esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Spectrum Reconstruction Model for Non-Uniform Sampling Signals
### Simulation Analysis of High-Order Error Term \(\delta^{M}\)  
The selected simulation signals are \(s_{1}(t)=\sin(2000\pi t),0\leq t<0.1\) for the sine wave signal and \(s_{2}(t)=\cos[2000\pi(t-0.05)^{2}],0\leq t<0.1\) for the linear frequency modulation (LFM) signal. The time-domain waveform plots of the non-uniformly sampled signals \(s_{1}(t)\) and \(s_{2}(t)\), represented as \(s_{1}(t_{i}^{(1)})\) and \(s_{2}(t_{i}^{(2)})\), respectively, are shown in Figure 1a,b, where \(i=1,2,\cdots,N\). The non-uniform sampling points \(t_{i}^{(1)}\) and \(t_{i}^{(2)}\) are randomly obtained within the range of \([0,0.1)\). The frequency spectra of \(s_{1}(t)\) and \(s_{2}(t)\) are primarily centered at the interval of \([-1000,1000]\). To improve the accuracy of the computations, the actual spectral bandwidth used for the calculations should be slightly wider than the theoretical spectral bandwidth of the signal [23]. We set the oversampling factor \(a=1.2\), which means that the actual computational spectral range of both signals is set to \([-1200,1200]\)Consequently, we have \(M=1200/\Delta f=120\), and choose the number of non-uniform sampling points for the two signals as \(N=\left\lfloor 1.2\times(2M+1)\right\rfloor=289\).  
Since \(\tilde{n}=\delta^{M}\), we can solve it using (2). Let \(S_{1}\) and \(S_{2}\) be the discrete frequency spectrum vectors of \(s_{1}(t)\) and \(s_{2}(t)\), and they have the same meaning as the variable \(S\) in Equation (2). The values of \(S_{1}\) and \(S_{2}\) are obtained by performing the fast Fourier transform (FFT) operation on the uniformly sampled signals of \(s_{1}(t)\) and \(s_{2}(t)\), which are sampled at a rate ten times higher than the Nyquist sampling rate [24]. The \(A\) is generated based on the positions of the non-uniform sampling points. The waveform comparison between the non-uniformly sampled signals and obtained by multiplying \(A\) with the discrete frequency spectrum vectors are shown in Figure 1c,d. By subtracting the amplitudes of the two waveforms being compared, we can obtain \(\delta^{M}\) of the two non-uniformly sampled signals, which are shown in Figure 1e,f, respectively. The simulation results indicate that compared to \(s_{1}(t_{i}^{(1)})\) and \(s_{2}(t_{i}^{(2)})\), \(\delta^{M}\) is significantly smaller. The ratio of \(\left\|\delta^{M}\right\|\left\|\left\|s\right\|\right\|\) in \(s_{1}(t_{i}^{(1)})\) and \(s_{2}(t_{i}^{(2)})\) is found to be \(5.176\times 10^{-14}\) and \(0.003\), respectively, satisfying the condition of \(\left\|\delta^{M}\right\|\ll\left\|s\right\|\). This also demonstrates that estimating the discrete frequency spectrum of non-uniformly sampled signals using (2) is feasible, as \(\delta^{M}\) is significantly smaller compared to \(s\).
# Continual Learning for Table Detection in Document Images
## 4 Evaluations
### Evaluation Metrics  
Being a sub-class of object detection, our task is evaluated with same performance criteria. The followings are the definition of the common evaluation metrics:  
* **Precision** The number of true positives divided by the total number of positive predictions is known as precision. This is a metric for determining the accuracy of a model. The precision is calculated through Equation (2): \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (2)
* **Recall** To indicate the rate of missed positive predictions, a metric named Recall (or Sensitivity) is commonly used. Recall could be obtained through dividing correct positive predictions (TP) by all positive predictions (TP + FP). Equation (3) shows the mathematical definition: \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (3)
* **Precision-Recall curve** For all possible thresholds, the precision-recall (PR) curve plots precision versus recall. A good object detector has a high recall rate as well as a high precision rate.
* **Intersection Over Union (IOU)** As written in Equation (4), the overlap of a predicted bounding box versus the correct one for an object is measured by the intersection over union of the two boxes: \[\text{IoU(A,B)}=\frac{\text{The Overlapping area}}{\text{The Union area}}=\frac{|A\cap B|}{|A\cup B|}\] (4)
* **Mean Average Precision (mAP)** The mean average precision (mAP) is a widely used parameter for evaluating object detection models. It is the area value under the precision-recall curve for each class, and the mAP is computed by averaging all average precision for all classes. Equation (5) formulates the metric: \[\text{mAP}=\frac{1}{N}\sum_{r=1}^{N}AP_{r}\] (5) where \(AP_{r}\) is the average precision \(r\).
## 3 Results and Discussion
### Synthesis and Characterization
According to the expectations, in the \({}^{1}\)H NMR spectrum, the protons in the cyclohexane ring ofmethoxy moiety appearing at equatorial and axial positions were present at various chemical shifts confirming the presence of two populations of signals originating from two different population of hydrogens. Such a phenomenon was also noted in the \({}^{1}\)H-\({}^{13}\)C HSQC NMR spectrum (Figure 2), where the hydrogen signals at 1.77, 1.23 ppm (c position of the cyclohexane ring) were correlated with the same carbon atom at 24.4 ppm. Similarly, pairs of hydrogens appearing at 1.77, 0.99 ppm (d position of cyclohexane ring) and at 2.57, 1.29 ppm (f position) were correlated with carbon atoms at 35.0 ppm and 41.0 ppm, respectively. In the \({}^{1}\)H-\({}^{13}\)C HSQC NMR spectrum very close proton signals at 1.07 ppm and 1.06 ppm present at two different cyclohexane ring substituents were also distinguished, which is also in agreement with the data lately reported by [PERSON] et al. [52]. The \({}^{1}\)H-\({}^{1}\)H COSY NMR spectrum of **2** revealed correlations between hydrogens within the isopropyl group and c and d vicinal hydrogens. Further, the correlation of signals at 1.60, 1.06 ppm proved proximity of e and g hydrogens (Figure 3). In the \({}^{1}\)H-\({}^{13}\)C HMBC NMR spectrum of the obtained conjugate, a few long-range heteronuclear correlations were observed (Figure 4), which allowed us to assign hydrogen signals at 1.07 and 1.06 ppm to the methyl group within the isopropyl substituent and 5-methyl group, respectively. Figure 1: Signals for hydrogen and carbon atoms of **2** according to NMR spectroscopy and correlations observed in two-dimensional COSY, HSQC, and HMBC experiments; COSY—Correlation Spectroscopy, HSQC—Heteronuclear Single Quantum Correlation, HMBC—Heteronuclear Multiple Bond Correlation. Figure 2: Fragment of \({}^{1}\)H–\({}^{13}\)C HSQC NMR spectrum of **2** in pyridine-\(d_{5}\).
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using Statistical Package for the Social Sciences (SPSS, v 24, IBM, Armonk, NY, USA). An intraclass correlation coefficient (ICC) was used to compute inter- and intraobserver agreement. It has been suggested that ICC values less than 0.5 are indicative of poor reliability, values between 0.5 and 0.75 indicate moderate reliability, values between 0.75 and 0.9 indicate good reliability, and values greater than 0.90 indicate excellent reliability [20]. In order to assess the diagnostic accuracy of imaging modalities, the area under the receiver operating characteristic curves and 95% confidence intervals were calculated. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. For each imaging technique, identifying the endodontic complication equated to score 1 and 2, and not identifying the endodontic failure equated to score 4 and 5. Cases with score 3 were not considered in the analysis. The level of significance was set at \(\alpha\) = 0.05.
# Dual-Circularly Polarized 60 GHz Beam-Steerable Antenna Array with 8 \(\times\) 8 Butler Matrix
###### Abstract  
A beam-steerable dual-circularly polarized 60 GHz antenna array is proposed. A \(1\times 4\) dual-fed stacked patch antenna array is integrated with an \(8\times 8\) Butler matrix. By utilizing the \(8\times 8\) Butler matrix, the proposed antenna array generates dual-circular polarization with beam-steering capability. The proposed antenna array system demonstrates good reflection coefficients in the frequency band ranging from 55.3 GHz to 64.9 GHz and has a mutual coupling of less than \(-10\) dB over the frequency range of 57.5 GHz-63.2 GHz. At 60 GHz, the maximum gains and beam-steering angles for input ports 2, 4, 5, and 7 are 9.39 dBi at \(-38^{\circ}\), 10.67 dBi at \(-11^{\circ}\), 10.63 dBi at \(+11^{\circ}\), and 9.38 dBi at \(+39^{\circ}\), respectively. It is also demonstrated that the dual-polarization is well formed by switching the excitation ports. The right-handed circular polarization (RHCP) is formed when four ports from port 1 to port 4 are excited and left-handed circular polarization (LHCP) is formed when four ports from port 5 to port 8 are excited. The proposed antenna array system could be a good candidate for millimeter-wave 5G applications that require wide beam coverage and polarization diversity.  
beam-steering; dual-polarization; circular polarization; antenna array; Butler matrix; right-handed circular polarization (RHCP); left-handed circular polarization (LHCP); 60 GHz; 5G +
Footnote †: journal: Applied Sciences
# Tribological Analysis of PETG Fire-Retardant Samples Manufactured by FFF
###### Abstract  
Additive manufacturing via Fused Filament Fabrication (FFF) has enabled the development of functional components with customized mechanical properties. In this context, the use of polymers with flame-retardant additives offers an ideal solution for sectors such as aerospace, where fire resistance is a top priority. However, the tribological properties of these materials have not yet been sufficiently studied, despite their relevance in applications subjected to friction or wear. This study analyzes the tribological behavior of parts manufactured using PETG (Polyethylene Terephthalate Glycol) and flame-retardant PETG (PETG FP) through dry condition Ball-on-Disk tests. The influence of manufacturing parameters such as extrusion temperature and layer height on the coefficient of friction (CoF), surface wear, and roughness is investigated. The results show that PETG FP exhibits an increased CoF compared to conventional PETG, although it demonstrates more stable behavior at elevated temperatures. Statistical analysis via ANOVA reveals that material type and layer height significantly affect tribological properties, while temperature plays a secondary role. This study provides key insights for the selection of polymeric materials in environments with critical functional demands.  
Keywords:fire-retardant polymer; tribology; PETG; Fused Filament Fabrication (FFF); additive manufacturing _Article_
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Protein Motif/Sequence Homology Search-Mediated Identification of mTOR Signaling Components  
5.1 Mammalian Rictor Was Cloned and Sequenced Based on a Sequence Homology to Yeast AVO3 Yeast Rictor (AVO3) was discovered by 3\(\times\) HA-mTOR immunoprecipitations as described [29]. The mammalian homolog of AVO3 was searched in a mammalian genome database. As a result, mammalian _Rictor_ gene was identified and firstly cloned from mouse C2C12 cDNA and verified by sequencing [90]. By raising a specific antibody able to detect this molecule, binding of Rictor to mTOR and G\(\beta\)L was confirmed by coimmunoprecipitation assays in HEK293 cells [90].
# Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition
## 3 Contributions  
In general, the performance of unimodal biometrics is easily affected by noisy data, interclass similarities, and intraclass variations as compared to the multimodal biometrics approach [1], while the conventional score-level fusion method is limited by the variability of the performance accuracies of different matches. To address these issues, the paper presents the following contributions:  
* A multi-instance finger vein system uses different instances of a finger vein, such as index, middle, and ring finger as the feature set. The use of multiple instances increases the number of unique patterns and lowers the interclass similarities. It also allows different finger vein instances to provide more information and thus maintain a minimum performance.
* We propose to optimize the hyperparameters of the histogram of oriented gradients (HOG) feature extractor and support vector machine (SVM) matcher for better performance. Improved performance is observed using the proposed optimization method, which will be indicated in the experimental analysis section. The use of optimizer for HOG features was inspired by the work of [PERSON] et al. [11] that show improved performance of HOG when using self-adaptive particle swarm optimization (SPSO) for hyperparameter optimization. However, we propose the use of Bayesian optimization as an alternative optimizer to SPSO. SPSO is a population-based optimizer, which means it performs multiple objective function evaluations, according to the size of the population in each optimization step. This makes SPSO much more computationally expensive compared to Bayesian optimization which only performs a single objective function evaluation in each optimization step while maintaining a robust performance.
* The score level fusion method based on Bayesian optimized SVM score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF) is proposed. In the BSSF method, each finger vein instance is matched using SVM. The resulting score for each finger vein instance is then multiplied with a weight that is optimized using a Bayesian optimizer. The weighted scores are then summed and used to determine the decision of match or not match. In the BSBF method, each finger vein instance is matched using an Euclidean distance matcher. The resulting score for each finger vein instance is then normalized and concatenated as a score vector. The score vector is then fed into the SVM classifier for decision. BSSF should give better performance as compared to BSBF, since there is less information loss, by classifying the HOG feature with SVM. Meanwhile, BSBF classifies the score after Euclidean distance matching, which results in more information loss. However, BSSF requires access to the features from a biometric system, which is not always available in the biometric system. On the other hand, BSBF only requires access to the scores from a biometric system, which is usually available even in a proprietary biometric system; this gives BSBF wider compatibility as compared to BSSF.
## 5 Conclusions  
It has previously been proven that solar-induced chlorophyll fluorescence (SIF) can act as a proxy for vegetation photosynthesis. In this study, we investigated the performance of estimates of canopy GPP produced using far-red SIF and their response to different meteorological conditions by using continuous tower-based observations made in a maize field during 2017 and 2018. The SIF\({}_{760}\) tracked the GPP well at both diurnal and seasonal scales, and SIF\({}_{760}\) was more linearly correlated with PAR than GPP was. CI is the dominant meteorological parameter that influences the non-linear relationship between SIF\({}_{760}\) and GPP, and the ratio of GPP to SIF\({}_{760}\) decreased noticeably with an increasing CI at both diurnal and seasonal scales. The results showed that the SIF\({}_{760}\)-based GPP estimation improved from values of R\({}^{2}\) = 0.66 and RMSE = 7.02 mw/m\({}^{2}\)/nr/sr to R\({}^{2}\) = 0.76 and RMSE = 6.38 mw/m\({}^{2}\)/nr/sr for the linear model, and from R\({}^{2}\) = 0.71 and RMSE = 4.76 mw/m\({}^{2}\)/nr/sr to R\({}^{2}\) = 0.78 and RMSE = 3.50 mw/m\({}^{2}\)/nr/sr in the case of the non-linear model. These results indicate that the SIF-GPP relationship is non-linear, and that the non-linear characteristics can mostly be explained by the CI. As the CI is usually an easily accessible meteorological variable, the presented findings are useful for the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data.  
[PERSON], [PERSON] and [PERSON] conceived and designed the research. [PERSON] conducted the data analysis and prepared the manuscript. [PERSON] and [PERSON] significantly contributed to the research method and the manuscript revision. [PERSON] and [PERSON] provided important support for the tower-based measurements. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2017 YFA0603001; the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA15017800); and the National Natural Science Foundation of China, grant numbers 41825002 and, 41671349.  
The authors declare no conflict of interest.
## 4 Discussion
### Comparison of Two 3D Roving Models (BIFC vs. LIAC)
#### 4.1.1 Rowing Cycle and Sub-Phases  
As hypothesized, the newly proposed 3D rowing LIAC model was rated as significantly more realistic than BIFC in drive (Q5), recovery (Q6), catch (Q7), release (Q8), and overall rowing cycle (Q9).  
For drive, expert rowers mentioned that the rendered forces with LIAC were realistically resistive. The development of the force during early drive and the reduction of the force in the late drive was also judged as realistic. Experts also reported a smoother force increase and decrease in the catch and release, respectively. Inclusion of parameter \(p_{imm}\) in the blade area calculation accounted for a smooth transition of the oar blade during catch and release, which plays an important role in the exerted propulsion forces, continuity of the oar motion and elimination of unwanted decelerating blade drag forces [45].  
On the other hand, the resistance provided by the BIFC model was reported to be low during the drive. It could be argued that a selection of a higher \(C_{L}^{max}\) value might have yielded a more realistically resistive force in drive. However, the force calculation in the BIFC model did not incorporate a parameter for the 'immersed blade area in the water' as in the case of LIAC. Thus, due to rapidly increased forces, the binary approach in the force modeling might have introduced a discrepancy during blade transition between air and water in the catch and release.  
For recovery, superiority of the LIAC model over BIFC is not surprising because in the pilot tests, initially selected values of rope tension for LIAC (\(\tau_{rec}^{min}=50\) N ) were also rated to be unrealistically high by three pilot experts. Nevertheless, we wanted to inspect and compare the overall ratings from main study expert rowers; thus, \(\tau_{rec}^{min}\) forces in the BIFC model were maintained at 50 N. If we had lowered the \(\tau_{rec}^{min}\) tension forces for the BIFC model, the observed significance regarding the realism of recovery forces of LIAC over BIFC would not have been found in our opinion.
## 1 Introduction
Usually, seawater intrusion is an active process, driven by changes to the hydrologic system. There is no sharp limit of mixing sea and freshwater, but there are mixing zones whose positions and sizes depend on the hydraulic gradient and the geological structure of the aquifer and other factors that can contribute to the movement of this zone into deeper parts of coastal aquifers. In other words, when seawater intrusion occurs in coastal freshwater aquifers the zone of dispersion or transition zone appears, which is the area where the body of saltwater and freshwater meet and form a hydrologic barrier [17]. This interface of the salt and freshwater moves back and forth naturally due to fluctuations in the recharge rate of freshwater back into these coastal aquifers [18]. Seawater intrusion can cause health, agriculture, and environmental problems for users of groundwater [19]. Effective and efficient control of saltwater intrusion should be regarded as an important task for any groundwater exploration project, and the limitations in terms of the economic cost and the water demands of growing populations in coastal areas should be considered [17]. Detecting the seawater intrusion in coastal karstic aquifers in Croatia is one of the measures in the water management of such areas and is the main topic of this paper. In coastal areas, there are numerous problems with occasional increases in salinity at pumping stations as a consequence of excessive groundwater exploitation, but there are also numerous occurrences of brackish leakage water at coastal springs in completely natural conditions. In order to define the national criteria for determining the risk of salinization, the groundwater body (GWB) Bokanjac-Policnik was selected as a pilot area. In that area, operational monitoring was carried out by drilling additional wells and placing measuring devices in them along the depth of the aquifer, with additional surface water monitoring, hydrological and hydrogeological analysis of the obtained data. Analysis of hydrochemical indicators that are used for detecting the seawater intrusion will be presented on the example of the GWB Bokanjac-Policnik. The given results will provide a better understanding of the salinization process in the case study area.
# Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs
## 2 Related Work
### Protocols for Position Uncertainty  
In [1], the authors propose an optimization technique based on the position estimation and uncertainty regions around the gliders. Two types of uncertainty regions are discussed in this technique: internal-uncertainty region and external-uncertainty region. The source glider specified the internal-uncertainty region, whereas the external-uncertainty region is defined by the neighboring gliders. The trajectory of the gliders is predefined (sawtooth trajectory), and they adjust their transmission power according to these uncertainties of regions.  
With the help of light beacons and a camera, the position of the AUV is estimated in [8]. The set of light beacons or light markers is placed on the AUV and detected using camera. After detecting light markers, the 3D position of the AUV is estimated; however, this tracking system is for very short range, that is up to 10 m.  
In [9], an AUV positioning algorithm is proposed based on the strap down inertial navigation system (SINS) and the long baseline (LBL) acoustic positioning system. The seabed hydrophones are used by the underwater LBL to confirm the position of the AUV; at least three hydrophones are attached at the bottom of the mother ship, and those receive signals from the hydrophone placed at the seabed. Using the LBL positioning system, the 3D position of each hydrophone is calculated, which is then used by AUVs for local area positioning.  
The authors propose a novel scheme for estimation and navigation of the AUV state in the presence of water currents [10]. They used the extended Kalman filter (EKF) to estimate the state of AUV and water currents in the north and the east directions. This estimation process is integrated with the speed through water (STW) to give a better estimation of the speed over ground (SOG). Using this information, the navigation and the AUV state estimation are performed.  
A paradigm-changing geographic routing protocol is presented in [11], to model the position uncertainty, and this approach relies on the statistical approach. The simulation results are validated by using real-time test bed emulations. These emulations use underwater acoustic modems. The performance tradeoffs of position-based schemes are given in Table 1.  
In [1; 8; 9; 10; 11], the energy consumption and packet drop ratio is high. In order to reduce the energy consumption and increase the packet delivery ratio, we have proposed two schemes, PAMP and Co PAMP. PAMP minimizes the energy consumption and end to end delay; whereas, Co PAMP improves the packet delivery ratio by avoiding the void regions.