esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Methyl _Tert_-Butyl Ether Removal by Mixed Consortium: Performance and Adaptability
###### Abstract  
Methyl _tert_-butyl ether (MTBE) is widely used as a gasoline additive with toxicity and carcinogenicity, and has caused environmental pollution worldwide. Biodegradation is a promising method for the removal of MTBE from contaminated sites. In this paper, three strains with high adaptability and different degrading characteristics to MTBE were cultured. The kinetic models were established to systematically simulate the biodegradation of MTBE by various strains at different concentrations. Moreover, the removal of MTBE in a synergistic system containing a mixture of three pure strains was studied. The mixed consortium enhanced MTBE removal at high concentrations (30-50 mg/L), and the degradation efficiency was increased by about 20% compared to pure strains at a concentration of 30 mg/L. Further, the mixed consortium degraded MTBE nearly three times faster than any of the individual, indicating that the co-cultures of three pure cultures improved both efficiency and the rate of MTBE biodegradation. In addition, it was found that the mixed consortium effectively removed MTBE in the presence of other gasoline components, and exhibited stronger adaptability, especially at low or high temperatures. This study supports the cultivation of a mixed consortium to remediate MTBE-contaminated environments, either as a single substrate or in environments containing other gasoline components.  
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**Keywords:** biodegradation; co-contamination; methyl _tert_-butyl ether; microbial mixed culture
## 3 Results and Discussions
### Effects of Inlet Structure on Critical Ignition Energy  
The schematic of ignition position in combustor is displayed in Figure 12. In general, the pre-detonation tube is tangentially connected to the outer wall of RDC to provide ignition tangential kernel. Because the mixing quality and total pressure has little change after \(x=200\) mm, the point P (200 mm, 680 mm) close to outer wall is selected as the ignition position from which the tangential kernel is released by pre-detonation tube. To predict the critical ignition energy at point P, Formula (5), which was proposed by [PERSON] [34], is employed, and it is defined as the following where \(E\) represents energy necessary to initiate a detonation, \(\rho_{0}\) represents initial density, \(C_{0}\) represents speed of sound in the combustible mixture, \(k_{i}\) represents coefficients for planar, cylindrical and spherical  
Figure 11: Contours of Reynolds number.  
cases. The static pressure and equivalence ratio at point P are listed in Table 3. Table 4 shows the critical ignition energy at point P.  
\[R_{oc}=\left[E_{i}/\left(k_{i}\cdot\rho_{0}\cdot C_{0}^{2}\right)\right]^{(1/i)} \tag{5}\]  
Unexpectedly, according to the numerical results in Table 4, there is little difference for the pressure of ignition position in the same case. Therefore, the critical ignition energy is mainly dependent on the equivalence ratio at the ignition position, which essentially based on the mixing quality of reactant.  
The equivalence ratio of each structure is all close to 1 in Case 1, resulting in the critical ignition energy is almost at the same accordingly. As the total mass flow rate decreases in Cases 2 and 3, the equivalence ratio of air axial injection (Structures 1 and 2) is far away from optimal mixing value gradually compared to the air radial injection (Structures 3 and 4) which still keeps favorable mixing quality. As a result, the critical ignition energy of air axial injection become higher than air radial injection (i.e., the critical ignition energy of Structure 2 is 1.3 times than Structure 3 in Case 3). Please note that the ignition of Structure 2 is always the highest among all the structures, while Structure 3 is the lowest.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 3 Longitudinal Controller Design
### Fuzzy PID Acceleration Controller Design
#### 3.1.2 Fuzzification  
The steepness of the membership function curve affects the control performance. The higher the steepness, the higher the resolution and the faster the response speed; the lower the steepness, the lower the resolution and the slower the response speed. When the driverless vehicle is in motion, its control response should be faster, so this paper uses the triangle membership function as the membership function in fuzzification [18], the following is the triangular membership function:  
\[f(x)=\begin{cases}0&x\leq a\\ \frac{b-a}{a}&a\leq x\leq b\\ \frac{c-x}{c-b}&b\leq x\leq c\\ 0&x\geq c\end{cases} \tag{11}\]  
In the formula, \(a\leq b\leq c\).  
The range of the speed deviation (\(e\)) and the speed deviation change rate (\(ec\)) in the fuzzy controller are both defined as [\(-\)12, 12], so the domain of the speed deviation (\(e\)) and the speed deviation change rate (\(ec\)) are both [\(-\)12, 12]. The output of the fuzzy controller is \(K_{p}\), \(K_{i}\), and \(K_{d}\), as shown in the following equation:  
\[\begin{array}{l}K_{p}=k_{p}+\Delta k_{p}\\ K_{i}=k_{i}+\Delta k_{i}\\ K_{d}=k_{d}+\Delta k_{d}\end{array} \tag{12}\]  
where \(k_{p}\), \(k_{i}\), and \(k_{d}\) are the PID controller's starting parameters; and \(\Delta k_{p}\), \(\Delta k_{i}\), and \(\Delta k_{d}\) are the fuzzy controller's online adjustment parameters.  
The fuzzy domain of \(\Delta k_{p}\) is defined as [\(-\)0.5, 0.5], and the fuzzy domain of \(\Delta k_{i}\) and \(\Delta k_{d}\) is defined as [\(-\)0.2, 0.2]. Figures 4-6 display the fuzzy controller's input and output variable affiliation functions, respectively.  
Figure 4: Membership function of \(e\) and \(ec\).
## 2 Materials and Methods
### Model Evaluation  
In general, the confusion matrix, overall accuracy of classification, user accuracy, producer accuracy, and kappa coefficient were used to evaluate the classification results. The confusion matrix is mainly used to compare the classification results with the actual measured values [28]. Here, \(r\) is the category; \(X_{ij}\) represents the percentage of category \(i\) judged as category \(j\) by the classifier in the total number of categories \(i\); \(X_{ii}\) is the number of pixels in row \(i\) and column \(i\) in the confusion matrix (the number of correct classifications); \(X_{i+}\) and \(X_{+i}\) are the total number of pixels in row \(i\) and column \(i\), respectively; and \(N\) is the total pixels.  
The overall classification accuracy [29] (OA) is equal to the sum of the correctly classified pixels divided by the total pixels, as shown in Formula (7):  
\[OA=\frac{\sum_{i=1}^{r}X_{ii}}{\sum_{i=1}^{r}\sum_{j=1}^{r}X_{ij}} \tag{7}\]  
User accuracy [30] (UA) indicates the probability that a certain type of sample is correctly classified, as shown in Formula (8):  
\[UA=\frac{X_{ii}}{X_{i+}} \tag{8}\]  
Producer accuracy [31] (PA) represents the probability that a certain type of sample in the classification diagram is correctly classified, as shown in Formula (9):  
\[PA=\frac{X_{ii}}{X_{+i}} \tag{9}\]  
The kappa coefficient [32] can make full use of the information from the confusion matrix. It can be used as a comprehensive index for the evaluation of classification accuracy. Table 1 shows the relationship between classification quality and kappa statistics, and the calculation formula of the kappa coefficient is (10) as follows:  
\[K=\frac{N\sum_{i=1}^{r}X_{ii}-\sum_{i=1}^{r}\left(X_{i+}X_{+i}\right)}{N^{2}- \sum_{i=1}^{r}\left(X_{i+}X_{+i}\right)} \tag{10}\]
## 3 Results
### Quality Evaluation of the CNC Milled Surfaces
#### 3.2.2 Surface Roughness and Stereomicroscopy Analysis of CNC-Processed Maple
Furthermore, to better understand the differences in surface quality when processing with T1 and T2 and also with respect to the reference shaved surface it is useful to examine the microscopic images in Figure 8. These images, which include surfaces processed with the tools T1 and T2, as well as the reference shaved surface, highlight the differences in surface quality resulting from the machining parameters. Figures 9 and 10 provide more insights regarding the influence of the cutting depth and stepover value for each milling tool. In Figure 8, on the shaved surface, the wood anatomy is clearly visible and well preserved: fibers appear aligned and intact, vessels and their lumens are well defined, and medullary rays, including those uniseriate, are sharply delineated. This proved that shaving is an operation that uncovers and reveals wood surface anatomy with minimized surface disturbance caused by processing. This morphology corresponded to the lowest Rk  
Figure 10: Comparative micrographs of maple processed by T1 for 2 cutting depths and 3 stepover values. Magnification 120\(\times\); h represents cutting depth. value (6.16 \(\upmu\)m), confirming the superior smoothness of the reference surface. In contrast, surfaces machined with T1 and T2 showed varying degrees of anatomical degradation. The surface processed with T1 (helical cutting edges) displayed significant fiber tearing, disrupted vessel outlines, and areas where the surface appeared fragmented, indicating an aggressive interaction between the tool and the wood. These characteristics were consistent with the higher roughness measured. Although similar types of damage were present on the surface machined with T2 (straight cutting edges), the degree of fiber disturbance was visibly lower. The surface quality using the T2 milling tool resembled best the quality of the shaved surface, leaving distinct the anatomical features when the smallest stepover value was used (Figure 8). The anatomical cavities and their clarity faded with an increase in the stepover value, due to the occurrence of fuzzy grain or even the detachment of fiber bundles. The visual impression in Figure 8 suggests that T2 induced a less severe disruption, which aligns with its overall more favorable performance at a lower stepover value. Microscopically, it was observed that an increase in the depth of cut from 1 to 3 mm increased the surface roughness of map in the case of T2 and for all values of stepover (Figure 9), which validates the previous comments based on the results in Table 3 and Figure 6. The differences in quality caused by the depth of cut are not clear in the images of the T2-processed surfaces, confirming the previous observations (Figure 10).
## 3 Results  
### Segmentation Results  
#### 3.1.1 Database  
To perform the comparison among the different segmentation algorithms, the ISIC 2017 database has been used [6; 26]. This database provides 2000 images for training and 600 images for testing, with their corresponding ground truth masks. The ISIC 2018 database for the task \"Lesion segmentation\" does not provide the ground truth masks for the test set, that is why ISIC 2017 database has been chosen for the part of segmentation. The ISIC 2019 challenge [6] does not include a \"Lesion segmentation\" task.  
For the segmentation algorithms based on convolutional neural networks, a data augmentation process was applied. The data augmentation step consists of random rotations between \(-30\) and \(30\) degrees; random translations on axes \(\mathrm{x}\) and \(\mathrm{y}\) within \(-10\) and \(10\) interval; random horizontal and vertical reflections; scaling with a random scale factor between \(0.9\) and \(1.1\). Finally, all images were randomly sheared in horizontal and vertical angles, specified between \(0\) and \(45\) degrees. All these operations picked their random values from a continuous uniform distribution.  
After data augmentation, the number of training images was 18,000.
## 9 Stokes Space Polarization and Field Recovery Principle
### Polarization Recovery Techniques in the Direct Detection Stokes Vector Receiver  
As previously mentioned in Section 8, the system AIR can be enhanced by exploiting the polarization diversity feature of light [8]. Nonetheless, it is noteworthy that the optical signal polarization state changes randomly in the course of transmission. This is mainly owed to the dynamic and time-varying birefringence distribution along the length of the optical fiber link. This results in the random fluctuation of the Stokes parameters. Consequently, for effective signal recovery, the variation in the polarization state has to be tracked by the receiver. Moreover, in a typical COH detection system, a 4D (or 2D complex) detection is performed by the receiver in the Jones space. Then, the variation is usually characterized by a \(2\times 2\) Jones matrix. On the other hand, in a 3D Stokes space, a \(3\times 3\) real-valued rotation matrix can be used to represent the polarization rotation [8; 13; 24]. Furthermore, provided that the polarization mode dispersion and polarization-dependent loss (PDL) impacts are negligible, the rotation matrix is usually considered as a frequency-independent unitary matrix in the short-reach systems. There are a number of polarization recovery schemes such as the blind rotation matrix estimation algorithm and the training symbol-assisted rotation matrix estimation that can be employed in the rotation matrix estimation. Mainly, the schemes are contingent on factors such as the Stokes space modulation (SSM) format and the respective signal distribution in the Stokes space. In this subsection, the polarization recovery schemes are categorized based on the related Stokes space modulation formats [13].
# Ten Years of TerraSAR-X Operations
## 4 Dedicated Mission Campaigns
### Modifications Induced by TanDEM-X Science Phase
#### 4.2.1 Pursuit Monostatic Phase  
From 19 September 2014 until 17 March 2015, TSX and TDX were flying in pursuit formation with an along-track separation of about 76 km (10 s). In such a configuration, the two SAR instruments can transmit radar signals in parallel without mutual interferences. For images that were taken by TDX, appropriate time delays and roll angles are applied during commanding to correct for the Earth rotation effects within the 10 s time lag. The parallel operation of both the instruments opens a number of new opportunities, e.g., imaging the same scene within 10 s or enlarging the imaged area based on neighboring or overlapping (either in along-track or across-track direction) data takes.  
Also, the downlink of both satellites may be operated in parallel. By using two receiving antennas, the downlinked data amount could be nearly doubled at NSG. However, for stations that were equipped with one antenna only, data downlink has to be restricted to one satellite, since TSX and TDX are too far apart to be tracked together, but too close to be tracked one after the other. Either the satellites were used in an alternating manner or a given station was fixed to a given satellite.
# _Vaccinium_ Species (Ericaceae): From Chemical Composition to Bio-Functional Activities
## 5 Biological Properties of _Vaccinium_ Species
### Vaccinium and Diabetes  
Several reports indicate a potential role of _Vaccinium_ in the control of diabetes, and it has been used in traditional medicine for centuries to ameliorate its symptoms [101; 102; 103]. Approximately 90% of the diabetic patients have type 2 diabetes that is characterized by peripheral insulin resistance and by a reduction in the number and the activity of pancreatic \(\beta\)-cells [104]. Anthocyanins from _Vaccinium_ have potential in terms of lowering the risk of developing various chronic diseases due to their ability to regulate energy metabolism as well as through their anti-inflammatory and anti-oxidative effects [11]. Furthermore, anthocyanins inhibit the activities of \(\alpha\)-glucosidase and pancreatic \(\alpha\)-amylase, important targets for some antidiabetic drugs [105; 106; 107]. Phenolic compounds affect key pathways of carbohydrate metabolism and hepatic glucose homeostasis including glycolysis, glycogenesis, and gluconeogenesis, which are usually impaired in diabetes.  
In addition, _Vaccinium_ extracts and derivatives protect pancreatic \(\beta\)-cells from glucose-induced oxidative stress, increase insulin secretion, possess glucose-lowering effects, restore glutathione concentration, inhibit DPP-4, enhance insulin response, and attenuate the secretion of glucose-dependent insulinotropic polypeptide and GLP-1 [108; 109; 106; 100]. Blueberry metabolites reduce the expression of inflammatory markers and restore the glycosaminoglycan levels increased by high glucose in in vitro models of diabetic ECs [110]. Moreover, malvidin, a major anthocyanin present in blueberries, decreases reactive oxygen species levels, increases the enzyme activity of catalase and superoxide dismutase, and downregulates NADPH oxidase 4 (NOX4) expression in ECs exposed to high glucose levels [111], indicating a protective role against diabetes-induced oxidative stress. In similar models, this compound also reduces vascular endothelial growth factor (VEGF) up-regulation, ICAM-1 expression, and NF-kB (p65) levels [112]. In addition, malvidin has been shown to be able to restore PI3K and Akt levels, which are reduced by high glucose [113].  
These observations are also confirmed in the retina of diabetic rats, where blueberry anthocyanins reduce oxidative stress, vascular endothelial growth factor (VEGF) and interleukin 18 (IL-1\(\beta\)) expression, and activate the Nrf-related/heme oxygenase 1 (Nrf2/HO-1) signalling pathway [114], suggesting that _Vaccinium_ anthocyanin may be helpful in inhibiting diabetes-induced retinal abnormalities and preventing the development of diabetic retinopathy.
# Experimental Validation of a GNSS Receiver Antenna Absolute Field Calibration System
## 2 Materials and Methods
### Validation Methodology  
In general, two approaches are best suited for validating GNSS receiver antenna calibration results, i.e., PCC patterns. The first approach is to compare calibration results with independent results obtained by a different calibration institution, the so-called \"ring calibration\". With such a validation test, the compatibility of two, or more, calibration institutions can be evaluated. Currently, the most comprehensive global \"ring calibration\" test is the IGS \"RingCalVal\" campaign, which includes eight calibration facilities globally and six different receiver antennas [24]. The second approach is to validate PCC patterns in a field test using short-baselines [11; 29; 40]. The advantage of such an approach is the fact that ground-truth, i.e., reference values of baseline components, is known and atmospheric effects (ionosphere, troposphere), as well as other GNSS error sources (satellite orbit and clock errors, satellite PCV), are effectively eliminated on a short-baseline.  
Thus, in accordance with the goals of this research, i.e., to experimentally test the developed LMMT robotic calibration system, to assess the accuracy of dual-frequency GPS calibration results, and to extensively validate LMMT calibration results in the spatial domain, the following experimental tests and analyses have been performed:  
1. Accuracy analysis at the PCC pattern level by comparison with independent calibration results provided by Geo++ GmbH;
2. Short-baseline validation and analysis in the spatial domain;
3. Validation and analysis at the GNSS/GPS network solution level.  
In the subsequent subsections of this section, each experimental test is elaborated from a methodological perspective. Obtained experimental research results are documented in Section 3.  
To acquire data for validation and analysis purposes, from late May to mid-June of 2023, four calibration campaigns were carried out within which six distinct GNSS receiver antennas were individually calibrated using the developed robotic calibration system. A recapitulation of the basic information of the calibration campaigns is given in Table 1. The six GNSS antennas involved are four reference-station-grade Trimble Zephyr 2 Geodetic antennas (Figure 5a) and two rover-grade antennas--a Leica Geosystems AX1202 GG antenna (Figure 5b) and a Topcon PG-A1 antenna (Figure 5c). No radome was used during calibration with any antenna. A summarized overview of the individually calibrated receiver antennas is given in Table 2.
# Optimal Coordination of PV Active Power Curtailment and EVs Charging among Aggregators
## 2 Mathematical Modelling of PVs and EVs  
### Model of PVs  
The power generation from the PV systems under aggregator \(k\) is modelled using the formulation introduced by the authors in a previous work [14]. The aggregation of the available PV power generation (\(P_{k}^{PV}\)) at time \(t\) is defined by Equation (1) as the summation of the expected power from the individual PV unit (\(p_{k,i}^{PV}\)) connected at household \(i\).  
\[P_{k}^{PV}(t)=\sum_{i\in N_{k}^{PV}}p_{k,i}^{PV}(t),\quad\forall k\in K,\ t=0, \ldots,N_{slots} \tag{1}\]  
where \(N_{k}^{PV}\) is the set of households under aggregator \(k\) with a grid-connected PV system, and \(N_{slots}\) is the number of discrete-time slots by considering a time step \(t_{s}\) and an interval length \(\Delta t=t_{s}/60\) for an evaluation period \(T\), i.e., \(N_{slots}=T/\Delta t\).
## 5 Discussion
### Annotation Challenges and Guideline Effectiveness  
One of the key challenges encountered during annotation was capturing implicit or context-dependent forms of toxicity. Annotators frequently faced difficulties distinguishing between subtle humor, irony, or indirect references and genuine bias. For instance, detecting benevolent sexism and covert racism required extensive contextual understanding. The detailed annotation guidelines, supported by clear theoretical foundations and exemplified instances, considerably facilitated consistent labeling, although achieving high inter-annotator agreement for sexism (\(\kappa=0.55\)) proved challenging due to inherent subtleties. A higher agreement for racism (\(\kappa=0.79\)) was achieved. The work presented in this paper could be further improved by replicating the process with a higher number of annotators, reflecting the qualified opinions of a council of experts in sexism and racism.
In this paper, other practical problems that may be faced using DCC are discussed, such as the estimation of the IPSR, the effect of co-registration of two images on DCC, and resolution degradation after DCC. Among these problems, the most intractable one is the estimation of IPSR.  
Although DCC has been proven to be effective in simulations [28; 30; 31], no flight experiment has ever been reported. To validate the DCC method and demonstrate the above issues, an experiment was conducted using an L-band airborne dual-channel SAR sensor. We used the DCC method and compared the SAR images before and after DCC, and the results of the experiment were quite satisfactory.  
The remainder of this paper is organized as follows. In Section 2, the signal model of the dual channel cancellation is introduced. Combined with simulation, the estimation of IPSR based on cosine similarity is then studied. After that, the effects of co-registration, resolution degradation, and dark straps are analyzed. In Section 3, the airborne experimental results at the L-band are shown to prove the validity and practicality of our method. Finally, we discuss our findings and draw conclusions in Sections 4 and 5.