esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Method and Validation
### Modeling
#### 3.2.2 Model Setting  
Considering the symmetry of the hydraulic collector, the half structure is intercepted to apply in the computational domain for less calculation time, as shown in Figure 8. In Figure 8, the circular end faces of the confluence tubes are defined as the velocity inlets. The inlets of No. 1 and No. 2 provide seawater to the acquisition nozzles at a velocity of 1.26 m/s (equivalent to 80 m\({}^{3}\)/h), and the inlet of No. 2 provides seawater to the transmission nozzles at a velocity of 0.55 m/s (equivalent to 35 m\({}^{3}\)/h). Note that the one-half model was employed in this study and that all the aforementioned volume flow rates were halved. Therefore, the simulated acquisition and transmission flow rates were 160 m\({}^{3}\)/h and 70 m\({}^{3}\)/h, respectively. The top exit of the conveying channel and the vertical surfaces under the acquisition nozzles, namely the surfaces numbered No. 3 to No. 6, were defined as the pressure outlets with a relative pressure of 0. The polymetallic nodules are distributed in large quantities on the seafloor at a depth of 3.5-6.5 km [4], with a hydrostatic pressure of 35-65 Mpa. The ambient pressure in this study was set as 60 MPa. The cross-section that is numbered No. 7 is the symmetrical plane of this computational domain. Other surfaces were set up as walls. The hydraulic collector was fixed, and the nodules flow was relative to it. The particles were continuously injected with a speed of 0.5 m/s on the ground and their abundance was set to 15 kg/m\({}^{2}\). In addition, the nodules were modeled as spherical particles with the same diameter, and they were evenly arranged on the smooth ground. According to the properties of the polymetallic nodule, the density and diameter of the particle were set to 2000 kg/m\({}^{3}\) and 0.05 m, respectively. In addition, the dynamic viscosity and density of seawater are 0.00161 kg/m\(\cdot\)s and 1025 kg/m\({}^{3}\), respectively, [28]. The collection duration was set to 30 s.  
The whole computational domain was divided into unstructured grids. The regions near the wall of the hydraulic collector were refined by the prism layer grid to accurately simulate the flow state. The y+ value of 50 was chosen, and the corresponding thickness of the first prism layer grid was 1.5 mm. The layer number and growth rate of the prism grid were 5 and 1.2, respectively. The realizable \(k-\epsilon\) model was selected to simulate turbulence. Considering the low concentration of polymetallic nodules in the conveying channel, the interaction between nodules can be ignored, and particles in the collector were simulated  
Figure 8: Computational domain of the hydraulic collector.  
by the DPM (discrete particle model ). The SIMPLE algorithm with the Eulerian model for bidirectional coupling calculations was used to realize the interaction between the particles and fluid.
## 1 Introduction
This study proposes a new method for deriving the probability distribution of PGA using seismic data recorded from a single event, with the aim of enhancing structural safety assessments. First, artificial earthquakes are generated stochastically, from which a distribution of PGA is subsequently determined. This resulting probability distribution can be utilized to select an appropriate group of artificial earthquakes for the seismic safety assessment of critical infrastructures, particularly in regions where strong seismic records are extremely rare. To simulate ground motions using actual seismic records from a single seismic event, the present study employs a point-source stochastic model characterized by two key source parameters: seismic moment (\(M_{0}\)) and corner frequency (\(f_{c}\)) [20]. Due to their inherent uncertainties, these parameters are treated as random variables, which can result in variations in the predicted PGAs [21; 22]. Their independence from source-to-site distance further justifies this assumption. Probability distributions are fitted to observed values of both parameters, \(f_{c}\) and \(M_{0}\), and copula sampling is applied to account for their negative correlation. The optimal copula function, selected from five copula functions based on the highest log-likelihood, is used to model the dependency between \(f_{c}\) and \(M_{0}\). The Monte Carlo Simulation (MCS) with random sampling of the parameters generates Fourier spectra for artificial earthquakes at various distances, which are then transformed into the time domain to produce their time histories and corresponding PGA distributions. The resulting probability distributions of PGA for these simulated artificial earthquakes at various distances are then compared and validated against recorded earthquake data.
## Materials and Methods
### Mathematical Models and Methods  
As it was mentioned in the Introduction, the continuous-time IMPP stochastic model had been chosen for characterizing the along-the-route placement and occurrence of TSs jointly for the purpose of RET CD in the present study. For a profound treatment of the mathematical theory of Poisson processes, see [39].  
In the chosen stochastic approach, the TS data logs are seen as realizations of an IMPP. The CD method used commonly in conjunction with Poisson processes is the cumulative sum (CUSUM) method. Detailed expositions of such methods can be found in [40; 41]. By assuming the validity of the IMPP model--at least with respect to the considered RETs and considered TSs--for describing and characterizing TS placements and occurrences along routes within and between urban environments, our task narrowed down to adapt a suitable CUSUM method for the purpose, and validate it with realistic TS data.  
It was our intention to adopt and validate a continuous-time variant of the CUSUM method for CD. Furthermore, trade-off is sought between the false alarm rate and the expected detection lag associated with the TS sequences and to provide hints for choosing appropriate thresholds for the change detectors.  
The continuous-variable variant of the CUSUM method for CD in IMPP realizations is derived in this subsection. The working of the RET change detectors implementing this method is demonstrated in Sections 3.1 and 3.2. The change detectors presented therein have been tuned to detect two different RET transitions, namely, Dt to Res and Res to Dt transitions, and are tested with synthetic input sequences.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**TS Type**} & \multirow{2}{*}{**Abbreviation**} & \multirow{2}{*}{**Index**} & **Expected Number** & **Expected Number of** & **Natural** & **Characteristic** \\  & & & **of Occurrences** & **Occurrences per km** & **Logarithm of** & **to** \\  & & & **per km in Dt** & **in Res Areas** & **the Rate-Ratio** & **to** \\ \hline \multirow{4}{*}{**C**} & NS & 1 & 2.00 & 0.35 & 1.74 & Dt \\  & PL & 2 & 1.70 & 0.25 & 1.92 & Dt \\ \cline{1-1}  & GW & 3 & 0.70 & 0.80 & \(-\)0.13 & Res \\ \cline{1-1}  & SL & 4 & 0.20 & 0.40 & \(-\)0.69 & Res \\ \hline Any & & & 4.60 & 1.80 & & Dt \\ \hline \hline \end{tabular}
\end{table}
Table 1: The empirical traffic sign (TS) rates along a random route for the homogeneous marked Poisson processes describing (downtown) Dt and residential (Res) urban road environments.
## 3 Results and Discussion
### Optimization of HS-SPME  
In order to optimize the conditions of VOC analysis by SPME, the effects of extraction temperature, extraction time and the salting out procedure on the efficiency of SPME were investigated. In addition, the efficacy of the SPME-GC system is also dependent on desorption temperature and time. After every three samples, the fiber blank unexposed to the sample was run with optimized parameters to ensure that under the experimental conditions, all analytes were removed before further analysis so as to avoid residual contamination.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 7 Conclusions  
Quantum computing promises to open new frontiers to HPC efforts. However, quantum circuit synthesis in the NISQ era involves significant challenges, stemming from constraints such as restricted qubit connectivity (requiring mapping and routing), rapid decoherence of qubits (requiring short circuits) and vulnerability to gate errors (requiring low gate counts). Exact synthesis techniques for optimized circuits are generally intractable, which necessitates approximate synthesis methods which can balance approximation error with other compilation targets. To address these concerns, we proposed a genetic-algorithm-based approximate quantum synthesis technique which conceptualizes quantum compilation as a multi-objective optimization problem. We leverage the Non-dominated Sorting Genetic Algorithm for direct compilation of unitary matrices to targeted quantum hardware. Our compiler synthesizes circuits which conform to machine specific native basis gate sets and qubit topologies, while attempting to minimize approximation error, circuit depth and 2-qubit CNOT gate counts. Furthermore, to augment the GA, we utilize a fuzzy logic based runtime parameter adaptation scheme. This controller dynamically modulates the algorithm's exploratory and exploitative search behaviors, enabling the GA to better navigate around local minima. Experimental results show promising results for 2 qubits; however, further investigations are required to understand how the method would perform for higher qubit counts.  
Conceptualization, I.I. and E.E.-A.; methodology, I.I., V.J. and E.E.-A.; software, I.I., V.J., S.T., A.N., K.F.E., S.K. and S.O.; validation, I.I., V.J., S.T., A.N., K.F.E., S.K., S.O. and M.C.; formal analysis, I.I. and E.E.-A.; investigation, I.I., V.J., S.T., A.N., K.F.E., S.K., S.O. and M.C.; resources, I.I., V.J., S.T., A.N., K.F.E., S.O., S.K., M.C. and E.E.-A.; data curation, I.I.; writing--original draft preparation, I.I., V.J., S.T., K.F.E., A.N., S.K. and S.O.; writing--review and editing, M.C., K.E.-A., D.K., B.P., M.S. and D.B.; visualization, I.I., S.T., V.J., M.C. and A.N.; supervision, E.E.-A.; project administration, E.E.-A.; funding acquisition, E.E.-A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author due to federal collaboration requirements.  
This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00 OR22725.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
NISQ Noisy Intermediate-Scale Quantum GA Genetic algorithm MOO Multi-objective optimization NSGA Non-dominated Sorting Genetic Algorithm BFGS Broyden-Fletcher-Goldfarb-Shanno FPGA Field Programmable Gate Array
# Correcting Ski Resort Trajectories Extracted from Video
## 2 Perspective Correction  
To obtain more accurate insights into skiers' movements, our current focus is on processing and analyzing their trajectory data. Accurately capturing and interpreting this data is crucial for understanding the dynamics of skiing, such as speed, direction, and turning patterns. We use video footage provided by KFV (Kuratorium fur Verkehrssicherheit, Board for Safety in Traffic), Vienna (Austria). The data were collected for different slopes in the downhill skiing resort Grosseck-Speiereck in Austria using a single camera with a fixed position. We identified all of the trajectories using the software PeTrack (Version 0.10.3) [8] in this study.  
The following configurations and parameters were used (Table 1). While PeTrack performed reliably under most conditions, it exhibited certain limitations in handling motion blur during rapid skier movements and detecting objects in frames with low contrast due to varying lighting conditions. These challenges have been acknowledged and discussed in Section 3.
## 3 Materials and Methods
### Design and Manufacturing
#### 3.1.3 Wireless-Communication Module and Setup  
Wi-Fi communication was developed to provide Internet capabilities to the smart sensor. Various wireless technologies were reviewed, and the most suitable approach was Wi-Fi technology; therefore, we implemented the ESP8266 Wi-Fi module to allow the CPU to communicate with online services. The ESP8266 Wi-Fi board can be powered with a 5 to 24 V direct current (DC), and has a current consumption of 170 mA when transmitting data.  
In general terms, communication handling includes some tasks as part of the configuration and data processing. The Wi-Fi keeps the session open; thus, IP address requests are only made once. The Arduino program prepares data to be sent to the data-logging script, which forwards the received information to Google spreadsheets. During these tests, the Wi-Fi achieved a time of 2 s between each posted lecture. As mentioned before, WEMOS-D1-R1 was programmed with the Arduino IDE. Moreover, the program developed in the Arduino IDE was also required to set the service-set-identifier (SSID) name and its respective password, and to specify the ID of the Google Script to define where to upload the processed data.  
A crucial task during this setup was to obtain a fingerprint for the connection session. This fingerprint is generally static for each host, but there are some hosts, such as Google, that deliver fingerprints depending on location services. After the fingerprint was obtained, the program verified that the certificate matched the Google server identification and started sending the collected data from the analog input pin. For the conducted experiment, an open-source terminal was employed (CygWin64).  
The next step was to modify the data-logging script. The script for logging data into Google spreadsheets was written in a Google Script language based on JavaScript. There were only two things that were configured in this script, the desired spreadsheet ID, and the selected worksheet inside which the data were to be written.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 2 Background
### Different Types of SCC Control Modes  
Based on the control modes for unmanned and autonomous vessels, there are also different modes for the SCC. [PERSON] et al. [5] argued that remote monitoring, remote operation, status investigation, ASC update, and intervention are the most important SCC control modes. During the remote monitoring mode, no action will be taken by the SCC and all ship status indicators are normal within the SCC. The SCC will enter into the ASC update and investigation mode when ship status indicators indicate an abnormality. During the investigation mode, the SCC operator will interact with the onboard system directly in order to obtain more details of the problem. However, during the ASC update mode, the SCC operator will update some aspects of the ASC plan [11]. When the ASC is not able to solve the problem, SCC will enter the remote ship operations mode. Furthermore, the SCC can be under intervention mode when more interactions with the onboard autonomous and navigation systems are required [5; 11].
## 4 Simulation Results and Discussion
Figure 6: Evolution of the MSE (**left**) and error centroid radius (**right**) for different reward strategies and update policy means of all previous rewards (MAR). Figure 7: Evolution of the MSE (**left**) and error centroid radius (**right**) for different reward strategies and update policy only learning with learning rate (OL-LR). The MSE and radius decrease to a minimum, which is typically reached between iterations 5 and 10. The MSE minimum is greater than 260 and is 0 for the radius. The MSE minimum is high because, as explained, the first peak in power output cannot be avoided, it cannot be improved by learning. As expected, the smallest MSE errors correspond to the smallest values of the radius. Another remarkable result is that iteration by iteration learning is not observed when O-P rewards are applied. This is because, as stated, these strategies do not promote exploration and optimal actions are not discovered. As will be shown in Section 4.3, this problem is solved when O-P rewards are combined with P-N rewards. It can also be highlighted how all the P-N rewards converge at approximately the same speed up to the minimum value, but this speed is different for each policy update algorithm. From this point on, there are major differences between the P-N reward strategies. For some policy update algorithms (OR, LF-LR, and QL), these differences increase over time, while for the rest, they decrease. The OR strategy is the one that converges the fastest to the minimum, but from this point, the MSE grows and becomes more unstable. Therefore, it is not recommended in the long term. However, SAR provides a good balance between convergence speed and stability. When it is used, the MSE for the three P-N reward strategies converges to the same value. As expected, OL-LR and SAR produce very similar results because the only difference between them is that in the former, the rewards are multiplied by a constant. As the rewards are higher, the actions are reinforced more and there are fewer jumps between actions. This can be seen in Figures 5 and 7.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 3 Autogenous Teeth as a Bone Graft Material
### Dentin Processing
Alongside this grinder, comes a disposable grinding chamber (single-use) as well as a dentin cleanser (0.5 M NaOH and 30% ethanol (V/V)) which is applied for 5 min and a phosphate buffer saline (PBS) solution with calcium and magnesium with an application time of 1 min, repeating this last step. This device can be used in order to produce a mineralized or partially demineralized graft, which in this last case, a 10% solution of EDTA during 2 min is added [38]. Finally, another system commonly used to produce a demineralized graft is the Tooth Transformer device (TT Tooth Transformer srl. Milan, Italy). This device comes with a tooth grinder and a series of disposable accessories that contacts with the resulting autologous material and liquid responsible for the demineralization. According to [PERSON], this process takes approximately 25 min [61]. Regarding prices, while the VacuaSonic(r) (Cosmobiomedicare, Seoul, Korea) costs around 12365 \(\upxi\), the Smart Dentin Grinder(tm) (Kometa Bio ltd., Holon, Israel) costs around 1277 \(\upxi\) and finally the Tooth Transformer (TT Tooth Transformer srl. Milan, Italy) device has a price of around 2000 \(\upxi\). Due to the potential of the autogenous tooth as a bone substitute, several clinical applications have appeared in the literature besides ARP procedures [9; 12; 35]. One study performed lateral alveolar ridge augmentation comparing the use of autogenous tooth roots versus the use of autogenous bone blocks [62]. In this particular study, after 26 weeks of healing, the implants were placed with no significant difference between groups (\(p>0.05\)) in terms of primary implant stability quotient [62]. The use of autogenous tooth graft was also associated with the treatment of grade II and III function defects by one study, which compared the clinical and radiologic performance of this graft material with the use of freeze-dried bone allograft [63]. The results of this study point out the potential benefit that autogenous tooth can have as a bone graft material, demonstrating a significant reduction in vertical bone depth, horizontal bone depth as well as radiographically bony defect [63]. Another possible application for autogenous tooth is in sinus floor elevation procedures [64; 65]. One particular study compared the use of autogenous tooth versus the bovine-derived xenograft Bio-Oss (Geistlich Pharma AG, Wolhusen, Switzerland) [64]. With a follow up period of 4 months, there was no significant difference between the two groups after a clinical, radiologic and histomorphometry assessment [64]. In another study, [PERSON] et al. performed a micromorphometry and histological evaluation 9 months after sinus bone graft using autogenous tooth [65]. This evaluation concluded that autogenous tooth showed excellent bone healing, proving to be a viable option for this kind of procedure [65].
## 2 Results and Discussion  
### Microbiological and pH Evaluation  
In the present study, the number of LAB cells in probiotic sausages after fermentation and during storage was approximately 8.0 log CFU g\({}^{-1}\) (Table 1). In the control sausages, the number of LAB cells was approximately 6.0 log CFU g\({}^{-1}\), which increased by 1 log level, reaching a value of 7.0 log CFU g\({}^{-1}\) in the 12 th week. According to the FAO/WHO definition, for a probiotic foodstuff, the number of probiotic bacterial cells should be higher than \(10^{6}\) per gram of the product [7]. It is known that raw meat contains some amount of lactic acid bacteria, which can provide a spontaneous fermentation process. However, to obtain a probiotic product, probiotic bacteria should dominate and a high count of these beneficial microorganisms should be achieved. In the previous study, we proved that _Lacticaseibacillus rhamnos_ LOCK900 is able to grow and dominate in meat products [6]. The shredded, non-sterile meat in sausages seems to be a suitable environment for the development of probiotics [8; 9; 10].  
However, technological processes, such as smoking or the addition of spices, may have an adverse effect on the survival of these microorganisms [11]. The pH values of the probiotic sausages were 4.95, 4.88, and 4.70 after production and 6 and 12 weeks of storage, respectively (Table 2), and were lower than those in the control sausages. A decrease in the pH value combined with an increase in the LAB content may inhibit the development of unfavorable microflora because some bacteria do not tolerate the presence of organic acids and pH values below 5.0 [12]. [PERSON] et al. [13] also found a clear effect of the use of _Lacticobacillus casei_ LOCK900 on fermented sausage as the probiotic samples were characterised by higher pH values compared with those of the control samples. The selection of an appropriate starter LAB strain for the technology plays a significant role in the production process of fermented sausages. A relatively low pH, which inhibits the development of unfavorable microorganisms, including pathogenic microorganisms, limits the formation of biogenic amines and improves product safety [14; 15; 16].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kind of Sample** & **After Fermentation** & **Week 6** & **Week 12** \\ \hline CONTROL SAUSAGES & 6.89 AA \(\pm\) 0.31 & 7.45 AA \(\pm\) 0.96 & 7.11 bA \(\pm\) 0.62 \\ \hline PROBIOTIC SAUSAGES & 8.12 aB \(\pm\) 0.67 & 8.43 bB \(\pm\) 0.88 & 8.67 aB \(\pm\) 0.32 \\ \hline \hline \end{tabular} Explanatory notes: Number of LAB: Data are expressed as mean \(\pm\) SD. Means in the same column followed by different uppercase letters represent significant differences (\(\alpha\leq 0.05\)). Means in the same row followed by different lowercase letters represent significant differences (\(\alpha\leq 0.05\)).  
\end{table}
Table 1: The count of LAB of dry fermented pork sausages after fermentation after 6 and 12 weeks of storage (log CFU g\({}^{-1}\)).