esa-sceva/satcom-chunk-collection huggingface dataset:
# Resective Surgery versus Debridement in Stage 2 Medication-Related Osteonecrosis of the Jaw
## 2 Materials and Methods  
The authors performed a retrospective comparative study that conformed to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines of patients diagnosed with MRONJ attended to by our Maxillo-Facial Surgery Department, from January 2014 to March 2019. Patient where selected according to the following inclusion criteria: oncologic patients with confirmed stage 2 MRONJ diagnosis, ongoing or previous therapy with zoledronic acid or denosumab, no MRONJ healing after at least 8 weeks of conservative antibiotic therapy and oral hygiene, at least 12 moths of available follow-up data. Patients were excluded if staged as 0, 1 or 3 according to the AAOMS staging system, underwent radiotherapy treatments in the head and neck area, were MRONJ diagnosed non-oncologic patients, treated with different antiresorptive and/or antiangiogenic drugs than zoledronic acid and denosumab, and presented severe contraindications to local/general anesthesia and surgery.  
The following data were retrieved from each included subject: age, gender, type of primary tumor, type of antiresorptive therapy received, MRONJ location. Collected data were organized using Microsoft Excel, version 14.0.7104.5000 (Microsoft Excel 2010 by Microsoft Corporation, Albuquerque, NM, USA) and anonymized. The patients were then grouped in two: group A patients who underwent surgical resection, group B patients who underwent debridement. Studied cohorts were homogeneous with respect of the received antiresorptive therapy (zoledronate or denosumab), age, and sex. All patients were studied by orthopantomography and CT or cone beam CT of the facial skeleton and mandible at baseline. Patients presenting lesions of the jaws of uncertain nature were studied by PET scan.
###### Abstract  
In this work we report on the spectroscopic properties and the near-infrared to visible upconversion of Er\({}^{3+}\) ions in aluminosilicate glasses manufactured by directionally solidification with the laser floating zone technique. Glasses were manufactured in a controlled oxidizing atmosphere to provide them with high optical transmission in the visible spectral range. Absorption and emission spectra, and lifetimes were assessed in both the visible and the near infrared spectral range. Green upconversion emissions of the \({}^{2}\)H\({}_{11/2}\)\({}^{-4}\)\({}^{1}\)\({}_{15/2}\) and \({}^{4}\)S\({}_{3/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\) transitions at 525 nm and 550 nm attributed to a two-photon process were observed under excitation at 800 nm. Mechanisms responsible for the upconversion luminescence were discussed in terms of excited state absorption and energy transfer upconversion processes. Excitation spectra of the upconverted emission suggest that energy transfer upconversion processes are responsible for the green upconversion luminescence.  
optical properties; erbium; upconversion; aluminosilicate glasses; laser floating zone Accepted: 22 January 2021  
Footnote †: journal: Applied Sciences
# Assessment of GNSS Galileo Contribution to the Modernization of CROPOS's Services
## 2 Materials and Methods
### Mission Planning
Those having good, medium, and bad observation conditions are marked in green, orange, and yellow color, respectively. Figure 1: Sky Plot (Trimble Planning) of the station A (Zagreb, Croatia) for the time window 06:00–18:00 CET (14 July 2020) considering the local horizon and Galileo satellites only. The cyan area shows the open sky where the satellites’ signals come from. Each satellite’s track is marked by the GAL satellite it belongs to (e.g., E33); on each track are marked the positions planned for the full-hour epochs (e.g., 11, 12... 18). The times (epochs) highlighted in Table 1 are indicated in Figure 2, where all DOP values calculated upon Galileo satellites' visibility are displayed. The Galileo constellation consisted of 22 operational satellites in July of 2020. In the time-window 6-18 CET, there were 4-8 visible satellites having PDOP values in the range of 1.67-4.29. The basic idea was to carry out the observations (kinematic and static) in 10 sessions with a different number of visible satellites (4-8) and consequently with different PDOP values (1.67-4.29) to gain insight into the performance of CROPOS's services using GAL-only observations. Out of 10 sessions, 4 sessions were assumed to have good observation conditions (1, 4, 7, and 9), another 4 sessions were assumed to have bad observation conditions (2, 6, 8, and 10), whereas the remaining sessions 3 and 5 had medium observation conditions. Due to the short time foreseen for the session, after sessions 1 and 9, there were no observed static observations.
# Comparative Study to Evaluate Mixing Efficiency of Very Fine Particles
## 5 Numerical Analysis Results
### Mixing Efficiency Analysis of Paddle-Blade Mixer with CGM Scaling  
This section presents the DEM simulation results for the paddle-blade mixer under various CGM scaling factors (CGM = 1, 2, 3, 4, 5, and 7). The analysis focused on changes in the particle distribution over time, the temporal behavior of the LMI, and a comparison of the statistical LMI metrics and computational time during the final second of the simulation.  
Figure 17 shows the time evolution of the LMI. During the early phase of mixing, variations in the rate of increase and stabilization of LMI are evident depending on the CGM level. Nevertheless, the general trends converged along a similar trajectory. Particularly for CGM values of 4 and higher, a rapid improvement in mixing was observed, with the absolute differences in the LMI between the CGM conditions gradually diminishing over time. Figure 18 presents snapshots of the particle distributions at 6, 18, 24, and 30 s for each CGM condition. As the CGM factor increased, the number of particles decreased; however, the overall mixing patterns remained consistent. In the high-resolution case (CGM = 1), the mixing interface appeared smoother and more continuous, with similar spatial distribution trends being observed even at CGM = 7.  
Table 6 summarizes the average, maximum, and minimum LMI values during the final second of mixing (29-30 s), along with the particle count, total simulation time, and percentage error for each CGM case. As the CGM factor increased, the simulation time was significantly reduced, from 186 h for CGM = 1 to only 26 min for CGM = 7. The error rates remained low for CGM = 2 (1.18%) and CGM = 3 (0.64%); even at higher CGM values, the errors remained within 5.5%. These findings emphasize the effectiveness  
Figure 18: Particle distribution snapshots at 6, 18, 24, and 30 s under various CGM factors in the Paddle-Blade Mixer.  
of CGM in improving computational efficiency substantially while maintaining reliable physical accuracy.
## 3 Results and Discussion
### Platform Function Test
\\ \hline  & & **Failure level:** & **Maintenance mode:** \\  & & Critical failure & **Maintenance mode:** \\  & & **Failure type:** & **Spare persistent  
From the analysis in Table 1, it could be seen that by artificially creating faults, such as blockage of the combine harvester header, breakage of the threshing drum drive chain, and battery damage, the platform remotely monitored the combine harvester header speed, threshing drum speed, battery voltage, and other parameters, through the vehicle-mounted terminal. The monitoring data had a sudden change, and the data was displayed as 0. According to the monitoring data of the speed of the header, the speed of the threshing drum and the voltage of the battery, the platform judged that failures, such as blockage of the header, blockage of the threshing drum, chain breakage, and battery damage, were about to occur. Then, based on the monitoring data of the above parameters, the platform judged the type of fault, the content of the fault, and the possible cause of the fault in combination with the actual work situation. Finally, the platform performed the corresponding operation maintenance according to the results of the fault prediction and fault diagnosis, and provided operation maintenance methods, maintenance methods, and fault handling measures to the workers or operators. In summary, the combine harvester remote operation and maintenance platform diagnosed the combine harvester operating state correctly, correctly output the corresponding abnormal state label, and gave specific operation and maintenance information. This further verified the reliability of the platform's functions and helped improve the continuity and stability of the combine harvester's operation.
# Validating and Testing an Agent-Based Model for the Spread of COVID-19 in Ireland
## 2 Materials and Methods
### Model Validation
#### 2.2.3 Comparison to Real Data  
After cross validation, the next step to validate an agent-based model is to compare the output of the model to real data. To do this, we simulate the time between February and December 2020 in Ireland and compare the average model output across 30 runs to the real number of cases per day in Ireland. We start the model from 1 February 2020. There were no cases notified in Ireland until 29 February 2020; however, starting the model prior to the initial cases allows for a model burn-in period. At the start of the model runs, there are three infectious agents: one is asymptomatic, and one is presymptomatic. Two agents are exposed but not yet infectious, and all other agents are susceptible. We match all agent movements and intervention strategies to those that were in place in Ireland during that time period. Initially, schools are open and agents are mixing at regular pre-pandemic levels. In the third week of March, schools are closed. In the fourth week of March, a lockdown is introduced; agents reduce their mixing to approximately 50% of pre-pandemic levels and 70% of agents who would normally be working are either working from home or not working. In April,, agents reduce their mixing levels again to about 17% of pre-pandemic mixing. Lockdowns and other restrictions start to lift by June, and thus agents increase their mixing to 33% of pre-pandemic levels in June. In the model, schools reopen in September, and agents remain at the same community mixing level. In the beginning of October, agents increase their mixing back to approximately 60% of pre-pandemic levels; however, movements are reduced to 33% of pre-pandemic levels in the last week of October, as a new lockdown is introduced in Ireland. At the start of December, the lockdown is lifted, and agents increase their movements to near pre-pandemic levels. Table 1, gives a summary of the dates that interventions were implemented and when agent mixing changed.  
Vaccinations are not included in the model, as vaccinations did not begin until late December 2020. However, we do include contact tracing, as this was an important part of the response to the pandemic and will impact the case numbers.  
The number of new cases per day in Ireland during the summer of 2020 was low, under 100 cases. This can be a problem with the scaling factor of the model. Fewer than 100 cases equates to less than 1 agent. This resulted in a number of runs where COVID-19 would have gone extinct in Ireland. As this did not happen, we included a scenario, where if the number of COVID-19 cases in the model was 0, a new case would be imported into the model. This would represent a case that occurred due to travel.
# Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers
## 4 Results  
To evaluate the performance of the proposed UCGA-based Adaptive Equalizer (UCGA-AE), the measures of the following characteristics are taken: BER against SNR, and MSE vs. iteration number. Afterwards, they are compared with the reference results obtained by means of RLS-based Adaptive Equalizer (RLS-AE). The UCGA initialization is also investigated. To ensure comparison fairness, the same channel conditions are considered for the proposed and reference methods, including the same random generator seed when simulating the channel performance.
## 2 Optical Inspection Technologies in Railways
### Sensor Systems
#### 2.3.6 Pole  
The pole detection serves to give orientation during maintenance. For example, a defective part of the OCS can be georeferenced by the two adjacent poles, even if no GNSS (Global Navigation Satellite System) signal is available. The poles are usually detected from the point clouds using various algorithms [62; 63]. However, there are also measuring systems that have been specially developed for this task. The measuring system from MERMEC consists of two laser distance-measuring systems mounted on the roof that are aligned vertically upwards. If both detection units register an object at the same time, then it is stored as a pole. The system works up to a speed of 320 km/h of the carrier vehicle [36]. The Laser Pole Detection System (LPS) by Fraunhofer IPM is based on the same measuring principle. Its range is up to 4 m at 62,000 measurements per second. It can be used successfully at speeds 5-260 km/h, but at less than 5 km/h the laser switches off for eye protection [37]. The LPS is also available as part of the Contact Wire Inspection System (CIS) (Figure 7).
# Precise Photon Correlation Measurement of a Chaotic Laser
###### Abstract  
The second order photon correlation \(g^{(2)}(\tau)\) of a chaotic optical-feedback semiconductor laser is precisely measured using a Hanbury Brown-Twiss interferometer. The accurate \(g^{(2)}(\tau)\) with non-zero delay time is obtained experimentally from the photon pair time interval distribution through a ninth-order self-convolution correction. The experimental results agree well with the theoretical analysis. The relative error of \(g^{(2)}(\tau)\) is no more than 5%o within 50 ns delay time. The bunching effect and coherence time of the chaotic laser are measured via the precise photon correlation technique. This technique provides a new tool to improve the accuracy of \(g^{(2)}(\tau)\) measurement and boost applications of quantum statistics and correlation.  
Keywords:photon correlation; quantum optics; photon statistics; chaos; optical feedback; semiconductor lasers +
Footnote †: journal: Applied Physics Letters
# A Survey: Network Feature Measurement Based on Machine Learning
## 3 Network Measurement Based on ML Method
### Discussion on Network Measurement Application with ML  
Six facets of network measurement using the ML approach are discussed in the third section. Network measurements can gather information about network operations' traffic and use it to inform all facets of network operations. The ML approach effectively extracts valuable network features, classifies and predicts the desired information we want to know, and manages network features as necessary. In recent years, network measurement has gained popularity as a study area. Especially when network bandwidthis expanding, it offers a smart control approach for future efficient and high-quality network transmission. Currently, throughput, delay, and available bandwidth are all addressed by the ML technique. We are aware that these three can interact with one another in some ways, and Figure 2 shows how they relate to one another. Additionally, a network's utilization rate is extremely high if throughput is virtually equal to bandwidth. The bandwidth problem is, in some ways, simpler to fix than the latency issue.  
Therefore, to aid networks in achieving more efficiency, the majority of scholars have concentrated on gaining more available bandwidth and producing more precise estimates of the available bandwidth. As opposed to the conventional empirical model of traffic measurement, the ML method can convert the detection of abnormal behaviors into pattern recognition when they occur in a network. It then classifies these behaviors by examining the characteristics of network traffic and the data gathered to differentiate between normal and abnormal behaviors. Because of the significant labor time savings, the ML approach has drawn increased attention. The study's findings demonstrate that, despite the possibility that the best suitable machine learning techniques vary depending on the circumstances, the use of ML in network assessment has advanced significantly and quickly. The public will see more and more appropriate general models as software and hardware development continues, in addition to the optimization of various models and the adjusting of datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **Applications** & **Ref** & \
\begin{tabular}{c} **ML** \\ **Algorithms** \\ \end{tabular} &
\begin{tabular}{c} **Performance Analysis** \\ \end{tabular} \\ \hline \multirow{4}{*}{Path delay} & \multirow{2}{*}{Reduce data volume} & \multirow{2}{*}{[51]} & \multirow{2}{*}{RF, LR, SVM} & It shows the importance of data pre-evaluation and feature selection, but the basic algorithm can not provide high accuracy in this case. \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \end{tabular}
\end{table}
Table 3: Network Measurement Application and ML Algorithm Performance Analysis.  
Figure 2: Throughput, bandwidth, and delay.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
## 3 Results and Discussion
### Pressure Distribution Result: Experimental (PPS) vs. CLO3D's Built-in Pressure Map  
To further analyze CLO3D's built-in pressure feature, we compared its pressure distribution map with experimental measurements from the pressure sensor (PPS). The overestimation in CLO3D was particularly evident with the plaited composite knit structures like SJP2 and INTP2. As shown in Table 5, CLO3D consistently displayed a smooth and homogeneous pressure distribution with a color gradient indicating the pressure levels in kPa. However, the PPS pressure maps, in contrast, revealed more realistic, granular representations of the actual pressure distribution, with lower average pressure that aligned better with the physical properties of the fabric and its interaction with the body. Notably, the discrepancies became more pronounced at stretch levels exceeding 15%. While CLO3D showed continuous, smooth gradients with pressure exceeding 13 kPa, the PPS displayed more complex profiles with distinct peaks and troughs, typically in the 4-5 kPa range. This comparison highlights that the actual pressure distribution is far from uniform and is significantly influenced by the fabric characteristics and body contours--factors that CLO3D's algorithms may not fully account for. The overestimation in CLO3D likely stems from limitations in modeling the mechanical interactions between composite knits and the body, especially under higher fabric stretch.  
Figure 8: Comparison of simulated pressure values from the CLO3D-based [PERSON]’s CP model and experimental pressure measurements obtained from PPS and Kikuhime, with and without electrode application, using four different samples: (**a**) SJ2, (**b**) SJP2, (**c**) INT2, and (**d**) INTP2.  
These findings suggest that while CLO3D is helpful for general garment design, it requires cautious interpretation and supplementary validation when applied to medical compression garment engineering. Incorporating [PERSON]'s CP model, which accounts for the digitized stretch stiffness, the fabric thickness, and the body's circumference--factors influenced by a theoretical model (Laplace's law)--could enhance the accuracy of pressure predictions and better reflect the complex interactions between the fabric and body in medical applications.
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
## 5 Experiment  
In this section, we present the numerical results. First, we report the algorithm performance on small instances. Then, we report the comparative results with a state-of-the-art algorithm on larger instances.  
The performance of the proposed ELSH algorithm relies on a set of correlated parameters. To achieve a reasonable tuning of the parameters and a good algorithm performance, we adopt the iterated F-race (IFR) method, which allows an automated parameter configuration [30]. Table 1 presents the final values for each parameter, together with the range of values determined by preliminary experiments. We use the two-satellite 1000-node instances of area distribution as the training set, and the tuning budget is set to be 1000 runs of ELSH.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Description** & **Test Range** & **Value** \\ \hline \(\pi\) & max iteration number & \([50,300]\) & \(150\) \\ \(c_{1}\) & small penalty multiplier & \([0.1,2.5]\) & \(1\) \\ \(c_{2}\) & large penalty multiplier & \([1,25]\) & \(10\) \\ \(\rho_{1}\) & penalty for VTW violation & \([10,50]\) & \(15\) \\ \(\rho_{2}\) & penalty for memory violation & \([10,50]\) & \(25\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for the ELSH algorithm.  
The experiment is coded in C++ and conducted on a Linux cluster system with an AMD Opteron 4184 processor (2.8 GHz and 2 GB RAM) running Ubuntu 12.04. All the results presented in this section is an average of 10 independent runs.