esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
###### Abstract  
With the development and popularization of the Internet and the rise of various live broadcast platforms, digital videos have penetrated into all aspects of people's life. At the same time, all kinds of pirated videos are also flooding the Internet, which seriously infringe the rights and interests of video copyright owners and hinder the healthy development of the video industry. Therefore, robust video watermarking algorithms for copyright protection have emerged as these times require. In this paper, we review robust video watermarking algorithms for copyright protection based on original videos and compressed videos. Basic models and properties of video watermarking algorithms are described, and the evaluation indexes corresponding to each property are also introduced. To help researchers understand various existing robust watermarking algorithms quickly, some basic information and the quantitative estimation of several performances are analyzed and compared. Finally, we discuss the challenges in the research of robust video watermarking algorithms, and give possible development directions for the future.  
video watermarking; copyright protection; robustness; performance comparison; evaluation indexes; overview +
Footnote †: journal: Applied Sciences
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 5 Experiment and Results
### Strong Rules of DTU Fault Data
#### 5.2.1 Planned Maintenance  
In some cases, wireless signal quality, software operating states, and family defects cause DTU offline. Family defects, humidity, hardware operating states, and DTU upgradation may lead to remote signaling faults. Mis-operation is likely to cause remote control faults. These eight rules form the initial fault rule base. In planned maintenance, when a certain type of fault happens, corresponding factors in the fault rule base should be of great concern. Taking measures to avoid those factors can lower DTU fault incidence in the future.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### K-Nearest Neighbor  
K-nearest neighbor (kNN), in the context of anomaly detection, is an algorithm that measures the distance of a data point to its kth nearest neighbor as the anomaly score [26]. We used this approach to measure the density of a dataset probability distribution. The value of the anomaly score depends on the dataset, the number of dimensions, and its normalization. As a result, it is challenging to select an appropriate value for the nearest neighbor.
## 4 Components and System
### Antennas
#### 4.2.1 Antenna Selection
These substrates are expensive compared to FR4, but the cost is still significantly lower than the mechanical fabrication of a horn antenna. In addition, a large number of antennas can be fabricated simultaneously, which significantly reduces the cost of a single antenna. The feeding of a planar antenna can be done in many ways. Usually, coaxial connectors are used, which couple directly into a microstrip line. Since the antenna can be integrated on the same substrate as the RF circuitry, this method is very common, especially in mass production. One example is patch antennas in automotive radar systems. Due to the high frequencies of 24 GHz or 77 GHz, large antenna arrays can be integrated into a small area. These achieve a high gain and the radiation pattern can also be adjusted via the array configuration. The high frequency range allows a comparatively large absolute bandwidth despite the relatively narrow-band patch antenna. They are therefore less suitable for use in radar systems with a large relative bandwidth. Instead, so-called Vivaldi antennas or tapered slot antennas have found widespread use in ultra-wideband applications. It was first introduced in 1979 by [PERSON] and con sists of an exponentially expanded slot line [28]. The electric field is initially concentrated in the narrow region in the slot and then slowly coupled to the free space by the flare and finally detached. Due to the absence of a resonant structure, the Vivaldi antenna is in principle very broadband. The upper and lower cutoff frequency is determined by the dimension along the slot. The gain can be adjusted via the opening rate and the length. The simple implementation and generally good performance make the Vivaldi antenna ideal for use in the radar system presented.
## 3 Description of the Modified Method
### FOPDT under PI Control  
Assuming the simplest case, when the PI regulator (6) controls the FOPDT plant (13), the transfer functions of the open-loop and closed-loop system with unitary feedback (\(H(s)=1\)) will be then equal to, respectively,  
\[G_{o}(s)=K_{p}\cdot\left(1+\frac{1}{T_{iS}}\right)\cdot\frac{Ke^{-\theta s}}{( 1+Ts)} \tag{37}\]  
\[G_{c}(s)=\frac{KK_{p}(1+T_{iS})e^{-\theta s}}{(1+Ts)T_{iS}+KK_{p}(1+T_{iS})e^{ -\theta s}} \tag{38}\]If the transient response is to be without overshoot, the zero of the closed loop should be compensated with the same pole. This will happen when the integral time constant is equal to the time constant of the model.  
\[T_{i}=T \tag{39}\]  
Then, the Equation (38) will be simplified to the form  
\[G_{c}(s)=\frac{KK_{p}e^{-\theta s}}{T_{i}s+KK_{p}e^{-\theta s}} \tag{40}\]  
which can then be transformed to (30).  
The second-order system will be critically damped when both of its poles are located at the branch point of the root-locus plot, i.e., as follows from (36), when two conditions are satisfied, that is when the denominator of the transfer function of the closed-loop system \(D\)(s) and its derivative \(\frac{dD(s)}{ds}\) are equal to zero.  
\[D(s)=T_{i}s+KK_{p}e^{-\theta s}\ \ =0 \tag{41}\]  
\[\frac{dD(s)}{ds}=T_{i}-\theta KK_{p}e^{-\theta s}\ \ =0 \tag{42}\]  
From this second relationship, we derive  
\[e^{-\theta s}=\frac{T_{i}}{KK_{p}\theta} \tag{43}\]  
Inserting the right-hand side of Equation (43) into (41) in place of the expression \(e^{-\theta s}\), we derive the following solution:  
\[s_{1,2}=-\frac{1}{\theta} \tag{44}\]  
Inserting the value of the poles (44) into the characteristic Equation (41), it is possible to determine the proportional gain of the PI controller, at which the system will have double real poles, which means that it will be critically damped. Taking into account (39), this gain should be equal to  
\[K_{p}=\frac{T}{e\cdot\theta K} \tag{45}\]  
Minimization of Settling Time  
In a control system with such calculated settings as (39) and (45), the settling time by assuming a 2% error band can be estimated from the following relationship:  
\[t_{s}\cong 6.5\cdot\theta \tag{46}\]  
If we assume that a very small overshoot is acceptable as long as it does not exceed the width of the error band, then the gain can be higher but should not exceed the value  
\[K_{p1}=\frac{1.25\ T}{Ke\theta} \tag{47}\]  
Although such a control system will no longer be formally critically damped, it will allow for further reduction of the rise time and also the settling time, the latter to the value  
\[t_{s}<4.1\cdot\theta \tag{48}\]
# Biomass from Green Areas and Its Use for Energy Purposes
## 3 Results and Discussion
### Results of Calorimetric and Thermogravimetric Tests  
Table 5 presents the results for the percentage content of ash and volatile substances, as well as the calorific value of solid fuels containing waste biomass from green areas. In the case of the analyzed pellets, the ash values were at a very low level. Pellets with 100% fir cones (P7) had the lowest ash content. The highest amount of ash was recorded in solid fuels composed of 50% fir cones and 50% sawdust from coniferous trees (P8). In the case of chestnut and acorn pellets, the addition of coniferous sawdust reduces the ash content in pellets P1, P2, P3, and P4. The opposite tendency was observed for pellets from pine and fir cones. The addition of coniferous sawdust increased the ash content (P6 and P8) compared to pellets without this component (P5 and P7). For volatile compounds, slight differences were noted between the tested types of pellets. The highest sum total of volatile compounds (80.20%) was found for pellets were made of 50% coniferous sawdust and 50% chestnts (P2). On the other hand, the smallest amount of volatile compounds was recorded throughout the combustion of pellets made of fir cones (P7--74.64%). In the case of the analyzed pellets, the admixture of sawdust from coniferous trees increased the sum total of volatile compounds (P2, P4, P6, and P8) in comparison with pellets made without this addition (P1, P3, P5, and P7). In the case of calorific value, its high value was found at the level of 16.95 MJ\(\cdot\)kg\({}^{-1}\) to 19.54 MJ\(\cdot\)kg\({}^{-1}\) (Table 5). Pellets made of 100% acorns (P3--19.54 MJ\(\cdot\)kg\({}^{-1}\)) had the highest calorific value. Pellets made of 100% pinecomes (P5--19.30 MJ\(\cdot\)kg\({}^{-1}\)) also had a high calorific value. In the case of the analyzed biomass, the addition of sawdust from coniferous trees slightly reduced the calorific value of the tested pellets. In turn, the lowest calorific values were found in chestnts pellet (17.5 MJ\(\cdot\)kg\({}^{-1}\)) and chestnts pellet with 50% coniferous sawdust (16.9 MJ\(\cdot\)kg\({}^{-1}\)). According to literature reports, the total ash content in wood pellets varies. In general, woody biomass and herbaceous biomass have ash contents of 1-8% and 1-19%, respectively [10]. The ash content in wood pellets is 0.55%, while the bark content is a maximum of 10% in the composition of this raw material [41]. Analyzing the pellets made of coniferous and deciduous sawdust, the ash content in the analyzed fuels was found to range from 0.31 to 0.56% [32]. Impurities present in the biomass for the production of solid fuels affect the high ash content in the produced pellet [41; 42; 43]. Pellets from agricultural waste were characterized by an ash content above 4%, and the addition of sawdust from coniferous trees in the amount of 50% significantly reduced the ash content in the tested
## 3 Fuzzy Evaluation of Surrounding Rock Stability of Ioess Tunnel with Rich Water
### Establishing the Evaluation Set and Indicator Set for Influencing Factors  
The analysis of loess surrounding rock stability should be approached from the main factors causing deformation and failure of the surrounding rock, following the principles of importance, independence, and ease of access for determination. For the instability characteristics of water-rich loess surrounding rock, the primary considerations are the soil parameters of the surrounding rock, stratigraphic factors, advanced support, and excavation construction factors. Since these factors are difficult to express quantitatively, and due to the complexity of loess under the coupling of water and force, it is challenging to analyze the stability of the surrounding rock using purely qualitative and quantitative indicators for a specific project. The degree of influence of each factor can only be judged based on vague empirical assessments. According to the classification of loess tunnel surrounding rock, analogical analysis of water-rich loess tunnel projects, regulatory requirements, and expert evaluations, the influencing factor indicator set \(U=(U_{1},\,U_{2},\,U_{3})\) at the criterion level in fuzzy hierarchical analysis is determined. The qualitative classification of the stability state of loess surrounding rock is divided into five states: stable, relatively stable, moderately stable, poor stability, and extremely unstable, represented by the evaluation set \(V=(V_{1},\,V_{2},\,V_{3},\,V_{4},\,V_{5})\). The influence of each factor on different stability states is represented by fuzzy variables as shown in Table 3.
## 2 Literature Review and Exploration of Concepts
### Supply Chains
This framework addresses quality and data authenticity issues in supply networks, offering a solution for challenges related to outsourcing and production surpluses. Furthermore, the framework presented in the paper implements smart contracts with unique features for supply chains. It includes a multi-layered participant schema, allowing flexibility for supply chain structures and enabling decentralized fund control for participants. Data related to processes and transactions are stored entirely on the Ethereum Virtual Machine (EVM), ensuring data protection, transparency, and traceability. The framework also incorporates bidirectional flow for returns and an extensive inventory tool, enhancing supply chain transparency, traceability, and security while preventing misconduct or misuse by participants [29].
# Experimental Investigation on Contaminated Friction of Hydraulic Spool Valve
## 4 The Experimental Analysis on Contaminated Friction of Spool with Different Morphologies
### The Experimental Analysis on Contaminated Friction of the Conical Spool  
Compared with the standard morphology and rough spool, the topological structure of the flow field in the conical spool fit clearance changes greatly. As seen in Table 1, the inlet clearance of cis-conical spool is large and the outlet is small, whereas the inverted cone spool is the opposite. To maintain the consistency of the fit clearance size of the standard morphology spool, the median values of fit clearance size are selected as the reference size of the clearance.  
The friction curves of cis-conical and inverted cone spools with the size of solid particulates are compared with the standard morphology spool respectively are highlighted in Figure 16.  
The size of the cis-conical spool with particulates is similar to other spools; the small size particulates are not obvious in spool stagnation, but particulates in the sensitive size range have significant impact on spool movement. Comparing with the curve of the spool with standard morphology, for the cis-conical spool, the lower limit of stagnation-sensitive size range is reduced, and the contaminated friction increased within the range of sensitive size. Regarding the inverted cone spool, the value of friction showed a trend of increasing first and then decreasing with increasing size of solid particulates, the lower limit of stagnation-sensitive size ranges is reduced, and the contaminated friction is less than standard morphology spool under the condition of the solid particulates is 0.9 times the size of clearance.  
Compared with the fit clearance of standard morphology spool, the cis-conical spool fit clearance has a larger inlet and smaller outlet. For the cis-conical spool, on the one hand, the smaller clearance outlet gives small size particulates greater opportunity to occur the tri-contact and generate friction, which reduces the lower limit of stagnation-sensitive size range compared to the standard morphology spool. On the other hand, the sensitive size particulates in the working medium are easier to enter into clearance of cis-conical spool and cause the amount of particulates rising that increase the friction directly. The reasons for the lower limit reducing in stagnation-sensitive size of invert cone spool are the same as cis-conical spool: the 0.9 times the size of clearance can be regarded as an inverted cone spool inlet and the small particulates in the sensitive size do not easily enter the clearance thus the friction is smaller.  
It is found that the conical spools increase the contact between small size solid particulates and the clearance matching surface, increasing the friction of spools in the sensitive size range compared to the standard spool; therefore, the conical spool has been an important factor in aggravating the clamping stagnation of spool movement.  
Figure 16: The curve of conical spool on contaminated friction with different particulates size.
# Modeling of Dry Clutch Wear for a Wide Range of Operating Parameters
## 4 Wear Rate Characterization
### Wear Rate Dependences on Individual Operating Parameters  
Visual inspection of the recorded wear rate characterization data related to all the operating points from Figure 3 has been performed to gain preliminary insights into the wear rate dependence on individual operating parameters. The parameter inputs \(\left(\overline{T}_{d},\ \overline{\omega}_{0},\ \overline{M}_{z},\ \text{and}\ \overline{t}_{2}\ \right)\) are calculated as average values of the cycle-wise parameters over the full set of \(n\) cycles recorded for a specific operating point. The operating point dependence plots of wear rate are shown in Figure 9, along with corresponding low-order polynomial fit lines (dashed black lines). The related correlation indices (obtained by Matlab function _corr_(.)) are given in Table 1. Evidently, the temperature has the most dominant, progressive influence on the wear rate (Figure 9a) with the correlation coefficient of 0.744. The slip speed is somewhat less influential with the correlation index of 0.295 (Figure 9b), while the two remaining inputs, the clutch torque and the closing time, appear not to have significant influence (Figure 9c,d), with the correlation indices of 0.21 and 0.173, respectively. However, although having marginal influence when analyzed individually, the last two parameters may influence wear rate in combination with other parameters (i.e., in a nonlinear interactive manner; see Section 6 for more details).  
Figure 8: Run-in and run-out responses for 13 tested friction plates.  
Figure 9: Recorded (dots) and interpolated wear rate values (dashed lines) shown in dependence of (**a**) clutch temperature, (**b**) initial slip speed, (**c**) torque and (**d**) closing time.
# Does Organic Farming Increase Raspberry Quality, Aroma and Beneficial Bacterial Biodiversity?
## 3 Results
### In Silico Fruit Validome Assembly  
To estimate the role of bacterial organic volatile compounds (bVOCs) on fruit volatilome, the emission of bVOCs by the bacterial species isolated from raspberry fruit was analysed by PTR-MS (Table S2). To mimic the fruit environment, bacterial isolates were grown on filter sterilized raspberry juice. The \(m/z\) emissions of each bacterial isolate, weighted by the relative abundance of its genus calculated from NGS data, were used to construct an in silico-assembled bacterial volatilome for organic and IPM grown fruit. PCA showed a clear discrimination of organic and IPM berries according to the volatilome of fruit associated bacterial community (Figure 8a). To identify the most relevant bVOCs among the 118 masses of the dataset, the loading values of Principal Component 1 (77% of explained variance) were extracted. The \(m/z\) fragments that showed the highest loading values were \(m/z\) 107.0349, \(m/z\) 33.033, \(m/z\) 97.0274, \(m/z\) 62.0216, \(m/z\) 59.049, \(m/z\) 69.033 and \(m/z\) 87.08. \(m/z\) 33.033, \(m/z\) 69.033, \(m/z\) 97.024 and \(m/z\) 107.0349 were putatively identified as methanol, furan, furfural and benzaldehyde, respectively, whereas \(m/z\) 59.049 and \(m/z\) 87.08 may attributed to linear aldehydes and ketones (C3 and C5, respectively) (Figure 8b).
# A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
###### Abstract
In recent years, new research approaches have considered machine learning-based algorithms for detecting and mitigating multipath/NLoS effects. By extracting certain features in the positioning unit, ref. [11] proposed a classifier based on supervised machine learning to categorize GNSS pseudorange measurements into three types: clean, multipath, and NLoS. Moreover, ref. [16] adopted an unsupervised method to identify satellite signals with multipath using carrier phase, pseudorange, and carrier-to-noise ratio measurements. Additionally, ref. [17] formulated multipath modeling as a regression task, fitting the multipath errors with respect to the azimuth and elevation in the spatial domain. To implement multipath detection and mitigation, these studies utilized various features derived from GNSS raw data as well as additional information. Accurate values of pseudorange biases due to multipath/NLoS effects are pieces of information essential for these machine learning approaches to train the model, and then they are used to detect and mitigate multipath/NLoS effects. Therefore, the primary objective of this research is to provide high-precision pseudorange bias values caused by multipath/NLoS to empower machine learning-based algorithms. Furthermore, these accurately estimated pseudorange biases can serve as a standard for assessing the performance of the methods proposed for detecting and mitigating multipath/NLoS effects. To compute the accurate pseudorange biases caused by multipath/NLoS effects, this research leverages precise corrections and models for GNSS and employs a clustering algorithm, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The main novel contributions of this work can be summarized as follows:  
1. Building upon the theoretical analysis of the pseudorange function, this research defines a leftover term containing the pseudorange biases caused by multipath/NLoS effects. 2. This research derives two probability distributions for the defined leftover term, which motivates the utilization of a clustering algorithm to estimate multipath/NLoS effects. 3. By utilizing a clustering algorithm, specifically DBSCAN, to isolate the other components in the defined leftover term, a procedure is proposed to estimate the values of multipath/NLoS biases if multipath/NLoS effects are present. The paper is organized as follows: in Section 2, the mathematical foundations for the pseudorange function are established, and the definition of the leftover term is provided. Section 3 introduces the proposed method for extracting pseudorange biases caused by multipath/NLoS effects from the leftover term using a clustering algorithm. Subsequently, Section 4 outlines the details of the real-world experiments under both static and dynamic scenarios to validate the effectiveness of the proposed method. Finally, Section 5 draws the conclusions regarding this work.
## 4 Methodology  
Considering satellite-UAV-integrated network visibility, payload computing capacity, maximum link count per satellite, and the total number of communication links, an objective function is formulated to minimize end-to-end delay in satellite networking. To optimize the satellite-UAV collaborative IoT systems topology, we introduce the Graph Partitioning Simulated Annealing (GPSA) algorithm, which combines the graph partitioning and simulated annealing algorithms.  
Furthermore, considering the dynamism and temporal nature of this network, the concept of time slices is introduced, enhancing the efficiency of iterative problem-solving.