esa-sceva/satcom-chunk-collection huggingface dataset:
# NN-Based 8 FSK Demodulator for the Covert Channel
## 6 Conclusions  
In this article, the 8 FSK modulation was proposed for covert communication based on the superposition of the signal. This modulation provides the possibility of transmitter and receiver low complexity and assists considerably in the effective practical implementation of such a system. The main advantage of this solution is the non-coherent transmission and a lower probability of detection by the intruder, thanks to non-constant constellation points on the constellation diagram. This is a major difference from a typical solution, based on the constant point of the phase modulation. Inherently, the covert signal suffers from non-deal SIC operation caused by hardware impairments and estimation channel gain error, mainly because of the low energy of the hidden signal. As was shown, even small errors severely affect the correlator-based detector performance. To solve this problem, an NN-based detector was proposed. Such a detector highly outperforms traditional FSK correlator-based demodulator. The additional use of channel coding improves the performance of the system. In this case, the metrics calculated based on NN outperformed the theoretically calculated metrics for the correlator-based detector. It leads to better soft-decision decoding. Ultimately, the simulation results showed that the NN-base demodulator with the Viterbi decoder provides error-free demodulation, even for normalized channel gain estimator error \(e_{abs}=2\%\), compared to \(e_{abs}=0.5\%\) for the correlator-based demodulator. Because of focusing on non-ideal SIC operation, the impact of channel noise, multipath transmission, and additional radiated undesirable missions [29] were omitted here for clarity. This may be a subject of future studies.  
Conceptualization, K.G.; methodology, K.G. and Z.P.; software, K.G.; validation, K.G.; formal analysis, K.G. and Z.P.; investigation, K.G.; re-sources, Z.P.; data curation, K.G. and Z.P.; writing--original draft preparation, K.G. and Z.P.; writing--review and editing, K.G. and Z.P.; visualization, K.G.; supervision, Z.P.; project administration, Z.P.; funding acquisition, Z.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NATIONAL CENIRE FOR RESEARCH AND DEVELOPMENT, grant number DOB-BIO10/05/03/2019, on \"Teleinformation module supporting the identification of victims of catastrophe and terrorist attacks\" (acronym IDvictim).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to project restrictions.  
The authors would like to express their great appreciation to the sensors journal editors and anonymous reviewers for their valuable suggestions, which have improved the manuscript quality.  
Figure 17: Hard and soft NN-based FSK demodulator with Viterbi decoder. BER performance vs. \(e_{abs}\)[%].
## 2 The Characteristics of Defect Images  
The surface temperature of continuous casting slabs is very high during the production process. The temperature can reach about 800-900 \({}^{\circ}\)C, which results in the surface being oxidized and forming a large number of various shapes scales [1]. The scales seriously interfere with the detection and recognition of defects. At the same time, due to insufficient illumination in the production site and the rough surface of continuous casting slabs, the slab surface appears uneven. Also, in the continuous casting slab production process, the quality of surface images collected is decreased due to the splash of cooling water, rolling mill vibrations, and other factors. Figure 1 shows several common surface images of continuous casting slabs, including cracks, scales, lighting variations, and slag marks. The cracks are true defect, while the scales, lighting variations, and slag marks are the interference factors that may lead to misclassification. The interference factors are labeled as false defects and also as a type of recognition object. The main task of continuous casting slabs is to recognize crack defects in the complicated background and interference factors.  
Figure 1a is longitudinal crack defect sample, abbreviated as cracks. The cracks are mainly along the longitudinal distribution of the slabs, the shape is curved, and the length ranges from a few centimeters to dozens of centimeters. The cracks usually have a certain depth, which shows a different grayscale value than the surrounding pixels in the intense light irradiation. The occurrence of cracks is mostly related to the high-temperature steel and various mechanical behaviors in the solidification process. A crack is a very serious defect.  
Figure 1b is a scales sample. The shape of scales is uncertain and varies greatly. Sometimes, some of the scales are warped, but most scales are attached to the surface of the slabs and show texture features. Due to the scales covering the surface of the slabs, some fine crack defects are hard to identify.  
Figure 1c is the lighting variations. The bright and dark areas are caused by the irradiation of multiple light sources or changes in illumination intensity. The boundaries of the bright and dark areas  
Figure 1: Surface images of continuous casting slabs. (**a**) Cracks; (**b**) Scales; (**c**) Lighting variations; (**d**) Slag marks.  
show straight line shapes, and the gray values on both sides are noticeably different. Therefore, these boundaries are sometimes misclassified as cracks.  
Figure 1d is the slag marks. They are mainly formed by the residual slag. Slag marks are also distributed along the longitudinal direction of the slabs with a certain width, and their gray values are lower than that of the surrounding pixels. These images are easily misclassified as crack defects.
## 4 An Optimal Design  
### Design Procedure Based on the Principle of Uniform Accuracy Lifetimes  
The optimal design based on the principle of uniform accuracy lifetimes can be achieved by the following steps:  
1. Obtain the accuracy loss sequences of the measurement system and its components through experiences or experiments, and determine their accuracy loss allowable values.
2. Set up models for all accuracy loss sequences, obtain their mathematic functions, and then calculate their original accuracy lifetimes \(\{T_{1},\,T_{2},\,\ldots,\,T_{n},\,T_{s}\}\) according to their accuracy loss allowable values.
3. Calculate the uniformity \(\rho\) of the lifetime of the whole system, and judge whether it meets the requirement \(\rho\geq\varepsilon\). If \(\rho\geq\varepsilon\), the system does not need to be redesigned. If not, the system should be optimally designed.
4. Determine the accuracy loss weights of the components, and establish the optimal design model with an objective function, such as the longest lifetime, the lowest cost or the biggest uniformity (see Section 2).
5. Employ the method of combining uniform sampling technology with SQP to solve the optimization model, and then gain the optimal average accuracy loss velocities of the system and its components (see Section 3).
6. On the premise of mastering a large number of test data and accuracy loss laws of typical structures, gradually change some factors such as the structures, parameters,materials and working conditions of the original system according to the above designed results until the uniformity design target is achieved.
## 5 Threats to Validity  
Our Experimental results show that the proposed SNS-based RE approach can effectively elicit user requirements from SNS. In this section, we discuss a few threats to the validity of this research and the way we tried to improve those threats.  
A potential threat to the presented study's internal validity is the human judgement in the data analysis phase, where final requirements have been analyzed by moderators along with domain experts. This might lead to experimental bias as humans tend to be biased in their judgement. However, it is infrequent in requirements interpretation and analysis to employ humans to manually interpret and classify requirements. Therefore, the bias threat which arises from using humans is not imminent, it can be avoided or mitigated by setting up a common mechanism to determine VR and CR. In order to mitigate this threat, we used Equation (1) to reduce bias.  
The threats to external validity affect the generalizability of experimental results [43]. A possible threat emerges due to the fact that our dataset was limited in size and we have used limited Facebook pages and tweets to extract the user requirements. A bigger dataset from more SNS sources might affect the results. However, we found various types of user requirements within our experimental scope. Therefore, even if the data set was not so big in our experiment, we have confidence in our research results. Additionally, we chose Facebook and Twitter for gathering the specific domain concerns, even though there are other SNS like Instagram, LinkedIn, and ResearchGate. Facebook and Twitter are the two most common and general-purpose social networks where billions of users are present. Therefore, we selected only Twitter and Facebook as platforms for our experiment.  
Another threat to validity is that SNS are not built on the perspective of RE. Therefore, comments and tweets might not include meaningful data for software development organizations. The SNS are also used for marketing and advertisement purposes by the software development organization. Therefore, Facebook comments and tweets might only include marketing messages rather than user feedback. Therefore, moderators started the discussion on SNS by posting related queries so that the discussion would be on a focused issue rather than a general discussion. However, the SNS is a major source to get users' instant feedback from diverse end-users, and also a place to put up a variety of opinions freely.  
One of the threats to the validity is a construct validity which is a degree to which variables measure the things they are supposed to. In our study, we identified three dependent variables and one independent variable to measure our SNS-based approach. Obviously, other variables can also be identified to measure other facets as well. Therefore, it is not possible to capture all dimensions in a single controlled experiment. In our study, effectiveness, efficiency and sentiment were the three variables because these measures sufficiently support to prove our hypothesis.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 4 Canonical Sequences and Sets  
Our synchronizer, including the update detector, relies heavily on the algebraic structure of internal command sequences. The theory of this structure has been developed in [12; 18]. We provide high-level overviews of the proofs whenever appropriate to make this paper self-contained and also available to non-experts. For full proofs the reader is referred to the original papers.  
To understand the semantical properties of command sequences, the first step is to investigate command pairs and determine when they commute, and when they require a special execution order. Proposition 1 covers the case when the commands act on the same node, and Proposition 2 considers command pairs on different nodes.  
**Proposition 1** ([12], Proposition 4).: _Suppose \(\sigma,\tau\in\Omega\) are on the same node \(n\). Then one of the following two possibilities hold: (a) \(\sigma\tau\sqsubseteq\omega\) for some \(\omega\in\Omega\) also on the same node \(n\), (b) \(\sigma\tau\equiv\epsilon\), that is, the pair breaks every filesystem._  
Proof.: Suppose \(\sigma=\langle n,x_{1},y_{1}\rangle\) and \(\tau=\langle n,x_{2},y_{2}\rangle\). If \(y_{1}=x_{2}\) then (a) holds with \(\omega=\langle n,x_{1},y_{2}\rangle\). If \(y_{1}\
eq x_{2}\) then (b) holds: if \(\sigma\) does not break the filesystem, then it leaves \(y_{1}\) at node \(n\). Now \(\tau\) requires \(x_{2}\) there, but finds a different value, thus breaks the filesystem.  
For two commands we write \(\sigma\mathbin{\|}\tau\) to mean that the nodes they act on are uncomparable, i.e., the nodes are different and neither is an ancestor of the other.  
Recall that \(\sigma\) is a null command if it has the same input and output value, that is, it is of the form \(\sigma=\langle n,x,x\rangle\).  
**Proposition 2** ([12], Proposition 5).: _Suppose \(\sigma\) and \(\tau\) are non-null commands on different nodes. Then (a) if \(\sigma\mathbin{\|}\tau\) then \(\sigma\tau\equiv\tau\sigma\), and (b) if \(\sigma\mathbin{\|}\tau\) then \(\sigma\tau\
ot\equiv\epsilon\) if and only if \(\sigma\ll\tau\)._  
Proof.: By checking all possibilities.
# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Content-Based Recommendation
It utilizes product-side information to produce product embeddings. By integrating meta-information with interaction data, new embedding vectors are generated for products. The future of content-based recommender systems is inextricably linked with advancements in deep learning. Furthermore, feedback loops derived from users can enhance the adaptability of the utilizing model, ensuring continuous user profile learning and updating. When dealing with large and complex user-item interaction matrices, traditional matrix factorization models tend to underperform [62]. The paper titled \"Deep learning\" [63], published in _Nature_, serves as a fundamental contribution to the field of deep learning. It presents the concept of deep learning by demonstrating how computational models with multiple processing layers can effectively represent data with varying levels of abstraction. These methods achieve significantly improved performance in various tasks. The paper delineates the fundamental equations of neural networks, backpropagation, CNNs, and RNNs, offering readers a comprehensive overview of the domain's evolution, key concepts, and challenges. Currently, the content-based recommendation is extensively employed in industrial-scale recommender systems, as it possesses the following advantages. 1. Personalized recommendations: These recommendations are based on the user's historical interests, ensuring that the recommended content aligns with the user's preferences. 2. Simple principle with strong interpretability: Content-based recommendations can be made based on label dimensions or by embedding items into a vector space using similarity, making this strategy easy to implement. It is also readily accepted and validated by users. 3. Addresses the cold-start problem to some extent: As long as sufficient content attributes are available, new items can be effectively handled without relying on other users' behaviors. However, some of the drawbacks of content-based recommendation led to limitations in its effectiveness and scope of application. It has a narrow recommendation scope, and its novelty is not pronounced. The results obtained from content-based recommender systems tend to converge on categories of items that the user has previously shown interest in. New users, without sufficient interaction histories, might not receive effective recommendations. Moreover, the comprehensiveness, integrity, and accuracy of content understanding can impact the efficacy of the recommendation process. Additionally, based on practical experience, the recommendation accuracy of this approach is not particularly high. Overall, content-based recommendation is a fundamental technique within recommender systems. It suggests items by analyzing item feature vectors and user interest vectors. The aim is to propose new items to users that match their preferences. But people's preferences change over time. So, sequence recommendations came into being.
## 2 Materials and Methods  
### General Situation of the Project  
The 22,031 working face of Xindeng (Zhengzhou, China) Coal Mine mainly mines the 2-1 coal seam, with a vertical distance from the surface ranging between 120 and 150 m. These data are provided by Xindeng (Zhengzhou, China) Coal Industry based on on-sitemeasured data obtained through geological surveys of the actual geological conditions in the working face. The comprehensive coal-rock column diagram is shown in Figure 1. The coal seam exhibits an average dip angle of 8\({}^{\circ}\), thereby classifying it as a gently inclined seam. The burial depth is 120-150 m, the working face length is 119 m, the mining length is 560 m, and the mining area reaches 66,640 square meters. No gangue interlayers are found in the coal seam, so the structure of the 2-1 coal seam in the working face is relatively simple. Experimental data show that the rock mass integrity coefficient for this seam corresponds to Class III-IV fractured coal. Broadly speaking, the coal seam at the 22,031 face is weak, and its roof and floor layers are soft, with a thick immediate roof that will collapse during the extraction process.  
In order to maintain safe production on site and enable the smooth execution of gob-side entry retaining techniques in this working face, it becomes necessary to study the dynamic laws of overlying rock strata and the failure modes of the roof structure. As the working face advances, the exposed length of the roof gradually increases. If a fracture occurs in the unsupported area, it is highly likely to trigger a roof fall accident. According to the analysis results of the mechanical model based on thin plate theory, the initial fracture of the immediate roof occurs 0.5 m away from the lateral side of the goat. Studying the relationship between the fracture position and the advancing step distance can optimize the support timing and effectively prevent roof collapse. Hence, via the combination of field monitoring and numerical simulation to trace real-time roof fracture dynamics and compare theoretical analysis outcomes with practical data, the roof support strategy can be enhanced, furnishing a reliable reference for the safe and high-efficiency application of gob-side entry retaining technology.
## 2 Materials and Methods  
### Patients  
A total of 200 unrelated and consecutive patients were included in the study. Patients with acute kidney disease, acute deterioration of kidney function, or who refused to consent were excluded from the study. A total of 163 patients (68 male, 95 female) were enrolled in the study. Glomerular filtration rate was estimated with the use of the Chronic Kidney Disease Epidemiology Collaboration equation (eGFR) [14]. CKD is categorized according to stages: eGFR between 60 and 80 mL/min/1.73 m\({}^{2}\), eGFR between 30 and 60 mL/min/1.73 m\({}^{2}\), eGFR between 15 and 30 mL/min/1.73 m\({}^{2}\), and eGFR less than 15 mL/min/1.73 m\({}^{2}\)[15]. We included patients with eGFR less than 60 mL/min/1.73 m\({}^{2}\). The control population comprised 218 (89 male, 129 female) individuals recruited from outpatient clinics for annual checkups. Hyperlipidemia was defined when serum triglyceride level was more than 1.7 mmol/L and/or cholesterol level was more than 5.2 mmol/L, and/or the individual was receiving regular administration of lipid-lowering agents. Hyperensive individuals were defined as those subjects having either blood pressure exceeding systolic 140 mmHg and/or diastolic 90 mmHg, and/or receiving antihypertensive treatment. Diabetes mellitus (DM) was defined as a fasting glucose \(\geq\) 7 mmol/L (minimum of 8 h fasting), non-fasting glucose \(\geq\) 11.1 mmol/L, or treatment for diabetes. Cigarette smoking history was based on self-report. Height and body weight were measured with participants standing without shoes and other heavy garments. Body mass index (BMI) was calculated as weight (kg)/height\({}^{2}\) (m). All laboratory measurements were carried out by technicians unaware of the subjects' history of pathological conditions. The Ethics Committee of the University of Debrecen, Hungary, approved the study (DEOEC RKEB/IKEB 3936-2013). The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki. All patients and controls provided written informed consent.
## 2 Methods
### Vegetation Health  
The multidecadal land cover trends across the trail system were quantified using the MODIS Land Cover Type MCD12 QI v6.1 global data product [63]. Using supervised classification, the 500 m spatial resolution MCD12Q1 data are generated by the United States Geological Survey (USGS) from MODIS Terra and Aqua sensors. The MCD12Q1 data were analyzed annually to identify the dominant land cover classifications along each trail corridor for the years 2001-2023.  
The terrestrial primary production was analyzed to quantify the vegetation contributions to the ecosystem energy budget within the Triple Crown network. The terrestrial gross primary production (GPP) includes all the energy captured by plants in the form of biomass, while the net primary production (NPP) also accounts for energy lost through respiration [74]. The long-term trends in the GPP and NPP from 1986 to 2021 were estimated using the Landsat GPP and NPP CONUS data products from the University of Montana Numerical Terradynamic Simulation Group (NTSG) [64]. Compared to 1 km and 500 m MODIS MOD17 GPP/NPP data, the improved 30 m spatial resolution of the Landsat GPP/NPP products is useful for estimating terrestrial production in environments with high spatial variability, such as mountainous terrain.  
PhenoCam data from eleven sites were obtained from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center Phenom Dataset v2.0 [65]. The PhenoCam data include conventional visible-wavelength automated digital camera imagery (red, green, and blue reflectance) collected in situ at high spatial and temporal resolution from stationary ground-based cameras. The images capture the seasonal dynamics and can be used to characterize the phenological patterns of the observed vegetation [75]. Seven PhenoCam sites were located along the AT, two sites along the PCT, and two sites along the CDT. The PhenoCam observations were used as in situ validation data to confirm the Landsat GPP estimates [76; 77]. The ORNL PhenoCam data (acquired Jan 2025) provide estimates of the green chromatic coordinate (GCC), where:  
\[\text{GCC}=\text{Green}/(\text{Red}+\text{Blue}+\text{Green}) \tag{1}\]  
The GCC vegetation index estimates the amount of foliage in the plant canopy and ranges from 0 to 1, with higher values representing a higher degree of greenness [78]. The r\({}^{2}\) between the smoothed ground-based GCC and satellite-derived Landsat GPP time series was used to validate the accuracy of the remote sensing estimates, with maximum r\({}^{2}\) values indicating stronger accuracy.
## 3 5G HetNet Services, Architecture, and Related Technologies
### 5G Technologies
#### Millimeter Wave  
The millimeter-Wave (mm-Wave) band, also known as the extremely-high-frequency (EHF) band, is extensively used in 5G communication systems and operates within a frequency range of 30-300 GHz. The high-frequency band provides a large bandwidth of up to 800 MHz, which accommodates maximum channels and enhances the cell capacity in terms of users (high data rate and low latency) in 5G [57]. Due to the wider bandwidth, this technology (mm-Wave) ensures a higher bit rate, capacity, and improved network efficiency compared to existing generations, making it suitable for 5G communication systems [58]. Despite these advantages, mm-Wave has some limitations, such as high sensitivity to blockage and high path losses (due to buildings, trees, and other obstacles), which can severely degrade the performance of the 5G system. Because of its higher frequency, mm-Wave cannot travel fast and is unable to provide service to UEs at the cell edge or away from the BS. Thus, mm-Wave is used in HetNet due to its small coverage radius.