esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 1 Introduction
### Parameters and Parameter Optimizations
(2009), the evaporation rate increases with increase in temperature [87]. Moreover, the viscosity of solution generally decreases with an increase in temperature. As the humidity increases, the average fiber diameter increases. Parameter optimization: Formation of nanofibers involve many input parameters, as mentioned above, to evaluate outputs such as fiber diameter, tensile strength, modulus, and dielectric properties of nanofibers. Parameter optimization helps to achieve desired outputs by tailoring the input parameters. One among the many mathematical modeling techniques for parameter optimization is Design of Experiment (DoE), which is an approach that helps to find the relationship between different inputs over outputs. Parameter optimization based on applied voltage and concentration has been studied by using the DoE approach by [PERSON] et al. (2005) [88]. The study concluded that concentration of solution played an important role to the diameter of nanofibers. [PERSON] et al. (2005) used two factors and four and three respective levels for finding average fiber diameter. [PERSON] and [PERSON] (2005) examined three variables and seven, four, and three respective factor levels for finding the average fiber diameter [89]. [PERSON] et al. (2018) used DoE approach with two factors and three levels for optimizing the two outputs, namely, specific dielectric constant and specific mechanical strength [90; 91]. A mathematical modeling, including the leaky dielectric model which describes the deformation of a Newtonian drop in an electric field and whipping model which depicts the interaction between the electric field and fluid properties for electrospinning processes, has been portrayed by [PERSON] et al. (2013) [92]. [PERSON] et al. (2016) developed a model for stable region and unstable region in the jet propulsion stream for predicting the fiber diameter [93]. [PERSON] et al. (2014) modeled and simulated viscoelastic elements for jet propulsion to predict and improve control of nanofiber diameter [94]. Modeling electrospinning of nanofibers for short-range and long-range electrostatic interactions, using a discrete slender model, was conducted by [PERSON] et al. (2009) [95]. The whipping instability in the unstable region of the electrospinning jet propagation has been studied in three polymeric solutions by [PERSON] et al. (2005) [96]. The fiber gets stretched into fractions of initial diameter at the instability region. [PERSON] (2014) modeled the electrospinning jet with an inkjet printer technique, using computer-aided fluid/multi-physics/multi-phase flow simulations in COMSOL multiphysics software [97].
# Dual-Indicator Micro-Electro-Mechanical System Monitoring Method for Rock Instability Early Warning
## 3 Experimental Validation and Performance Evaluation  
### Experimental Setup and Modeling  
To validate the applicability of MEMS sensing technology for unstable rock mass collapse monitoring, we developed a MEMS-based simulation monitoring system (as shown in Figure 4). The system comprises three key components: (1) artificially fabricated rock blocks with material ratios matching those of natural bedrock and parent rock formations; (2) integrated sensing instrumentation including MEMS accelerometers and a laser Doppler vibrometer (LDV) system; and (3) hydraulic stabilization jacks for bedrock anchorage.
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
###### Abstract  
Accurate information acquisition is of vital importance for wireless sensor array network (WSAN) direction of arrival (DOA) estimation. However, due to the lossy nature of low-power wireless links, data loss, especially block data loss induced by adopting a large packet size, has a catastrophic effect on DOA estimation performance in WSAN. In this paper, we propose a double-layer compressive sensing (CS) framework to eliminate the hazards of block data loss, to achieve high accuracy and efficient DOA estimation. In addition to modeling the random packet loss during transmission as a passive CS process, an active CS procedure is introduced at each array sensor to further enhance the robustness of transmission. Furthermore, to avoid the error propagation from signal recovery to DOA estimation in conventional methods, we propose a direct DOA estimation technique under the double-layer CS framework. Leveraging a joint frequency and spatial domain sparse representation of the sensor array data, the fusion center (FC) can directly obtain the DOA estimation results according to the received data packets, skipping the phase of signal recovery. Extensive simulations demonstrate that the double-layer CS framework can eliminate the adverse effects induced by block data loss and yield a superior DOA estimation performance in WSAN.  
double-layer compressive sensing; direction of arrival; block data loss; packet size; joint sparse representation +
Footnote †: journal: Sensors
## 1 Introduction and Motivation  
Nowadays, one of the main challenges for the automotive industry is the development of autonomous vehicles, which involves the solution of several problems, e.g., motion-planning, control, design and implementation of the algorithms. In everyday traffic, motion-planning functionality is crucial, in which several requirements must be simultaneously guaranteed, i.e., the vehicle trajectory must be dynamically feasible, safe and comfortable. There are existing technologies for this problem, although they only provide suggestions for the driver during the maneuver [1, 2]. The overtaking maneuvers are especially dangerous since the motion of other participants must be predicted, in order to guarantee the collision-free trajectory. Furthermore, there can be overtaking situations when a feasible trajectory cannot be planned between the initial and the target state of the vehicle. The stability and the trajectory tracking problems of the vehicles under varying circumstances must be solved, which requires the design of robust control systems. In the future, the first autonomous vehicles will probably operate in a mixed traffic environment, which means that, besides the automated vehicles, human-driven cars also will be present on the roads. Therefore, an accurate assessment of the given traffic situation will be an essential step during autonomous vehicle control, which helps to determine the collision-free area. In order to address these tasks, the behavior of human drivers must be modeled and predicted. Since the prediction of a human-driven car involves several uncertainties, it can besolved using probability-based approaches. Due to several uncertainties and differences between the traffic situations the motion-planning is still challenging. Using the collision-free area, and other information about the environment, one feasible trajectory is planned, which fulfills the comfort and safety requirements. In recent literature, several approaches can be found, which aims to deal with this task. In the following, a brief overview of four recent approaches is presented.
## 2 Automatic Tracking Method of Weak Acoustic Targets within Jamming Environment  
This section describes the proposed algorithm--P-Gabor, as seen in Figure 2. Using principal component analysis (PCA), we first denoise the original BTR to improve tracking performance as pre-processing, then we obtain a relatively clean BTR diagram as shown in Figure 2a. The target motion-analysis parameters are then derived using prior linear fitting information as shown in Figure 2b, and those green dots are the discrete track points for each snapshot. Then we use the prior estimated parameter information to generate a template by connecting with the Gabor function as shown in Figure 2c,f. Next, we cut the region of interest (ROI) of BTR as shown in Figure 2d to wait for the matching process; the enlarged jamming part is shown in Figure 2e. Finally, the target bearing is estimated by matching the templates to the target trajectories, as shown in Figure 2f-h. This method is only implemented in the post-processing part, which is the processing unit that previous researchers often neglected in underwater acoustic target detection.
# Integrated Earthquake Catalog III: Gakkel Ridge, Knipovich Ridge, and Svalbard Archipelago
## 4 Conclusions
The catalog name is indicated on the histogram; Figure S4: Modified Figure 9. Magenta line in (b) shows the probability of missing a duplicate in the model of multivariate normal distribution, green line shows the total probability of the first and the second kind errors. Dashed green line in (a) and (b) shows threshold value of Ro = 7, minimizing total error. (c) Green contour line of the metric (1) \(Ro=7\) shows obvious missing of duplicates; Figure S5: Distribution of earthquakes in space and time. (a) Gakkel ridge; (b) Knipovich ridge; (c) Svalbard; Figure 56: Map of earthquake epicenters of three created integrated catalogs: I (Eastern Sector), II (Western Sector) and III (Gakkel Ridge, Knipovich Ridge and Svalbard Archipelago). **Author Contributions:** Conceptualization, I.A.V., A.D.G. and P.N.S. ; Data curation, P.N.S., B.A.D., B.V.D., N.A.S., E.O.K. and Y.V.B. ; Formal analysis, I.A.V. ; Investigation, P.N.S., B.A.D., B.V.D. and  
Figure 25: Non-cumulative frequency-magnitude distributions constructed using original magnitudes. (**a**) Svalbard, arrows mark discontinuities in distribution. (**b**) Knipovich Ridge, arrows mark two modes of distribution. (**c**) Gakkel Ridge, distribution weakly follows the Gutenberg-Richter law. E.O.K. ; Methodology, I.A.V. ; Resources, B.A.D., B.V.D., N.A.S. and E.O.K. ; Software, I.A.V. ; Validation, I.A.V., A.D.G., P.N.S., B.A.D. and B.V.D. ; Visualization, I.A.V., B.V.D. and Y.V.B. ; Writing--original draft, I.A.V., A.D.G., P.N.S., B.A.D., B.V.D., N.A.S., E.O.K. and Y.V.B. ; Writing--review and editing, I.A.V., A.D.G., P.N.S., B.A.D., B.V.D., N.A.S., E.O.K. and Y.V.B. All authors have read and agreed to the published version of the manuscript. The reported study was funded by the Russian Science Foundation, project number 21-77-30010 *System analysis of geophysical process dynamics in the Russian Arctic and their impact on the development and operation of the railway infrastructure-. Not applicable. Data Availability Statement: Data are contained within the article and Supplementary Materials. This work employed data provided by the Shared Research Facility *Analytical Geomagnetic Data Center* of the Geophysical Center of RAS ([[http://ckp.gcras.ru/](http://ckp.gcras.ru/)]([http://ckp.gcras.ru/](http://ckp.gcras.ru/)) accessed on 1 August 2023). The data used in the work were obtained with large-scale research facilities *Seismic infrasound array for monitoring Arctic cryolitozone and continuous seismic monitoring of the Russian Federation, neighboring territories and the world*. This work employed data provided by the International Seismological Centre (2023), Seismological Dataset Repository, [[https://doi.org/10.3-1905/6](https://doi.org/10.3-1905/6) TJZECY]([https://doi.org/10.3-1905/6](https://doi.org/10.3-1905/6) TJZECY) (accessed on 1 August 2023). The authors declare no conflict of interest. The following abbreviations for seismological agencies are used in this manuscript:
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 2 Framework for HWLNet
### Illumination  
In order to comply with the illumination regulations given by the international organizations for standardization (ISO), which specifies 300 lux to max 1500 lux illuminance for office work, researchers [15] have derived a model for optimal illuminance.  
A model for optimal distribution of LiFi APs is given in Figure 6, where the location and strength of LED lights' output are such that the corners of a room have 300 lux and max. illuminance does not exceed 1200 lux, which is well within the parameters of the ISO. Developing LiFi networks with illumination requirements involves positioning LEDs in optimal locations in a room, determining their orientation towards users and each other, developing emission patterns, and determining max. and min. output powers and colour temperatures. A paper [16] proposes a power allocation scheme to obtain the max. number of users under certain colour temperatures.  
The obtained results show that high data rates are in direct coalition with high-colour temperatures due to LED properties, since an increase in energy level provides more visible light, thus implying higher throughput. Another study [17] discovered that maximizing the throughput of data and higher colour temperatures caused an increase in LED flickering. This study suggests that a control mechanism should be in place to prevent eye damage and injuries.  
Figure 6: Illuminance distribution in a room.
## 3 Results and Discussion
### Analysis of Physical Properties  
The strength of the cereal bars changed significantly when the fruit pomace was introduced into the recipe (Figure 2). The use of the grape residues had a positive effect on the mechanical properties of the products. The force required to break down the bars increased by almost 20%. In contrast, the addition of the apple pomace resulted in a decrease in the breaking strength of the cereal bars, but the changes were only significant when the proportion of the apple residue was increased from 10 to 20 g per dough portion. The increased strength of the cereal bars may be related to an increase in adhesiveness, i.e., the strength of the internal bonds holding the product together. The parameters described are closely related to the technological properties and composition of the food raw materials. Higher moisture content, water binding capacity, and the presence of structure-forming substances, such as cellulose or pectin, have a positive effect on the compactness of the product, thereby increasing its resistance to pressure damage [31].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Moisture [\%]** & **TPC [mg/g]** & **IDF [\%]** & **SDF [\%]** & **TDF [\%]** \\ \hline CB & 16.28 \(\pm\) 0.62 \({}^{\text{e}}\) & 1.21 \(\pm\) 0.02 \({}^{\text{b}}\) & 12.23 \(\pm\) 0.11 \({}^{\text{a}}\) & 5.11 \(\pm\) 0.08 \({}^{\text{c}}\) & 17.34 \(\pm\) 0.19 \({}^{\text{ab}}\) \\ \hline G1 & 18.13 \(\pm\) 0.07 \({}^{\text{d}}\) & 1.00 \(\pm\) 0.03 \({}^{\text{a}}\) & 11.60 \(\pm\) 0.07 \({}^{\text{b}}\) & 5.44 \(\pm\) 0.06 \({}^{\text{b}}\) & 17.04 \(\pm\) 0.13 \({}^{\text{b}}\) \\ \hline G2 & 19.50 \(\pm\) 0.36 \({}^{\text{c}}\) & 1.04 \(\pm\) 0.05 \({}^{\text{a}}\) & 10.84 \(\pm\) 0.11 \({}^{\text{c}}\) & 5.45 \(\pm\) 0.04 \({}^{\text{b}}\) & 16.29 \(\pm\) 0.15 \({}^{\text{c}}\) \\ \hline A1 & 21.17 \(\pm\) 0.37 \({}^{\text{b}}\) & 1.03\(\pm\) 0.01 \({}^{\text{a}}\) & 12.26 \(\pm\) 0.12 \({}^{\text{a}}\) & 5.36 \(\pm\) 0.04 \({}^{\text{b}}\) & 17.62 \(\pm\) 0.17 \({}^{\text{a}}\) \\ \hline A2 & 23.64 \(\pm\) 0.23 \({}^{\text{a}}\) & 0.94 \(\pm\) 0.05 \({}^{\text{a}}\) & 11.54 \(\pm\) 0.05 \({}^{\text{b}}\) & 5.75 \(\pm\) 0.07 \({}^{\text{a}}\) & 17.30 \(\pm\) 0.11 \({}^{\text{ab}}\) \\ \hline \hline \end{tabular} CB—control bars; G1—bars with 10 g grape pomace addition; G2—bars with 20 g grape pomace addition; A1—bars with 10 g apple pomace addition; A2—bars with 20 g apple pomace addition. Data are presented as mean (\(n=3\)) \(\pm\) standard deviation; values of each parameter with different superscript letter in the rows are significantly different (Tukey test \(p\leq 0.05\)).  
\end{table}
Table 2: Chemical compositions of cereal bars fortified with grape and apple pomace.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 3 Privacy Techniques
### Tokenization  
Tokenization in FRSs helps to break down the data into smaller components (tokens) to make them easier to analyze and process. This includes breaking down the text into words, sentences, and phrases and breaking down numerical data into numbers and other values [99]. Tokenization also helps to ensure privacy and data security by preventing the leakage of sensitive user data, such as personal information and preferences [100; 101]. Several studies have used tokenization in FRSs. For example, in [102], the authors used tokenization to improve the performance of an FRS. They used a tokenization method based on the federated learning framework, which allowed the recommendation model to learn from distributed data sources without compromising user privacy. In [103], the authors used tokenization to improve the accuracy of an FRS by splitting the data into smaller parts.  
The benefits of using tokenization in FRSs include improved accuracy, privacy, and scalability. In particular, tokenization allows the system to process data more quickly and accurately while ensuring user privacy [104; 105]. In addition, it allows the system to scale to larger datasets without compromising performance. On the other hand, the limitations of using tokenization in FRSs include the complexity of the tokenization process and the potential for data loss [106]. Tokenization can be computationally intensive and time-consuming, and it can lead to data loss if the tokens are not generated correctly. Besides, some tokens may be more difficult to process than others, leading to potential accuracy issues.
# Fretting Fatigue Experiment and Finite Element Analysis for Dovetail Specim at High Temperature
## 5 Discussion
### Partition Characteristics of the Fretting Contact Surface  
It is reported from [14] that the specimen surface can be divided into three regions according to damage mechanism theoretically, namely, the stable wear region (Region I), crack initiation region (Region II), and undamaged region (Region III), as shown in Figure 14b. In this study, three regions can be easily distinguished from the SEM observations in Figure 9, reflecting a high degree of unity between theoretical analysis and experimental results. Moreover, the width of Region II increases slightly as the fatigue load increases. In addition, the intensification of fretting wear will result in the thinning and shedding of the oxide layer on the contact surface. In the SEM observation results of Figure 10 and the pictures of specimens in Figure 14a, the color of Region II is lighter than that of Region I. Therefore, it is obvious that the thickness of the oxide layer has been significantly reduced in Region II due to the dramatically changing stress state, which is consistent with the analysis of fretting fatigue mechanism in [14].  
The stress state and sliding distance of the contact surface under 13,500N fatigue load are shown in Figure 15. Compared with Region I in Figure 15, the gradient of the calculated result in Region I is obviously more significant. The high stresses and relative sliding displacements result in the formation of wear pits, which reduces the effective loading area and increases stress as compared to the unworn specimen. Therefore, the oxidation layer becomes thinner as the fretting wear intensifies, and the fretting wear debris is constantly generated, resulting in fretting damage such as pitting on the fretting surface of Region II. Finally, under the simultaneous action of high-stress gradient and continuous large slip, fretting fatigue cracks were initiated, causing the fretting fatigue fracture from Region II.  
Figure 14: Fretting fatigue damage mechanism for experimental and theoretical analysis. (**a**) Picture of specimens; (**b**) partition of contact surface.  
Figure 13: Peak pressure distribution at the upper and lower end of the contact surface. (**a**) 13,500 N; (**b**) 16,451.6 N.  
The experimental results and theoretical analysis show that the contact surface of fretting fatigue has partition characteristics. The surface in contact can be divided into Region I and Region II by the observation of the specimen. Obviously, Region II has the characteristics of higher stress gradient, larger sliding distance, and thinner oxide layer. Hence, the degree of fretting wear in Region II is significantly higher than that in Region I. The rationality of contact surface partition was verified by the experiments in this paper.
## 5 Discussion  
### EF-NPP and Traditional Ecological Footprint Analysis  
Recently, near real-time MODIS GPP/NPP products have been used in global or regional studies more and more [17; 41]. EF-NPP is an improvement on traditional ecological footprint analysis. The main difference is the calculation method of the equivalence factors and yield factors. EF-NPP calculates the factors by the NPP of different kinds of land, while the traditional ecological footprint calculates by the consumption and yield of bio-productive land. Other than this, EF-NPP can calculate the factors of built-up land, in order to better express the productivity [36; 37; 22].  
Compared with the study published in 2014, which also focused on the ecological footprint of Xuzhou [42], the ecological footprint and ecological capacity values of this study were relatively low, as shown in Figure 6. The main reasons for the difference are the different equivalence factors and yield factors. The cultivated land and forestland equivalence and yield factors of this study were both lower, thus led to the low ecological footprint and low ecological capacity values. From both results we can see that the ecological footprint of Xuzhou increased during the study period, so it is important to establish a reasonable ecological footprint control mechanism in order to improve the ecological carrying capacity, and control and reduce the ecological deficit. As far as the composition of the ecological capacity, it is necessary to optimize the structure of the ecological capacity, and improve the grassland, forestland, and built-up land ecological capacity.  
Figure 6: The comparison of ecological footprint in Xuzhou with [PERSON]’s study.  
Compared with the ecological footprint of the whole of China measured by EF-NPP [43], we found that the ecological footprint and ecological capacity of Xuzhou central area were both lower than the average value of China. As shown in Figure 7, the equivalence factors of cultivated land, forestland, built-up land, and fossil energy land in Xuzhou were lower than the average level of China, meaning the NPP of these bio-productive land in Xuzhou were relatively low. Additionally, the yield factors of cultivated land, forestland, built-up land, and fossil energy land in Xuzhou were lower than the average level of China, meaning the NPP of these bio-productive land did not reach the average level.  
Currently, there is still no standard framework for ecological footprint analysis, and the existing studies of ecological footprint analysis have paid less attention to the relationship between economic development and the ecological environment [44; 45]. Ecological footprint analysis will be improved in the future to measure the natural capital more accurately and more comprehensive.