esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Naval Maintenance  
Degradation is traditionally considered a measured performance characteristic of cumulative changes over time leading to system failures [36]. Equipment maintenance aims to prevent the degradation of systems and thus reduce downtime. Traditionally, maintenance is classified based on whether it is performed as preventive or corrective maintenance [37]. Preventive maintenance is conducted to avert accidental equipment failure through scheduled repairs or the replacement of worn components, following established procedures. In addition, corrective maintenance involves performing the necessary repairs to restore the equipment to its working condition after a breakdown. Understanding the concept of maintenance is crucial despite the significant confusion associated with the terminology used to define the types of maintenance.  
A systematic classification proposed by [PERSON] [38] considers factors such as associated risks, intervention modes, action planning, costs and available resources to assist decision-makers in choosing the most suitable type of maintenance for parts, equipment, facilities or systems, resulting in four major domains: reactive, proactive, predictive and advanced maintenance. Although traditional ship maintenance relies on the practical knowledge of ship personnel, modern maritime maintenance programs adhere to the standards set by the International Maritime Organization (IMO), employing written procedures and Planned Maintenance Systems (PMS) to ensure effective maintenance practices.  
The development of Computerized Maintenance Management Systems (CMMS), akin to onboard PMS, was driven by advancements in computer operation systems and dedicated software creation. Since 2015, these programs have included modules for maintenance management and feature modules designed to manage information and enhance operational safety [39]. The introduction of CMMS in maritime transport has brought improvements in communication efficiency and speed with the office, easier monitoring of maintenance activities and procurement and simplification of data exchange processes. Currently, the maritime industry estimates the existence of over 70 CMMS programs with various operating features and designs [40].  
Studies conducted by [PERSON] [41], [PERSON] [42] and [PERSON] [43] and [PERSON] [44] addressed maintenance strategies and reliability in maritime systems. [PERSON] introduced Maintenance Progress-FMECA (MP-FMECA), which combines technical activities to prevent failure. [PERSON] focused on enhancing the maintenance plans and optimizing the turbocharger operation. [PERSON] examined the limitations of Reliability Centered Maintenance (RCM) in autonomous ships, particularly in the main engine cooling system, emphasizing the need to consider the voyage duration when developing failure mode. Using data from alarm monitoring and ML, an algorithm was developed to discover anomalistic symptom judgments to be used for ship maintenance prediction [44]
## 2 System Description
### Channel Model  
The channel model that is considered in this paper is an empirical-stochastic model for Land Mobile Satellite MIMO (LMS-MIMO) used in [31; 32]. The stochastic properties of this channel model are obtained from the S-band tree-lined road campaign's measurement done in a suburban area using dual circular polarizations. The Markov model in Figure 2 is used to determine the large-scale fading (shadowing) states, which varies between low and high shadowing values for both co-polar and cross-polar channels. The four possible states in the Markov model are due to the high or low shadowing state of both the cross-polar (XP) and co-polar (CP) channels. The \(4\times 4\) transition matrix, P, below obtained from the measurement in [33] is used to determine the next possible channel state. State 1 is CP High XP High, State 2 is CP Low XP High, State 3 is CP High XP Low and State 4 is CP Low XP Low. The top right corner value of 0.1037 is the probability of \"CP High, XP High\" to \"CP Low, XP Low\".  
\[P=\left[\begin{array}{cccc}0.6822&0.1579&0.0561&0.1037\\ 0.2887&0.2474&0.0447&0.4192\\ 0.1682&0.0966&0.1745&0.5607\\ 0.0098&0.0199&0.0150&0.9554\end{array}\right] \tag{1}\]  
The small-scale fading is modelled using Ricean distribution. The Ricean fading for each of the MIMO channels is generated using Ricean factors. The details of how the large scale and small scale  
Figure 1: 5G satellite network system architecture.  
Figure 2: Four-state Markov model of a Land Mobile Satellite (LMS-MIMO) channel.  
fadings are obtained are shown in [33]. The path loss (in dB) at 2 GHz in GEO satellite channel can be computed as follows:  
\[L_{Fs}=190.35+20\log\Bigl{(}\frac{38500+D}{35788}\Bigr{)} \tag{2}\]  
The total loss considered in this channel model includes large scale fading, small scale fading, the path loss (\(L_{Fs}\)), and polarization loss. The inter-spotbeam interference, \(I\), has been considered in this channel model. This interference is obtained as a result of power received from satellite gNodeBs sharing the same frequency. Details of how the interference is computed and the considered channel parameters are provided in [31]. The Signal-to-Noise plus Interference Ratio (SNIR) for each subchannel can be expressed as follows in dB:  
\[SNIR[dB]=[EIRP+G_{R}-L_{Total}-(N+I)][dB] \tag{3}\]  
The Channel Quality Indicator (CQI) is determined from the computed SNIR in (3) using the SNIR-CQI mapping used in [31] for a Block Error Rate (BLER) of \(10^{-3}\). This SNIR-CQI mapping can be expressed as follows:  
\[\begin{split} if\ SNIR<-3.8;\quad CQI=1\\ if-3.8\leq SNIR\leq 22.6;\quad CQI=(0.55*SINR)+3.45\\ if\ SNIR>22.6;\quad CQI=15\end{split} \tag{4}\]
# Data-Driven Construction of User Utility Functions from Radio Connection Traces in LTE
## 5 Performance Assessment
### Implementation Issues  
The method is designed as a centralized scheme that can be integrated into OSS platforms. Due to its simplicity, its computational load is relatively low. The theoretical time complexity increases linearly with the number of analyzed connection traces. In practice, the most time-consuming process is trace pre-processing, which can be done by trace processing tools provided by OSS vendors and the classification process, which is performed by using an unsupervised algorithm and can be implemented, along with the rest of the method, in any programming language (in this work, Matlab [42]). Specifically, the total execution time for the considered datasets in a 2.6-GHz quad-core processor laptop is less than 5462 s (92 s per 1000 connections).
## 4 Design Trade-Offs of Distributed Open Systems
### CALM Principle  
Some queries are impossible to resolve in distributed open systems. Intuitively, in a distributed open system, some data may not be accessible; therefore, queries that need to take into account all the information of the system such as those that count the data that satisfy some constraints (e.g., counting the exact number of web pages that include a certain word) are impossible to resolve.  
The Consistency as Logical Monotonicity (CALM) principle describes those queries that can be resolved in a distributed system without coordination [52]. A system is considered to be _logically monotonic_ if the truth of a given statement cannot change by considering new information. In such systems, the responses to distributed queries are consistent.  
The designer of a distributed system can check the monotonicity of its queries as follows:  
**Order independence:**: This is a needed condition for logical monotonicity [52]; i.e., if the system behavior depends on the order in which the information is received, then it is non-monotonic. For instance, in the double-spending problem, where an agent tries to spend \"the same coin\" twice, the state depends on which payment was made first. Therefore, it is a non-monotonic problem.
**Monotonicity:**: By definition, if new information may revoke a previously valid response to a query, the query is non-monotonic. For instance, counting the number of positive votes for an answer in a Q&A system is non-monotonic, since new votes would change the response.
**Formal analysis:**: This can prove the logical monotonicity of a system [52].  
In distributed open systems, non-monotonic queries may produce non-consistent results without a coordination mechanism. Thus, in the presence of non-monotonic queries, the designer should decide on the consistency requirements of the system.  
**Guideline 1**.: _Monotonic queries can be consistently resolved in open distributed systems without coordination technologies._  
Thus, in the presence of network partitions, choosing perfect consistency over availability can be implemented without coordination using logically monotonic systems. If inconsistent behavior, such as missing some votes in a Q&A system, is acceptable for the system, then coordination mechanisms are still not needed.  
**Guideline 2**.: _Consistency requirements are a design decision. If inconsistent behavior is acceptable for the non-monotonic queries of the system, coordination technologies are not required for open distributed systems._  
Moreover, some non-monotonic open distributed systems may achieve eventual consistency without coordination, as explored in the next subsection.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Invertebrates
#### 2.1.7 Leafhopper Inspired Jumping Robots  
The hind coxae of leafhoppers are huge and are connected to each other at the midline by means of a protrusion from one coxa that inserts in a socket of the other coxa and acts like a snap closure. Its function is not sure, as larvae do not have this structure, and they can jump anyway. In leafhoppers, the fast and simultaneous depression of the trochantera around the coxae of hind legs are the key joint movements to power a jump. Cocontraction could result in storage of energy in skeletal structures in the thorax. The body asset is initially determined by the front and middle legs, and take-off is powered by the hind legs. In some jumps, the front and middle legs may contribute to the thrust during take-off, but they always loose contact with the ground a few milliseconds before the hind legs, thus their contribution to take-off is limited [61; 62].  
[PERSON] et al. used a high-speed camera to record the jumping of leafhoppers. By tracking and analyzing the recorded videos, they found that leafhoppers maintain a constant acceleration during take-off. Based on this findings, three generations of leafhopper-inspired jumping robots [136; 137; 138] have been proposed proposed. In particular, \"Grillo-II\" is the first application of eccentric cam in jumping robots and \"Grillo-III\" can mimic the constant acceleration characteristic during take-off by optimizing its four-bar mechanism. By adding fixed wings, the robot could land stably and jump again. At the moment the jumping height and distance of \"Grillo III\" are relatively low compared to the body length [38].
# Using Allan Variance Based Semi-Parameter Model to Calibrate Pointing Errors of Alt-az Telescopes
###### Abstract  
In this paper, a new model called Allan variance based semi-parameter model is put forward to improve the pointing precision for alt-az telescopes. As for electro-optical tracking systems, the precision of line-of-sight (LOS) pointing is seriously influenced by both geometric and nonlinear error sources. In modeling part of the parameter model, a simplified mount model is set up to demonstrate impacts of geometric errors that are caused by imprecision during mechanical processes on LOS pointing, according to values of encoders and miss distances in charge-coupled-device (CCD) cameras. Considering the limitations of mount models, an improved hybrid model called the Allan variance based semi-parameter model is introduced to compensate for residual nonlinear errors in the calibration portion. Compared with the classical mount model, the Allan variance based semi-parameter model removes both geometric errors and nonlinear errors, and the LOS pointing precision has been remarkably improved after calibration of the proposed model.  
alt-az telescopes; LOS; pointing errors; nonlinear error; mount model; Allan variance; Allan variance based semi-parameter model +
Footnote †: journal: Applied Mechanics and Physics Letters
## 6 Decision Algorithm
The average accuracy of defect detection from the decision method suggested in this research is highly satisfactory, according to the results. The fault-detection methods are compared between BPNN, PNN, and RBF, the latter of which are the currently used methods of fault detection. Each method is studied in two cases in the same set. The results in Tables 4 and 5 show the percentage of fault detection accuracy in each phase and coil distance. The average accuracies of internal winding faults in Table 4 are very close in value. Every method in every case has an accuracy of over 92.59%, most of which are up to 100%. When considering the accuracy of each method of detecting internal winding faults, it was found that PNN, BPNN, and RBF methods gave an accuracy of up to 98.15%, 98.15%, and 97.22%, respectively. The accuracy of the three methods was very similar. This supports that all three methods can be used to detect internal winding faults.
Sometimes researchers eliminate observations with missing values or a known imputation method is chosen without the certainty that the chosen method achieves a better, equal or worse approximation than other known methods in relation to the data pattern inherent in the set. There is no perfect method and it generally depends on the preferences of the researchers [20]. The aim of this study is to investigate which of the common missing data imputation methods best fits the available data pattern. For this purpose, missing data imputation simulations were performed with different methods as an application to a real case which is used as a first step for a further study on pollution prediction in the region of Silesia, Poland. This study provides a relatively fast and efficient computational method to choose the imputation method among the best known ones or those to be tested in order to perform the imputations of missing values of data sets prior to other subsequent studies that require complete data sets, as is the case for time series. For this purpose, the method was applied to data from seven pollution stations to observe the performance of the method for comparison. This methodology consists of taking a data set and eliminating the observations that contain at least one missing value in any of the variables so that a data set with all known observations is obtained. For each of the variables, known values are replaced by missing values in k groups of equal size. These replacements are random and in such a way that each observation of the variable is replaced only once by a missing value and assigned to one of the k groups. All observations end up being replaced only once. In each variable the replacement is performed independently of the rest; therefore, in the same observation index (row of a table of values) there may be variables that contain a missing value and others containing a known value. The missing values are imputed with each method in each round. Thus, the performance can be compared when imputing the same missing values by different methods. Another advantage is that, since the algorithm has replaced, imputed and compared all observations only once, the entire set of values is trained and validated so that the imputation methods demonstrate their ability to represent the data set and its inherent distribution under equal conditions for all imputation methods. Studies related to the absence of missing data in air quality research usually consider the data to be Missing at Random (MAR) [23; 30; 31]. For each of the seven available stations, the following imputation methods were applied: linear imputation (LI), predictive mean matching (PMM), random forest (RF), unconditional mean (UMI), k-nearest neighbours (k-NN) and imputation by Kalman smoothing on structural time series (Kalman). To evaluate the performance of each of the above methods and for each station, RMSE was used as a metric.
## 10 Conclusions  
In this study, the influence of adopting a developed RL based Q-learning algorithm to distinctly predict the channel parameters for every user device in the MISO-NOMA system is analyzed. In the developed Q-learning algorithm, the Q-model is created on the basis of the initialized channel statistics then updated based on the interaction between the Q-agent and the environment to maximize the received downlink sum rate and minimize the estimation loss. The efficacy of the developed Q-learning procedure is investigated by inspecting the performance of the proposed algorithm against different benchmark channel estimation schemes. The first benchmark scheme is based on standard MMSE procedure, the second approach is applying DL based LSTM network, the third scheme is implementing RL based actor-critic algorithm, and the fourth benchmark scheme is using RL based SARSA algorithm. In addition, the reliability of the proposed Q-learning procedure is validated by analyzing the behavior of the developed Q-learning algorithm in different fading channels. Furthermore, we provided a scenario that explores how the proposed channel prediction method based on Q-learning algorithm and the derived power allocation structure are both cooperatively employed for multiuser recognition in the MISO-NOMA network. Simulation results emphasized that dependability can be ensured by the developed Q-model even when the number of users in the cell is increased. Furthermore, the simulation outcomes in terms of BER, Outage probability, and individual user rate have demonstrated that the developed Q-learning algorithm for channel estimation jointly with an optimized power scheme can both realize consistent performance.  
Conceptualization, M.G.; Methodology, M.A.; Validation, A.A.; Formal analysis, M.G. and M.A.; Supervision, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Stability in Simulated Gastrointestinal Fluids  
NLCs have been formulated not only for increasing bioavailability of the SLM but also to protect the active components from degradation during gastrointestinal transit. The integrity of the nanoparticles can represent an important prerequisite to reach the sites of absorption in the intestine and obtain increased uptake at the intestinal epithelium itself [39]. For this purpose, the physical stability of the formulation in simulated gastrointestinal conditions was determined by subjecting it to simulated gastric digestion at pH 2.0 in presence of popsin, followed by simulated intestinal digestion in presence of the pancreatin-lipase-bile salts mixture.  
After 2 h of incubation in SGF followed by 2 h of incubation in SIF, NLCs resulted stable and no aggregation or degradation phenomena occurred (Table 4). This stability in acidic and intestinal conditions may be due to the effect of steric stabilization of nonionic surfactant, Brij S20, which decreases the flocculation and coalescence phenomena with its molecular structure. In addition, this stability can be conferred also by the properties of lipids, Stearic acid and Cappyol 90, able to protect encapsulated SLM by enzymatic degradation and by pH conditions [40].  
\begin{table}
\begin{tabular}{c c c} \hline
**Medium** & **Size** & **PDI** \\ \hline SGF & 258.4 \(\pm\) 3.9 & 0.34 \(\pm\) 0.05 \\ SIF & 205.2 \(\pm\) 2.8 & 0.28 \(\pm\) 0.10 \\ \hline \end{tabular}
\end{table}
Table 4: Physical stability of SLM-NLCs in simulated gastrointestinal fluids (SGF: simulated gastric fluid; SIF: simulated intestinal fluid). Results are expressed as means \(\pm\) standard deviation of three experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Freeze-Dried Product** & **Particle Size (nm)** & **PDI** & \(\zeta\)**-Potential (mV)** & **EE (\%)** & **Yield (\%)** \\ \hline SLM-NLCs & 383.6 \(\pm\) 4.7 & 0.32 \(\pm\) 0.03 & \(-\)30.1 \(\pm\) 0.8 & 64.1 \(\pm\) 0.2 & 97.8 \(\pm\) 0.1 \\ SLM-NLCs + Glucose & 205.2 \(\pm\) 2.7 & 0.16 \(\pm\) 0.04 & \(-\)36.7 \(\pm\) 0.5 & 87.6 \(\pm\) 4.9 & 98.9 \(\pm\) 0.2 \\ SLM-NLCs + Mannitol & 305.1 \(\pm\) 9.5 & 0.24 \(\pm\) 0.01 & \(-\)32.6 \(\pm\) 0.7 & 76.0 \(\pm\) 3.1 & 99.2 \(\pm\) 0.1 \\ \hline \end{tabular}
\end{table}
Table 3: Physicochemical characterization and reaction yields of SLM-NLCs after freeze-drying process. Light Scattering and High performance liquid chromatography (HPLC) analyses were performed after reconstitutions of the samples in water. Results are expressed as means \(\pm\) standard deviation, \(n\) = 3.
## 4 Temperature Dependence
### Temperature Dependence of Nonlinear Fast Elastic Parameters  
Curves reporting the velocity variation dependence on strain are obtained for each temperature. The focus in this work is only on the fast contributions to nonlinearity (\(\delta v_{\text{fast}}\)) and results for Berea and concrete are reported in Figure 5. In the case of Berea, a notable effect is observed, with relative velocity variations that nearly double within the temperature range considered. On the contrary, no trend is observed for concrete; thus, fast nonlinearity seems to be temperature-independent.  
Curves reported in Figure 5 are all well fit by the sigmoidal-like function proposed herein (Equation (6)). The fit is shown by a continuous line in the plots. Thus, the fitting parameters \(a_{1}\), \(a_{2}\) and \(a_{3}\) could be used to quantify the nonlinearity dependence on temperature. Rather than discussing the dependence of the parameters on temperature, the authors  
Figure 4: Linear viscoelastic parameters vs. temperature: velocity (**left**) and damping (**right**). Data are averaged over repetitions of the experiment and the errorbar is given by the semidispersion.  
prefer to consider some of the properties of the fitting curves in Equation (6), which are more meaningful:  
* The asymptotic value given by \(a_{2}/a_{3}\), which represents the upper limit of velocity variations for large strains (note that the sigmoidal-like behavior is expected to be valid only till a maximum strain range, beyond which other mechanisms might take place and, thus, such asymptotic value does not really exist);
* The inflection point of the curves, obtained by setting to zero the second derivative of Equation (6);
* The slope of the linear approximation around the inflection point, given by the value of the derivative of Equation (6) at the inflection point. This value could be considered as the equivalent of the hysteretic parameter \(\alpha\) often used in NRUS measurements, even though it is uncalibrated here. Recall that the hysteretic parameter \(\alpha\) is not related to the damping coefficient used here (and is called \(\alpha\) as well).  
The nonlinear properties vs. temperature for Berea are reported in Figure 6. As expected, the nonlinearity increases with temperature, while the inflection point moves to lower strain amplitudes. The slope (somehow similar to the hysteretic parameter \(\alpha\)) increases (in modulus) with temperature, with an increase of about 50 percent. Of course, no temperature dependence trend is observed for concrete.  
The results for damping are discussed in Appendix C.