esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Overview  
In this study, a fast, secure, serverless, robust, survivable, easy to manage, and scalable distributed file system has been developed for medical big data sets. This system is a smart distributed file system developed primarily for Krikkale University and called Krikkale University Smart Distributed File System (KUSDFS). KUSDFS is structurally designed to manage large amounts of data such as Hadoop, NFS, and AFS. The contributions of this study are explained in detail below. Today, the amount of data in the health sector is near 10% of data produced globally [42]. Healthcare software systems do not tolerate waiting from server that they order a process on, so all transactions should be run as soon as possible or within an acceptable timeout period. Hadoop and the other solutions have a lot of burdens to accomplish these tasks conveniently. Because they are mainly created for big data storage needs and to overcome grid computing operations by the MapReduce framework. As a result, for Krikkale University healthcare software, there is a crucial need to implement its own distributed file system, here is the key factor in our study.  
Comparisons with other data processing methods have been made to evaluate the performance of the system. The characteristics of the comparison systems are:  
Hadoop  
The Hadoop configuration used for comparison includes one name node and three data nodes. Red Hat Enterprise Linux Server 6.0 runs on each node. Also, Java-1.6.0, MongoDB-3.4.2 and Hadoop-2.7.2 is installed on each node.  
CouchBase  
One of the systems used to compare is Couchbase, which is a popular NoSQL database system. In a study by [PERSON], she claims that Couchbase is clearly as powerful as the other popular NoSQL databases [43]. In the system, each bucket is made up of 20 MB of clusters. The test machine has 6 GB of RAM and 200 GB of storage space. Microsoft SQL Server 2014 is also selected and installed on the same machine to make comparisons for RDBMS database engine.  
System components are: client applications, data nodes, virtual file system, and replica nodes.
# Axial Load Transfer Mechanism in Fully Grounded Rock Bolting System: A Systematic Review
## 3 Analysis and Results  
### Descriptive Results  
#### 3.1.1 The Trend of Research on the Axial Load Transfer Mechanism of FGRBS  
Figure 3 depicts all the publications and citations between 1988 and 2024. The first article, which investigated the axial load transfer mechanism in FGRBSs, was published by [PERSON] and [PERSON] (1988) [31] in International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts. The analysis revealed interesting trends in publication frequency and citation patterns. Notably, there were gaps in publication output during specific periods: 1989-1992, 1994-1995, 2000, 2005-2010, and 2012. In fact, only nine articles were published prior to 2013. However, a significant surge in publications is evident from 2013 onwards, with the peak year being 2023, which saw 10 publications. This surge in interest for rock bolting can be attributed to several parameters, including technological advancements, geological considerations, and evolving mining practices. On the technological front, recent innovations in rock bolt types, grouting materials, and installation techniques have likely made these systems more attractive options. For instance, [PERSON] and [PERSON] [32] developed a novel fibre optic sensor (FOS) to monitor the axial response of FGRBSs. This innovation has the potential to improve the monitoring and performance evaluation of the FRGBSs. The shift from open-pit to underground mining presents another main reason for the increased focus on rock bolting systems. Underground environments necessitate robust ground support due to changes in in-situ stress, which can significantly impact the stability and potential failure of the surrounding rock mass [33; 34]. Indeed, deep mining operations, in particular, can trigger phenomena like rock bursts and squeezing [35]. As a result, along with applying the FRGBSs, this necessitates the design and implementation of reliable bolt support systems, potentially incorporating new energy-absorbing rock bolts, such as cone bolts, D-bolts, Garford solid bolts, Roofex bolts, and Yield-Lok bolts with high load capacities and substantial deformability [9; 36; 37].  
The first article within the dataset was cited in 1991. Interestingly, the total number of citations for publications before 2012 was only 109. Mirroring the rise in publications, the number of citations also increased significantly after 2012. By the end of the review period (2024), the articles had been cited a total of 2041 times. The years 2022 and 2023 witnessed the highest number of citations per publication, with 338 and 293 citations, respectively.  
Figure 4 visually depicts the distribution of research output across different countries within the reviewed literature. Notably, China, with 41 articles, stands out as a leading contributor, publishing a significantly higher number of articles compared to countries like Australia, Canada, and Iran. The remaining countries each contributed less than five articles on the topic to the dataset.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 3 Experiment 1: Preliminary Word Error Rate and Listening Effort Measurements
### Materials and Methods
#### 3.1.3 Listening Test  
We took the following information from the participants: Age group (21-30, 31-40, etc.), presence of hearing impairment, the kind of audio equipment used ('good quality headphones', 'normal quality headphones', 'good loudspeakers', 'normal loudspeakers', and 'bad equipment') and whether the listener had close contact with laryngectomees.  
We had 57 native Spanish participants (from Spain) in this test, out of which 15 had close contact with laryngectomees and hence, were familiar with OS. The listeners were from the age range of 21 to 70. There was no hearing impairment reported for any participant.  
Participants listened to 5 randomly chosen stimuli from each of the 6 speakers, a total of 30 stimuli. Each of these 30 stimuli was a different sentence. Using a Latin Square design [39], coverage of all the 180 stimuli was ensured.  
The participants were instructed to use headphones, pay close attention to the stimuli, and provide the responses honestly and uninhibitedly. There was a sound sample provided along with the instructions for them to ensure they could hear the sound properly. Participants typed what they heard and provided the LE rating on the aforementioned Likert scale. If they missed some portions or were unsure of what they heard, they were asked to put three dots (...) in that place. The first two sentences were presented as practice sentences (one HS and one OS), to familiarise the participant with the task. These sentences were sampled from the same corpus of 100 sentences but were different from the ones presented in the actual test.  
The listening test ([[https://aholab.ehu.eus/users/sneha/Listening_test.php](https://aholab.ehu.eus/users/sneha/Listening_test.php)]([https://aholab.ehu.eus/users/sneha/Listening_test.php](https://aholab.ehu.eus/users/sneha/Listening_test.php))) was web-based, and it was possible to reach out to a wide range of participants. However, this also meant differences in audio equipment, the effects of this on the responses are reported in the Results section.
# Evaluation of Biodentine Tricalcium Silicate-Based Cement after Chlorhexidine Irrigation
## 2 Materials and Methods  
Fifty-four Biodentine specimens were prepared with strict adherence to the manufacturer's instructions; five drops of dedicated liquid were added to the powder inside the capsule that was immediately closed and mixed for thirty seconds at a speed of 4000 rpm in a mixing device (Dental Mixer SYG200, Septodont, Saint-Maur-des-Fosees, France). Subsequently, the cement was transferred to cylindrical polyvinyl molds and compacted with a fitted plugger on a smooth glass surface. The size of specimens was 8 mm in diameter and 3 mm in height. Three setting times, measured from the end of mixing process, were adopted: 12 min [9] (group A), 45 min [5] (group B) and 24 h [29] (group C). The specimens from each group, after being removed from the PVC mold, were immediately polished with rotating sandpapers for 20 s (successive grits of 600, 800, 1000, and 1200) and immersed in 10 mL of 2% chlorhexidine digluconate (Gluko-Chex 2%, Cerkamed, Stalowa Wola, Poland) for 5 or 20 min. Half of the containers with irrigation liquid and specimens were additionally placed in ultrasonic device Sonic-0.5 (50 Hz frequency, Polsonic, Warsaw, Poland) in order to enhance the action of chlorhexidine. All of the specimens were then immersed in demineralized water for 30 s and dried with compressed air, in order to eliminate any unnecessary precipitates from cement surface. The control group of 6 specimens did not undergo any irrigation protocol.  
Figure 1 illustrates the distribution of cement specimens as well as adapted irrigation protocols.  
The influence of chlorhexidine on the cement surface was evaluated using two techniques: visual microscopic assessment and chemical analysis. The surface of cement specimens was investigated with a scanning electron microscope (SEM, S-3000N, HITACHI, Tokyo, Japan). The analysis was performed from the area of 2 mm \(\times\) 3 mm with magnification of 1.0 k, 15 kV accelerating voltage, and 15 mm working distance. The cement chemical composition was evaluated via SEM using the X-ray microanalysis with energy dispersive spectroscopy method (EDS) with the aid of Vantage software (Version 4.1,  
Figure 1: Presentation of cement specimens’ distribution and irrigation protocols.  
Thermo Fisher Scientific, Waltham, MA, USA) and AZtec software (Version 1.0A, Oxford Instruments, TubneyWoods, Abingdon, UK). The detector dead time was set at 25-30%.
## 3 Overview of Short-Range and Long-Range Communication
### Long-Range V2X Communication  
Long-range V2X communication uses cellular networks, such as 4G or 5G, to communicate between vehicles and infrastructure over longer distances. Unlike short-range V2X, which is designed mainly for safety-critical applications, long-range V2X is mainly intended to provide a broader range of services, such as high throughput data transfer and streaming, infotainment, navigation, and remote vehicle management. Long-range V2X communication can provide real-time traffic information, such as congestion and road closures, over much wider areas than short-range V2X. It can also enable new services, such as remote driving, remote vehicle diagnostics, software updates, and over-the-air services. Additionally, long-range V2X can, similar to short-range V2X, support vehicle platooning, where a group of vehicles travel closely together to improve fuel efficiency. However, there are also challenges to implementing long-range V2X communication. For example, it requires a reliable and robust cellular network, which may not be available in all areas. Additionally, traffic from other users, may have a negative impact on the network performance and QoS provision may not be guaranteed. This issue could be resolved by exploiting 5G network slicing. Network slicing in 5G allows the simultaneous existence of multiple virtualized and independent logical networks on a shared physical network infrastructure. Each network slice represents a distinct and self-contained end-to-end network that is customized to meet the specific QoS demands of a particular application like V2X. Tailor-made slicing provision and configuration for different V2X use-cases needs to be supported by the Mobile Network Operators (MNOs). This may be a challenging task that has several techno-economic aspects, especially in cross-border scenarios, where a V2X service (e.g., remote driving) using a dedicated slice from a certain MNO must be handed over seamlessly to another similar slice from another MNO as the vehicle crosses the border. Moreover, advanced 5G NR capabilities, the use of Frequency Division Duplex (FDD) and mini-slot level scheduling make 5G NR worthy enough to provide low latency communication in V2X scenarios.
# Space Robot Sensor Noise Amelioration Using Trajectory Shaping
## 2 Materials and Methods
### Whiplash Compensation  
Whiplash compensation was proposed as a solution to the flexible spacecraft system control problem in [2]. To prevent overshoot, [2] proposed a driving function that creates motion in the opposite direction as the desired final position.  
The whiplash compensation trajectory generation scheme was implemented in SIMULINK(r) and follows the format of Equation (19).  
\[z=(A-A_{0})[\sin(\omega t+\varnothing)] \tag{19}\]  
The flexible spacecraft system was simulated using MATLAB(r)'s SIMULINK(r). A variable-step size was used along with MATLAB(r)'s automatic solver selection. Figure 3 shows the topography of the flexible spacecraft system SIMULINK(r) model. The input trajectory options are shown on the left part of the figure and include a sine trajectory, a square wave, and the shaped-whiplash trajectory. The PID controller and second order filters are applied, and have been described in Sections 2.3 and 2.4, respectively. The final rotation angle is examined, and its performance assessed Section 3.  
The state and rate sensor errors are introduced in the SIMULINK(r) model in order to mimic realistic performance. The state and rate sensor errors are defined by a normally distributed random number with 0 mean and 0.01 variance. The sample time used is 0.01 for the state, rate, and inertia error values. The seed value is generated as a uniformly distributed random number.  
Figure 3: Flexible spacecraft system simulation created in SIMULINK®. Subsystems are displayed in Appendix B.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable/Acronym** & **Definition** & **Variable/Acronym** & **Definition** \\ \hline \(z\) & Sinusoidal trajectory & \(\omega\) & Frequency \\ \(A\) & Desired magnitude & \(t\) & Time \\ \(A_{0}\) & Initial magnitude & \(\varnothing\) & Phase offset \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of proximal variables and nomenclature 1.  
The simulation was performed 1000 times for Monte Carlo analysis. Each simulation included the state and rate sensor noises. The Monte Carlo analysis was performed for each of the three trajectory generation schemes: (1) step function, (2) sinusoid trajectory, and (3) whiplash compensation.
# Thermal Conductivity and Rheology of Graphene Oxide Nanofluids and a Modified Prediction Model
## 3 Results and Discussion
### Thermal Conductivity of Graphene Oxide Nanofluids
#### 3.3.4 Our Proposed Model  
The above results show that both the theoretically predicted values are still significantly deviated from the experimentally measured values of thermal conductivity. In order to accurately predict thermal conductivity values of GO nanofluids for practical applications such as its promising applications in solar collectors, it is necessary to propose a more comprehensive and accurate model.  
To consider more factors which influence thermal conductivity of GO nanofluids, we propose an equation format to correlate the thermal conductivity as a function of temperature \(T\) and mass concentration percentage \(\varphi\), with four coefficients from a to b to modify each term in the thermal conductivity equation. The new format of equation is shown as follows:  
\[\frac{k_{nf}}{k_{bf}}=a*(\frac{T}{T_{0}})^{b}*(\varphi^{c})+d \tag{3}\]  
where \(k_{nf}\) is the thermal conductivity of nanofluids, \(k_{bf}\) is the thermal conductivity of base nanofluids, \(\varphi\) is the mass concentration percentage of nanofluid, \(T\) is the temperature of nanofluid and \(T_{0}\) is the constant value of equilibrium dimension temperature, here taken as 273.15 k; a-d are all undetermined parameters. Using the experimental data for fitting, the final expression of the present model can be obtained as follows:  
\[\frac{k_{nf}}{k_{bf}}=1.171*(\frac{T}{273.15})^{14.12}*\left(\varphi^{0.9004} \right)+0.992,\ \ R_{sqr}=0.97 \tag{4}\]  
The above correlation characterizes the relative thermal conductivity \(k_{nf}/k_{bf}\) versus temperature \(T\) and mass concentration percentage \(\varphi\). The three-dimensional coordinate diagram of this equation is shown in Figure 8.  
Figure 8: Three-dimension diagram of the proposed correlation.  
Figure 7: Comparison of experimental data for GO/DI-water-based nanofluids with results from models. (**a–f**) under temperature of 25, 30, 35, 40, 45 and 50 \({}^{\circ}\)C, respectively.  
In order to verify the accuracy of our proposed correlation, the experimentally measured value is compared with the correlation value, and the results are shown in Figure 9. According to the comparison, the absolute value of the maximum relative deviation is only 2.008%. This demonstrates that the output data of our correlation are reliable, which can be used to predict the thermal conductivity of graphene oxide nanofluids. As shown by the Figure 9, our proposed model is reasonably in better agreement with the experimental results than those compared with the two previously reported correlation models.
## 3 Explicit MPC Controller Design  
### Control Structure of Active Jet Noise Control of Turbofan Engine  
The structure diagram of an active jet noise controller based on the explicit model prediction is shown in Figure 5. The explicit model controller is divided into two parts: offline solution and online solution. Offline solving introduces multi-parameter programming to solve the constrained quadratic programming problem; that is, to solve the multi-parametric quadratic programming (MPQP) problem about jet noise. The state feedback explicit control rate is calculated, that is, the piecewise affine system (PWA) between the optimal control amount of the engine and the current state amount in a specific flight state, and the optimal control variable is expressed as:  
\[U=F_{i}\Delta X+G_{i} \tag{15}\]  
where \(\Delta X\) is the difference between the system state quantity and the command, namely \([\Delta N_{h},\Delta N_{l},\Delta SPL]^{T}\), which are the engine high and low pressure speed and the sound pressure value of the jet noise at the measurement point, respectively, and \(U\) is the obtained optimal control quantity, namely \(\left[w_{f},a_{fan},A_{microjet}\right]^{T}\). \(F_{i}\) and \(G_{i}\) are constant matrices, and the subscript i corresponds to the i-th partition area of the PWA system. The size of the two constant matrices depends on the current system state quantity \(X\) and the controller input \(\Delta X\). The online solution of the explicit MPC needs to search for the corresponding partition and constant matrix according to the current state quantity and command, and calculate the optimal control quantity at the current moment, according to the explicit solution of the control rate on the partition. This solution does not require repeated online rolling optimization calculations, and the calculation time is greatly reduced, and it is easy to realize the project of the online real-time model predictive control.  
Figure 4: The structure of integrated model of turbofan engine and jet noise.
## 3 Results and Discussion
### Cross-Validation (CV) and Partial Least Squares Regression
#### 3.4.1 Cross-Validation  
With the parameters used for CV, the smoothed spectra yielded the lowest _RMSE_ (1.8%) with 2 latent variables (LVs). The performance of the model was significantly poorer from 6 LVs (Figure 7) and this did not improve using LOOCV which is considered the most accurate [39]. If this is the case, [PERSON] [46] manifests that the model is overfitting the data and is not useful for predicting unobserved data. To our knowledge, this unusual behavior of the data in cross-validation has rarely been reported in the literature. According to several authors [39; 47; 48] the optimal number of LVs is determined when only marginal improvements in model performance are observed, but with our data this marginal improvement was not clear, and the optimal number of LVs was selected based on lowest _RMSE_ (Figure 7A). The \(R^{2}\) in most cases was negative, indicating a bad model fit (Figure 7B). Negative \(R^{2}\) resulted in model calibration, which has been also reported [19].  
Figure 6: Correlation coefficient between individual spectral curves and TC. This graph shows the negative correlation between soil carbon content and soil reflectance. The higher correlation occurs near 550 nm.  
Figure 7: Performance of partial least squares regression (PLSR) in cross-validation with \(k=5\). **(A)** Root-mean-squared error (_RMSE_), and **(B)**\(R^{2}\). These graphs could be used to indicate the consistency of the data for modeling. Both curves indicate that the data is not useful for modeling.
# Centimeter-Level Precise Orbit Determination for the Luojia-1A Satellite Using BeiDou Observations
## 3 BeiDou Multipath Modeling with Luojia-1A Observations  
The high-quality BeiDou observations are the prerequisites of LEO POD. Hence, we analyzed the BeiDou observations from the Luojia-1A satellite before starting the orbit determination procedure.
## 3 Results and Discussion
### Effects of Media Composition on the Intracellular Accumulation of Lipids and Carotenoids
Similar total carotenoid production has been previously reported for other _R. toruloides_ strains. [PERSON] et al. [36] observed a carotenoid production of 763.85 \(\upmu\)g/g DCW when using the UV-mutant _R. toruloides_ M5 strain and optimizing the culture conditions. [PERSON] and [PERSON] [37] observed a total carotenoid production of 717.35 \(\upmu\)g/g DCW with the yeast _Rhodotorula mucilaginous_ MTCC-1403 and using an acidic extract of onion peel and mung bean husk. The yeast _Sporobolomyces_ _roseus_ CFGU-S005 showed a final carotenoid production of 3.43 mg/L from a fed-batch culture and using a concentrated pasta processing waste hydrolysate as medium [38]. Higher values of carotenoid accumulation have also been reported in the literature. A total \(\upbeta\)-carotene accumulation of 1.42-2.03 mg/g DCW has been observed when cultivating the yeast _Rhodotorula glutinis_ in crude glycerol and sugar cane molasses [39; 40]. [PERSON] et al. [32] reported a final concentration of \(\upbeta\)-carotene as high as 2.80 mg/g DCW using the yeast _Rhodotorula pauldigena_ CM33 and a glucose-based synthetic medium. These authors even increased this value up to 3.25 mg/g DCW by optimizing media composition with external nutrient addition and the use of sucrose as a carbon source. In the present study, biowastes with different nature have been used for the production of both lipids and carotenoids without the need of supplementing the medium with external nutrients. These results show the great potential of this approach as bioprocessing alternative to provide added value and contribute to the better management of resources, thus reducing the associated environmental impact of waste.