing network. The hash value transmitted by IPFS is stored in RSIC, and the public product data hash block on RSIC is uploaded to RSOC, and the security is verified through the comparison of the hash value. (3) All transactions are recorded and stored in the immutable ledger of the blockchain, thus providing a high degree of transparency and traceability for the production of each datum in a secure, trusted, reliable, and efficient manner. (4) The \"IPFS + blockchain\" method not only retains the advantages of the decentralization and security of the blockchain, but also solves the long-standing problem of scalability, and can store remote sensing big data.
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 4 Discussion  
### Grassland Area Change Analysis  
Grassland area changes on the TP depend on the trade-off between the rate of grassland degradation and greening. After overlaying the five land cover products, the percentage of grassland area on the TP determined by three or more products is 55.16%, 60.40% and 60.15% in 2000, 2010 and 2020, respectively. The grassland area on the TP expanded from 2000 to 2010, because the area of grassland greening is much larger than the area of grassland degradation. From 2010 to 2020, the percentage of grassland area decreased slightly, and the degraded grassland area exceeded the green area, resulting in a net loss of 8455 km\({}^{2}\) of grassland. The rate of grassland degradation has accelerated in the last decade and has outpaced the rate of grassland greening. The slope of NDVI decreased from 70.48%  
Figure 8: Distribution of observed grassland degradation points (**a**) and their spatial consistency with six remote sensing indexes (**b**).  
(2010-2019) to 62.11% (2000-2009), indicating a slowing trend of vegetation greening on the TP [51].  
The trade-off between grassland degradation and greening is influenced by a combination of factors, and the same factor has different effects at different stages. As a major global greenhouse gas, elevated atmospheric CO\({}_{2}\) concentration promotes photosynthesis, reduces vegetation stomatal conductance, and alleviates the water limitation of vegetation growth [52]. The impact of climate change on grassland greening on the TP is mainly achieved through warming because high-altitude vegetation is usually limited by low temperatures [53], while the relationship between warming and grassland greening is nonlinear [54]. Before reaching the optimum temperature for photosynthesis, the increase in temperature increases the greenness of vegetation, while beyond the optimum temperature, the photosynthesis of vegetation is weakened [55].
## 3 Results and Discussion
### Link Budget Simulation
#### 3.7.3 Bit Rate  
In Figure 21, the bit rate and code rate are varied across different spreading factors. With a higher spreading factor, the bit rate decreases significantly for all code rates. In addition, increasing the code rate (x-axis) from 4/8 (indicated as 1/2 in the figure below) to 4/5 will increase the bit rate, as it is shown in the figure below.