esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Epidemiology and Diagnosis
### Incidence Associated with Disease  
MRONJ has traditionally been linked to the treatment of osteoporosis and cancers, as the medications used for these conditions are often associated with the development of MRONJ. Besides these, other factors such as diabetes, immunosuppressive conditions, and anemia have been identified as potential risk contributors to MRONJ [48]. However, the relationship between MRONJ and certain conditions like diabetes remains controversial, with some studies reporting no direct connection [49; 50].  
Recently, attention has shifted towards exploring the incidence of spontaneous MRONJ in individuals with fibrous dysplasia (FD) and McCune-Albright syndrome (MAS). A detailed systematic review was undertaken to assess the risk of MRONJ among patients diagnosed with FD and MAS, particularly focusing on those receiving BPs and other antiresorptive treatments [20]. This review analyzed data from a total of 312 patients with FD and FD/MAS and identified 12 reported cases of MRONJ, resulting in an incidence rate of 3.85% [20]. Although the review suggests that the risk of MRONJ in patients with FD or FD/MAS is relatively low, it is noteworthy that this incidence rate is somewhat higher compared to those observed in patients with osteoporosis and other malignancies, underscoring a nuanced risk profile for MRONJ in this specific patient group [19; 20].
# Synchronous Generator Stability Characterization for Gas Power Plants Using Load Rejection Tests
## 3 Experimental Tests
### Measuring Devices and Configuration  
A series of records of the tests were carried out on more than 60 gas power plants of the Spanish power grid. The steady-state and transient measurements were registered using a 4-channel oscilloscope where the machine's frequency (CH1 = f), the AVR output voltage (CH2 = E\({}_{\text{FD}}\)) and the output active and reactive power (CH3 = \(P_{S}\), CH4 = \(Q_{S}\)) were measured. The last two variables were used to fit 10-15% of the rated power condition and to monitor the load rejection. The registers were carried out during 60 s, but different time spread responses were found attending to the different power plants inertias and to the AVR and speed governor settings. The maximum, minimum and time settings for each channel can be also observed in the oscilloscope registers shown in Figures 8-10.  
Figure 8: Plant 1 tests: (**a**) no-load voltage set point changes; (**b**) no-load frequency set point changes; (**c**) load rejection test at \(P_{S}\) = 1.7 MW and \(Q_{S}\) = 1.4 MVAr.
## 5 Experiments and Results
### Virtual Experiments  
To assess the robustness and generalizability of the proposed optimization framework, a series of virtual experiments were conducted. Unlike real-world industrial data, these experiments were based on synthetically generated datasets designed to simulate a controlled multi-objective manufacturing environment with varying complexity. Two synthetic datasets generated with the scikit-learn library were used in the experiments. Both datasets contained 16 input parameters, simulating sensor readings and process variables. The output dimensions varied: the first dataset had two output targets, while the second included five. The primary variable of interest was the number of controllable parameters, which was systematically increased in increments of two (i.e., one, two, four, and eight control parameters). The objective was to observe how increasing the dimensionality of the control space influences the optimization performance across different targets.  
Figure 7(a) (left) shows the reward trends for Target 1 and Target 2 for the two-output dataset. Both targets exhibit a monotonic improvement in reward with increasing control dimensionality. Notably, Target 2 benefits significantly from a richer control space, with reward increasing from approximately 0.45 to 0.9. Target 1, which starts with a relatively higher baseline, shows marginal but consistent improvement. In contrast, the five-output dataset results, shown in Figure 7(b) (right), demonstrate a more complex relationship. Targets 1 and 2 initially improve as the control parameters increase, but their performance eventually plateaus or begins to declines, likely due to overfitting or the introduction of less-informative control dimensions. Target 3 and 4 remain relatively stable across the control dimensionality sweep, indicating insensitivity to added parameters. Target 5, which starts with the lowest reward, shows consistent improvement, suggesting that certain objectives require more complex control representations to be adequately optimized.  
These virtual experiments confirm that increasing the number of control parameters can enhance performance but also reveal diminishing or negative returns for certain objectives beyond a threshold. This highlights the importance of feature selection and dimensionality control in multi-objective RL settings.  
Figure 7: Virtual experiments on synthetic datasets with increasing control parameter dimensions. (**a**) Reward trends for two targets with rising control parameters. (**b**) Reward trends for five targets with rising control parameters.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AUV & Autonomous Underwater Vehicles \\ BER & Bit Error Rate \\ CDF & Cumulative Distribution Function \\ IM/DD & Intensity Modulation/Direct Detection \\ OOK & On-Off Keying \\ PDF & Probability Density Function \\ SISO & Single-Input/Single-Output \\ SNR & Signal-to-Noise Ratio \\ UOWC & Underwater Optical Wireless Communications \\ \end{tabular}
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 3 The Architectural Model
### The Control Plane--Tiered Tunnel Automation
#### 3.2.3 Tier 3--The Announcements of Transitive SFC Routes and Encryption Service Locations  
Tier 3 is responsible for establishing and managing the inner SFC Encryption Links. The route distribution is similar to Tier 2 and is also a process consisting of two parts. The announcements of the location of the encrypting services and the SFC announcements of the next hops in the SFC. These routes are announced over a separate Tier 3 BGP peering interface. The Tier 3 routes must be clearly distinguished from Tier 2 routes. Hence, a new BGP address family (AFI and SAFI) is also suggested to be defined for this Tier. The components of Tier 3 are therefore similar in nature to those of Tier 2, but they serve a distinct purpose and therefore must refer to a new address family.  
The SFC header for Tier 3 is in this context not SFC subsystems such as nested SFCs where the inner SFCs belongs to a sub-chain of SFCs in RFC 7665 [15]). The Tier 3 SFCs are transitive, meaning that they always coexist with the upper SFC layer and contribute to routing decisions for  
Figure 8: A full mesh of Transport Link tunnels.  
both Tiers 2 and 3. To differentiate SFC subsystems from inner SFCs, the Tier 2 SFC header must also contain information about the type of the next SFC header. When using NSH, this means that both inner and outer SFCs must exist in one NSH header. Hence, it is a header extension and not a next protocol header.  
Finally, an important prerequisite is that the encryption services are already running before the announcement of the Tiers 2 and 3 SFCs. Hence, Tier 3 SFC announcements need to be announced before Tier 2 SFC announcements. This also reflects the fact that information about Tier 3 SFCs must exist in Tier 2 SFCs, which is explained in the Protocol and Interface Section 5.
## 3 Applications of Single Molecule Measurement: Disease Diagnosis, Monitoring  
As examples of single-molecule measurement applications (Table 2), we focus on the diagnosis of cancer diseases [63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78], viral infections [55; 64; 89; 90; 91; 92; 93; 94; 95], drug detection and screening [96; 97; 98; 99; 100; 101], dementia, brain and nervous system diagnoses, and monitoring of other dangerous substance epidemics [102; 103; 104; 105; 106; 107; 108; 109; 110; 111]. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Detection** & **Target Marker** & **Detection Method** & **Detection Device** & **Sample Source** & **Reference** \\
**Purpose** & & & & & & \\ \hline \multirow{4}{*}{Cancer (pancreatic cancer)} & DNA: Tumor & & & & \\  & suppressor genes & & & & \\  & (CDKN2A/p16 and SMAD4/DPC4) & & & & & \\ \hline \multirow{4}{*}{Cancer (cervical cancer)} & DNA: human & & & & \\  & papillomavirus (HPV), & & & & \\  & which is integrated & & & & \\  & into the human & & & & \\  & genome, & & & & \\ \hline \multirow{4}{*}{Cancer (prostate cancer)} & DNA: tumor-specific & & & & \\  & genomic structural & & & & \\  & variants & & & & \\ \hline \multirow{4}{*}{Cancer (cervical cancer)} & DNA: human & & & & \\  & papillomavirus (HPV) & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \end{tabular}
\end{table} 
Table 2: Biological/medical application by single-molecule electrical measurement using nanodevices. Figure 4: Cancer diagnosis by single-molecule electrical detection of cancer marker molecules and counting of miRNA molecules. Tumor-specific marker molecules in liquid biopsy samples, such as blood, urine, and saliva obtained from patients, are measured. After these samples are pretreated by purification, amplification, and chemical treatment, single-molecule measurements are performed using nanodevices to distinguish between patients and healthy individuals. There are several reports of the detection of genes and miRNAs by sequencing, and/or detection using probe molecules that selectively bind to the target.
# Sand Removal Mechanism of a High-Speed Roller Bit with Helical Sealing
## 4 Results
### Optimization of Structural Parameters of the Helical Sealing
#### 4.2.1 Numerical Simulation  
According to previous research, the detention time of rock debris and the sand removal performance are respectively proportional and inversely proportional to the pressure change rate. The above are thus selected as criteria by which to judge the performance of sand removal.  
1. Effect of the pitch  
Considering the limited height of the sealing chamber, the pitch of the helical sealing ring is inversely proportional to the ring number. However, the ring number cannot be great. Otherwise, the small pitch can affect the efficiency of sand removal and the strength and working life of the sealing. Additionally, the ring number cannot be small, as the pumping will be ineffective. Three rings are thus selected in this paper. Pitches of 1.6 mm, 2.0 mm, and 2.4 mm are selected with the ring number and other structural parameters unchanged.  
As shown in the numerical simulation results of Table 1, the pressure change rates at helical structures b, c, and d are extracted and their average values obtained. With an increasing screw pitch, the pressure change rate gradually decreases, the pressure drop rate increases, the detention time of rock debris decreases, and the sand removal performance worsens. For a pitch of 2.4 mm, the pressure change rate is lowest, the detention time of rock debris is shortest, and the sand removal performance is best.  
Figure 14: Drilling bit after the test.  
With other structural parameters unchanged, sealing gaps of 0.5 mm, 0.8 mm, and 1.1 mm are selected to study the gap effect on the sand removal performance. As shown in the numerical simulation results of Table 1, the pressure change rate gradually increases, the pressure drop rate decreases, the duration of movement of rock debris increases, and the sand removal performance decreases with an increasing sealing gap. The sand removal performance is best for a sealing gap of 0.5 mm.  
Effect of the inclination of the helical surface  
With other structural parameters unchanged, the sand removal performance is observed for inclinations of the helical surface of \(23^{\circ}\), \(27^{\circ}\), and \(30^{\circ}\). The numerical simulation results presented in Table 1 show that the pressure first increases and then decreases with increasing inclination. The pressure change rate is lowest for an angle of \(23^{\circ}\). With increasing inclination, the movement time of rock debris first decreases and then increases. At inclinations of \(23^{\circ}\) and \(27^{\circ}\), the differences in the pressure change rate and particle retention time can be neglected. However, experimental data show that the sand removal performance is best for an inclination of \(23^{\circ}\), which is selected in this paper.  
The average pressure change rate and the detention time of rock debris change most with a variation in the sealing gap, among the three structural parameters, which indicates that the gap has the greatest effect on the sand removal performance.
## 3 Fault Diagnosis: Machine Learning-based Adaptive Neuro High-Order Variable Structure Observer
### Fault Tolerant Control Using an Adaptive Modern Fuzzy Backstepping Variable Structure Controller
\right]\) are the triangular and linguistic variables, which are defined as negative high (NeH), negative medium (NeM), negative low (NeL), zero (Ze), positive low (PoL), positive medium (PoM) and positive high (PoH). The fuzzy membership functions for the system estimation of the differential of the error \(\left(\dot{e}\right)\) in the interval \(\left[ \begin{array}{cc}-1.8,&1.8\\ \end{array} \right]\) are the triangular and linguistic variables, which are defined as NeH, NeM, NeL, Ze, PoL, PoM and PoH. In addition, the fuzzy linguistic variables for the fuzzy output \(\left(U_{PD-fuzzy}\right)\) in the interval, \(\left[ \begin{array}{cc}-18,&18\\ \end{array} \right]\) are the triangular and fuzzy sets, which are defined as NeH, NeM, NeL, Ze, PoL, PoM and PoH. Table 2 illustrates the PD fuzzy rule table used to improve the performance of the fuzzy BVSC (FBVSC). Therefore, the FBVSC is represented by the following equation:  
\[\left\{\begin{array}{c}U_{FBVSC}=U_{VSC}+U_{B}+U_{PID-fuzzy}\\ U_{VSC}=\dot{a}\left(\hat{X}_{1}\right)\times\left[\mu_{1}sgn\left(X_{1}-\hat{X }_{2}\right)+\mu_{2}sgn\left(X_{2}-\hat{X}_{1}\right)\right]+\dot{a}\left(\hat {X}_{1}\right)\times\\ \left[\hat{\beta}\left(\hat{X}_{1}\quad\hat{X}_{1}\quad\hat{X}_{1}\right)+ \hat{\gamma}\left(\hat{X}_{1}\right)+\mu_{1}\frac{\dot{a}}{dt}\left(\left(X_{1} -\hat{X}_{1}\right)+\left(X_{2}-\hat{X}_{1}\right)\right)\right]\times\dot{a} \left(\hat{X}_{1}\right)+\dot{\alpha}^{-1}\left(\hat{X}_{1}\right)\dot{\Theta} _{ANHYSO}\\ U_{B}=\dot{\alpha}^{-1}\left(\hat{X}_{1}\right)\times\left[\mu_{1}\left(X_{1}- \hat{X}_{1}\right)+\mu_{2}\left(X_{2}-\hat{X}_{1}\right)\right]+\left[\dot{a} \left(\hat{X}_{1}\right)+\beta\left( \begin{array}{cc}\hat{X}_{1}&\hat{X}_{2 }\\ \end{array} \right)+\dot{\gamma}\left(\hat{X}_{1}\right)\right]\\ U_{F}=U_{PID-fuzzy}=U_{PD-fuzzy}+U_{PI-fuzzy}=U_{PD-fuzzy}+\int U_{PD-fuzzy}\] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline \multirow{4}{*}{error \((e)\)} & NeH & NeM & NeL & Ze & PoH & PoM & PoL \\  & PoH & PoH & PoH & PoH & PoM & PoL & Ze \\  & NeM & PoH & PoH & PoH & PoM & PoL & Ze & NeL \\  & NeL & PoH & PoH & PoM & PoL & Ze & NeL & NeM \\  & Ze & PoH & PoM & PoL & [PERSON] & NeL & NeM & NeH \\  & PoH & PoM & PoL & [PERSON] & NeL & NeM & NeH \\  & PoM & PoL & [PERSON] & NeL & NeM & NeH & NeH \\  & PoM & Ze & NeL & NeM & NeH & NeH & NeH \\  & PoL & Ze & NeL & NeM & NeH & NeH & NeH \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The proportional and derivative (PD) fuzzy rule table for the fuzzy backstepping variable structure controller (FBVSC). After improving the performance of the BVSC based on the FBVSC technique, to reduce the effects of faults and chattering, an SVM-based ANHVSO is used in parallel with the FBVSC to design a modern FBVSC (MFBVSC) technique. In this scenario, the proposed observer is used to estimate and identify a fault and its location.
## 4 Experimental Results and Discussion
### SFO Estimation Accuracy and Compensation Range  
Following the convergence analysis, we investigate the estimation accuracy of the proposed scheme under a wide SFO range. Figure 5 shows the estimated SFO value and offset through a 1 m tap water channel in the IMDD-OFDM system. It can be seen that when the SFO varies from \(-1000\) ppm to \(+1000\) ppm, the estimated SFO values are very close to the real SFO, and the estimated offset is within \(\pm 3\) ppm. In particular, when the SFO is less than \(\pm 400\) ppm, an estimation deviation within \(\pm 2\) ppm can be achieved. The results demonstrate that the proposed SFO estimation technique is capable of achieving high-accuracy SFO estimation in an asynchronous OFDM-based UWOC system.  
Figure 4: (**a**) The estimated offset at different pilot intervals; (**b**) The convergence performance versus frame number when the pilot interval is 34.  
Figure 5: Estimated SFO value and estimation offset versus real SFO.  
Figure 6 shows the EVM performance of a system over different SFO after 1 m underwater transmission, where the 16-QAM constellations are also inserted in the presence of \(\pm\)50 ppm and \(\pm\)600 ppm SFO. It can be seen that the EVM performance without SFO compensation deteriorates rapidly with the increase in SFO. As shown in Figure 6a-d, the phase rotations can be observed in 16-QAM constellations without SFO compensation, and the constellations are hard to distinguish. In addition, the directions of phase rotation for the negative and positive SFO value are opposite and the rotation angle is proportional to the value of SFO. After using the proposed SFO compensation scheme, the system performance can be significantly improved. From Figure 6e-h, the constellations become much clearer because the phase rotations are corrected with SFO compensation. Compared with the SFO-free case, even if the SFO is up to \(\pm\)1000 ppm, the EVM penalties after SFO compensation are less than 3 dB. Moreover, when the SFO is less than \(\pm\)300 ppm, the EVM with compensation is very close to the case without SFO and the EVM penalty is below 1 dB.
## Materials and Methods
### Research Methods  
#### 2.3.1 ET Calculation Based on PT-JPL Model  
In order to better simulate vegetation ET values, the PT-JPL [18] was developed as a GIS-based model by integrating remote sensing technology into vegetation dynamics to simulate ET values from the stand scale to the regional scale. The PT-JPL model divides ET into three components: _Et_, _Es_, and _Ei_. The following are the equations and parameters involved in the operation of this model:  
\[ET=Et+Es+Ei \tag{7}\]  
\[Et=(1-f_{wet})f_{g}f_{t}f_{m}\alpha\frac{\Delta}{\Delta+\gamma}R_{nc} \tag{8}\]  
\[Es=(1-f_{wet}+f_{sm})(1-f_{wet})\alpha\frac{\Delta}{\Delta+\gamma}(R_{ns}-G) \tag{9}\]  
\[Ei=f_{wet}\alpha\frac{\Delta}{\Delta+\gamma}R_{nc} \tag{10}\]  
In these equations, \(\alpha\) is the PT coefficient with a value of 1.26 in our research; \(\Delta\) is the slope of the VPD (kPa.\({}^{\circ}\)C\({}^{-1}\)); \(\gamma\) is the psychrometric constant with the value of 0.066 (kPa.\({}^{\circ}\)C\({}^{-1}\)) [27; 29]; _Rnc_ is the net radiation to the canopy (W\(\cdot\)m\({}^{-2}\)); _Rns_ is the net radiation to the soil (W\(\cdot\)m\({}^{-2}\)), _kRn_ is the extinction coefficient with a value of 0.6 in our research. In the above equation, the other elements of the parameters and data used in the calculation equation are as follows:  
\[f_{wet}=RH^{4} \tag{11}\]  
\[f_{c}=f_{IPAR} \tag{12}\]  
\[f_{g}=\frac{f_{APAR}}{f_{IPAR}} \tag{13}\]  
\[f_{t}=exp\left(-\left(\frac{T-T_{opt}}{T_{opt}^{2}}\right)^{2}\right) \tag{14}\]  
\[f_{m}=\frac{f_{APAR}+1}{f_{APARmax+1}} \tag{15}\]  
\[f_{sm}=RH^{VPD/\beta} \tag{16}\]  
\[f_{APAR}=b_{1}\times(1-\exp(-k_{1}\times LAI)) \tag{17}\]  
\[f_{IPAR}=b_{2}\times(1-\exp(-k_{2}\times LAI)) \tag{18}\]\[RH=0.263*shum*press*\left(exp\frac{17.67\left(T-T_{0}\right)}{T-29.65}\right)^{-1} \tag{19}\]  
\[G=R_{n}[\Gamma_{c}+(1-M)(\Gamma_{s}-\Gamma_{c})] \tag{20}\]  
In these equations, \(f_{I\!PAR}\) is the proportion of PAR entrapped by the vegetation canopy. \(f_{APAR}\) is the proportion of \({}_{PAR}\) (photosynthetically active radiation) engrossed by the vegetation canopy [30]. During the calculation of \(f_{APAR}\) and \(f_{I\!PAR}\), \(b_{1}=0.95\) and \(b_{2}=0.9355\)[19]. \(T_{opt}\) is the optimum temperature for plant growth; \(\beta\) is an index to indicate the sensitivity of soil moisture constraints to VPD in the range of 0-1; shum is the specific humidity, kg/kg; _press_ is the near-surface pressure, Pa; \(T\) is the temperature, K; and \(T_{0}\) is 273.16 K. \(G\) is the soil heat flux; \(Rn\) is the net radiation in the unit W\(\cdot\)m\({}^{-2}\); and \(\Gamma c\) and \(\Gamma s\) are the parameters of the better vegetation coverage area and the better bare soil area with values of 0.05 and 0.325, respectively. \(M\) is the monthly vegetation coverage.  
Different optimization and adjustment are needed in different studies. In our research, the parameters we use refer to [PERSON] et al. [11] and [PERSON] et al. [29], and then, after our adjustment, they are finally put into use. The specific parameter values used are shown in Table 2.
# Valorization of Carrot Pomace: UVC Induced Accumulation of Antioxidant Phenolic Compounds
###### Abstract  
Carrot pomace is the main waste residue obtained during carrot juice extraction. Plant tissues respond to abiotic stresses (i.e., wounding stress and ultraviolet C (UVC) radiation) by accumulating bioactive compounds. Due to the mechanical damage occurring during juice extraction, carrot pomace undergoes extreme wounding stress. In this study, the effects of UVC light (11.8 W m\({}^{-2}\), 0-120 min) and storage time (48 h, 25 \({}^{+}\)C) on the accumulation of phenolics compounds and the antioxidant activity (AOX) of carrot pomace were evaluated. Carrot pomace that was non-treated with UVC (control) showed a 709.5% increase in total phenolics at 48 h. A high correlation of AOX values against total phenolics (\(R^{2}\) = 0.87) was observed, indicating that phenolics were the main contributors to the AOX of the tissue. After UVC treatment, the pomace that was radiated for 120 min with UVC showed an increase (40.4%) in chlorogenic acid (CHA) content. At 24 h, protocatechuic acid and 3,5-dicafefloylquinic acid, which were not detected before storage, showed accumulation by 166.5 mg/kg and 169.4 mg/kg, respectively, in UVC treated pomace. Chlorogenic acid showed the highest increase (143.6%) at 48 h in the control. Valorization of carrot pomace was achieved by increasing its concentration of antioxidant phenolics through UVC radiation.  
carrot waste; valorization; by-products; health-promoting compounds; chlorogenic acid; ultraviolet C radiation; antioxidant capacity; circular economy; sustainable food systems 2021 12 October 2021 24 October 2021 24 October 2021 29 November  
damage occurring during juice extraction, carrot pomace is under extreme wounding stress. Therefore, it is likely that storing the tissue under the appropriate conditions would result in the accumulation of secondary metabolites. Previous reports have demonstrated that wounding stress application in carrots, either applied alone or in combination with UV radiation, results in the biosynthesis of antioxidant phenolic compounds [8; 9]. The main phenolic compound that is accumulated is the chlorogenic acid (CHA), which has been related to the prevention and treatment of the metabolic syndrome [10], colon cancer [11; 12], and the improvement in cognitive abilities and neural development [13].  
Carrot processing waste is around 175,000 tons annually in the United States [14]. Previous reports on the valorization of carrot waste have been mainly focused on extracting nutraceuticals such as carotenoids [14] and dietary fiber [15]. In the present study, the effects of UVC light (11.8 W m\({}^{-2}\), 0, 30, 60, and 120 min) and storage time (48 h, 25 \({}^{\circ}\)C) on the accumulation of phenolics compounds and antioxidant activity (AOX) of carrot pomace were evaluated as tools to valorize the waste residue prior to its further use as a raw material for the extraction of nutraceuticals or for its conversion to a food ingredient.