esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Improvement of ZPC Tracking Performance
### Tracking Performance Comparison of ZPC Against Other Works  
This subsection compares our proposed ZPC receiver with other representative NR navigation receivers by tracking accuracy. The ZPC receiver is a cooperative navigation receiver that utilizes all detectable signal components as a local replica signal, including SSB, CSI-RS, and DMRS in PDSCH and PDCCH. [PERSON] first proposed an NR receiver  
Figure 6: The relationship between delay-locked loop (DLL) tracking accuracy and different correlation methods. DLL standard deviation (STD) represents the DLL tracking accuracy.  
exclusively using SSB signals [37]. This reception strategy ignores other available navigation signals and does not consider the LEO navigation scenario. [PERSON] introduces a cognitive (CON) method for LEO OFDM-like signals to identify navigation reference signals without considering cyclic prefix influence [30; 38].  
For comprehensive comparison, three NR signal configurations are used in the experiment:  
* **Signal 1**: using default configuration as baseline (see Table 1).
* **Signal 2**: equivalent bandwidth to Signal 1 with PDSCH spectral center shifted from zero frequency to \(260\Delta f\).
* **Signal 3**: PDSCH bandwidth expands (1.8 MHz \(\rightarrow\) 10.62 MHz), slot number increase (5 \(\rightarrow\) 9), and DMRS-Additional-Position adjustment (0 \(\rightarrow\) 1). For every NR frame, the resource elements of the reference signal increase from 830 to 6850, leading to an augmentation in the reference signal's power.  
Figure 7 compares the DLL tracking accuracy of different navigation reception strategies for three NR signals. The experimental results reveal four key findings. (1) The SSB navigation receiver (proposed by [PERSON]) exhibits identical tracking performance for different NR signals. This receiver only utilizes SSB signals, so the PDSCH changes do not affect its tracking performance. (2) Our proposed ZPC receiver achieves superior tracking accuracy compared to SSB receivers. This enhancement stems from comprehensively utilizing all known reference signals, including SSB, DMRS in PDCCH and PDSCH, and CSI-RS. (3) Compared with CON, the ZPC receiver significantly enhances tracking accuracy for identical NR signals. Because the CON receiver does not consider the CP process, noise caused by CP exists. Moreover, the PBCH changes over time, and the CON receiver cannot recognize the PBCH signal. Notably, this paper assumes the CON receiver operates in an ideal scenario, whereas real-world implementations face additional challenges from dynamic PDSCH bandwidth allocation and an aperiodic reference signal. (4) The tracking accuracy of ZPC can significantly improve by using different NR configurations. because navigation signals demonstrate varying PSD and CNR across different NR configurations. So, the base stations can flexibly configure NR signals according to navigation requirements.  
Figure 7: DLL tracking accuracy for different NR signals and reception strategies. For signals 1, 2, and 3, ZPC, respectively, achieves 80.3%, 85.6%, and 82.6% higher tracking accuracy than CON.
## 4 Results and Findings  
In this section, the paper's contribution will be compared with the state-of-the-art techniques currently used in cloud analysts, with a focus on the PSO that is typically implemented by IaaS, SaaS, and PaaS cloud providers. In particular, the overall response time, DCPT, and cost of the different solutions based on service broker policies will be investigated. Moreover, RR ESCE and throttled algorithms will also be analyzed.  
All of the LB algorithms for comparison are implemented into a cloud analyst simulator. A cloud analyst simulator is a tool based on CloudSim that simulates large-scale applications in a cloud environment. Cloud analyst is an extension of CloudSim with added visualization capabilities. The PSO, RR, ESCE, and throttled virtual machine LB algorithms have been configured on the cloud analyst. The selected three service broker policies are CDC, optimize response time, and reconfigure dynamically with load for experimental purposes with LB algorithms (PSO, RR, ESCE, and throttled). The following are various scenarios in which implementation has been performed.  
Section 4.1 is used for the analysis of the performance parameters of PSO, RR, ESCE, and throttled algorithms exploiting the CDC, optimizing response time, and reconfiguring dynamically with load service broker policies to capture a more realistic scenario where performance may change due to overall response time, DCPT, and cost parameters.
## 2 Materials and Methods  
### Standards and Materials  
Analytical grade standards of bisphenol A (BPA), nonylphenol technical mixture, 4-n-nonylphenol and 4-tert-octylphenol (purity greater than 99%) were obtained from Dr. Ehrenstorfer GmbH (Augsburg, Germany). All solvents used were of high purity (LC-MS grade), including acetonitrile, methanol and water, purchased from Fisher Scientific (Leicester, UK) and dichloromethane from Labscan (Dublin, Ireland). Individual stock solutions were prepared at 1000 \(\upmu\)g mL\({}^{-1}\) in methanol and stored at \(-20\)\({}^{\circ}\)C. Further dilutions and mixtures of them were prepared in methanol/water (50:50) or in the sample extract to appropriate concentration levels. All working solutions were stored in amber glassware at 4 \({}^{\circ}\)C. To perform QuECHERS extraction, Teflon centrifuge tubes of 15 mL and 50 mL were used while the salt-sorbent materials included anhydrous magnesium sulphate (MgSO\({}_{4}\)), LiChroprep RP-18 (40-64 \(\upmu\)m) and tri-sodium citrate dehydrate purchased from Merck KGaA (Darmstadt, Germany); sodium acetate and sodium chloride (NaCl) of analytical reagent grade from Riedel de Haen (Hannover, Germany); sodium citrate dibasic sesquihydrate from Sigma-Aldrich (Bellefonte, PA, USA); bondesi-primary secondary amine (PSA; 40 \(\upmu\)m) and SampliQ Carbon SPE (GCB, graphitized carbon black) from Agilent Technologies (Waldbronn, Germany). Ammonia solution (25%) was purchased from Riedel de Haen (Hannover, Germany) while Millex-HV (PVDF, 0.45 \(\upmu\)m) syringe filters were purchased from Millipore (Cork, Ireland).
# Innovative Acoustic-Hydraulic Method for High-Performance Fine Liquid Atomization
## 3 Mathematical Model of the Acoustic-Hydraulic Nozzle  
### Hydrodynamic Processes:Hydrodynamic Cavitation Threshold  
In the system under consideration, processes occurred that involved the movement of fluid under the influence of hydrostatic pressure and ultrasonic vibrations. Under certain conditions, both hydrodynamic cavitation and ultrasonic cavitation processes can occur. These conditions are as follows.  
The hydrodynamic cavitation threshold occurs at a certain fluid supply pressure:  
\[X=\frac{(p-p_{s})(1-\varepsilon^{2})}{\varepsilon^{2}(p-p_{a})}\leq 1, \tag{1}\]  
where \(p\) is the hydrostatic pressure of the liquid flow, \(p_{s}\) is the vapor pressure of the liquid, \(p_{a}\) is the atmospheric pressure, \(\varepsilon=S_{2}/S_{1}\), \(S_{1}\) is the cross-sectional area of the cavitation zone, and \(S_{2}\) is the area of the outlet.  
This expression was obtained by jointly solving the continuity equation and the Bernoulli equation in the inlet section into the cavitation zone:  
\[\begin{array}{l}V_{x}=\sqrt{\frac{2(p-p_{s})}{\rho(1-\varepsilon^{2})}},\\ V_{1}=\varepsilon V_{x}.\end{array} \tag{2}\]  
where \(V_{1}\) is the flow velocity in the cavitation zone, \(V_{x}\) is the flow velocity after the outflow, and \(\rho\) is the density of the liquid.  
The hydrodynamic cavitation number is defined as \(X=\frac{2(p-p_{s})}{\rho V_{1}^{2}}\)[16]. For X > 1, the flow regime is pre-cavitation; for X < 1, developed cavitation is observed. From Equation (1) we obtain the cavitation pressure threshold:  
\[p_{cr}=\frac{p_{a}\xi-p_{s}}{\xi-1}, \tag{3}\]  
where \(\xi=\frac{\varepsilon^{2}}{1-\varepsilon^{2}}\). At pressures above \(p_{cr}\), hydrodynamic cavitation occurs.  
It should be noted that the pressure \(p_{1}\) in the cavitation zone differs from the liquid pressure at the inlet to the structure \(p\). By solving the system of continuity and Bernoulli  
Figure 3: Scheme of the experimental setup (**a**) and operation of the experimental setup (**b**): 1—nozzle, 2—liquid drops, 3—laser probe beam (Spraytec), 4—laser beam source (Spraytec), 5—laser beam receiver (Spraytec). The nozzle is located 15 cm above the beam.  
equations in two sections--at the entrance and exit to the cavitation zone--we obtain an expression for the pressure in the cavitation zone:  
\[p_{1}=p+\xi(p-p_{a})\left(\left(\frac{S_{1}}{S_{0}}\right)^{2}-1\right), \tag{4}\]  
where \(S_{0}=S_{3}-S_{1}\) is the cross-sectional area of the annular part of the structure (flow area before entering the cavitation zone).  
The volumetric flow rate of the liquid is expressed by the equation:  
\[Q=V_{x}\cdot S_{2}=S_{2}\sqrt{\frac{2(p-p_{a})}{\rho(1-\epsilon^{2})}}. \tag{5}\]  
We will evaluate the performance of the nozzle under consideration in accordance with expression (5).
## 1 Introduction
Although researchers have broadened the application scenario and scope by incorporating piezoelectric materials into traditional phononic crystals, most studies are restricted to one-dimensional and planar two-dimensional phononic crystals. Therefore, in this study, a two-dimensional piezoelectric phononic crystal with thickness and a square hollow as well as convex structures is investigated and analyzed to obtain the wave transmission properties in terms of the dispersion relationships for the propagating and complex band structure. Moreover, the adjustable shape and geometrical parameters of the square hollow and convex structures in the piezoelectric phononic crystal can provide more possibilities for the generation of wide bandgaps and extend tunable space. The layout of the article is as follows. After the introduction, a description of the schematic diagram of a 2D piezoelectric phononic crystal with thickness is given and the numerical formulations for carrying out the complex band diagram are derived. Section 3 presents numerical results, after which the effects of series and parallel circuits on the transmission loss of a finite-length piezoelectric phononic crystal are taken into account. The tunability of the band structure is illustrated through a parametric study, which is discussed in Section 4. Lastly, Section 5 outlines the conclusions.
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 3 System Calibration and Image Reconstruction Methods  
To address the problems introduced by system deviations, this paper proposes a novel system calibration method based on straightness and scale constraints and an image reconstruction method based on weighted average. The methods will be described in detail below, including the following: (1) how to parameterize the system deviations and model the practical scanning trace; (2) how to calibrate and obtain the system parameters; and (3) how to reconstruct a quality image.  
Figure 3: Simulated practical scanning traces (upper row) and the corresponding images (lower row) reconstructed using the ideal scanning trace with different system deviations. (**a**) Ideal case; (**b**) Angle deviation of the rotation axis; (**c**) Position deviation of the initial illuminated spot; (**d**) Rotation velocity deviation.
## 2 Methodology
### InSAR Data and Method
We use the stack processor [45] within the ISCE software [35] for interferogram processing. The stack of SAR images is coregistered using pure geometry with precise orbits and a digital elevation model (DEM). We remove the topographic phase using the SRTM DEM (SRTMGL1, 1 arc second with voids-filled, ~30 m) [46]. We pair each SAR image with its nearest 3 neighboring images in time domain to form 596 small temporal baseline interferograms, multi-look each interferogram by 3 in range, and filter the phase using the Goldstein filter with a strength of 0.2. We excluded 16 interferograms due to low coherence. The interferograms are phase unwrapped using the minimum cost flow method [47]. Increasing the spatial resolution comes at a cost of lowering coherence. We fit a simple temporal model that includes linear and periodic (annual and semi-annual) terms, and consider RMS residual phase in mm as a measure for our data quality (Figure S10). The original number of acquisitions is 264. We identified and removed 62 particularly noisy acquisitions using a 5 mm residual threshold. The coherence history (Figure S11, left panel) of our region shows a lower coherence in comparison with our regional scale results. This decrease is caused by increasing spatial resolution. Nevertheless, the temporal coherence of the Al-Yutumah valley is high (S12), indicating minimum unwrapping error. Moreover, as phase unwrapping error can be detected using the number of interferogram triplets with non-zero integer ambiguity of the closure phase [42], our results (S13) show two triplet integer peaks suggesting 2 out of 580 interferograms have unwrapping errors in the coherent region, which is a minimal amount, and should has negligible effect on our time series estimation. We use the MintPy software [42] to form the InSAR time series from the stack of interferograms using the small baseline approach [48]. We exclude low-coherence interferograms using the coherence-based network modification with a threshold of 0.7. We remove the stratified tropospheric delay using the ERA5 weather reanalysis dataset [33] with the PyAPS software [40], the topographic residual caused by the DEM error [41], and a linear phase ramp for each acquisition to remove the long spatial wavelength component. We use the temporal coherence [49] to eliminate the unreliable pixels with a threshold of 0.7. The noisy acquisitions are identified based on the residual phase root mean squares with a predefined cutoff value (3 times the median absolute deviations) and excluded during the estimation of topographic residual and average velocity [42].
## 5 Result and Discussion
### Exhaust Gas Temperature at the Exit of the HTL Evaporator and Heat Utilization Ratio
When toluene is used for HTL, and \(T_{\rm g1}\) is 523.15-623.15 K, the optimal HTL evaporation temperature does not reach the upper limit. With the increase in \(T_{\rm g1}\), \(\eta_{\rm g}\) is mainly affected by the increase in \(T_{\rm g1}\), although \(T_{\rm g3}\) increases initially and later decreases, while \(\eta_{\rm g}\) increases. When cyclopentane is used for HTL, and \(T_{\rm g1}\) is 623.15 K, the maximal \(\eta_{\rm g}\) is 74.6%. Figure 11: Effects of \(T_{\rm g1}\) on the exhaust gas temperature at the exit of the HTL evaporator. In Figure 13, with the increase in \(T_{\rm g1}\), \(\eta_{\rm jw}\) of the cyclopentane system increases initially, then decreases, and finally increases. When \(T_{\rm g1}\) increases from 563.15 K to 568.15 K, the cycle type changes from saturated to superheated. \(\eta_{\rm jw}\) decreases with the increase in \(T_{\rm g1}\). With the increase in \(T_{\rm g1}\), \(\eta_{\rm jw}\) increases for the cyclohexane and toluene systems. \(\eta_{\rm jw}\) of the benzene system increases initially with the increase in \(T_{\rm g1}\). When \(T_{\rm g1}\) increases to 618.15 K, the optimal HTL evaporation temperature reaches the upper limit and the PPTD of the HTL evaporator still occurs at VPP, and \(\eta_{\rm jw}\) decreases initially and later increases. Considering that the optimal HTL condensation temperatures of the cyclopentane, cyclohexane, and toluene systems are lower than \(T_{\rm jw1}\), Case 1 occurs, and \(\eta_{\rm jw}\) is higher than 35%. When toluene is used, Case 2 occurs, \(\eta_{\rm jw}\) is lower than 26%, and Case 2 must be avoided. The cyclopentane system has a higher \(\eta_{\rm jw}\) than the other systems. When \(T_{\rm g1}\) is 623.15 K, the maximal \(\eta_{\rm jw}\) is 51.8%. An increase in \(T_{\rm g1}\) can improve \(\eta_{\rm jw}\).
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 1 Introduction
### Why NTN?  
It is difficult for current fifth-generation (5G) wireless technologies based on the ground to provide adequate coverage in extremely remote areas while also meeting the stringent quality-of-service (QoS) standards expected of terrestrial networks. Since flying nodes are able to function at significantly greater heights in comparison to ground-based surveying methods, the NTN with flying nodes can complement terrestrial networks byproviding widespread communication for mariners, farmers, and travelers in the air and on the ground [9].  
To provide fully global wireless communications everywhere and at any time, 6G wireless networks should seamlessly combine NTNs with terrestrial networks [2]. Furthermore, unlike terrestrial communications, which may experience service disruptions due to natural catastrophes or adversaries, NTNs guarantee uptime for really essential, mission-critical applications [10]. Overall, according to predictions, NTNs will boost 6G's functionality in terms of coverage, user bandwidth, system capacity, service reliability, availability, energy consumption, and connection density.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 3 Results and Discussion
### Overall Comparison  
In Table 2, we show the results of our method in comparison to baseline and state-of-the-art DDI classification approaches which reported their overall performance metric based on the four positive classes. These approaches are trained on the same training set and evaluated on the same test set provided by the DDI corpus and follow the same experimental setting without the inclusion of external information. These approaches can effectively be divided into two categories: traditional methods that utilize extensive feature engineering and state-of-the-art neural network-based approaches that aim to learn feature representations automatically based on different architectures. We compare our proposed approach to the traditional method \"FBK-irst\" presented in [32], which used linear features, path-enclosed tree kernels and linguistic features. For the NN-based approaches, we compared our approach to the following methods:  
* \"SCNN\" [10]--CNN-based architecture with manually designed features;
* \"MCCNN\" [8]--CNN with multichannel word embeddings;
* \"ASDP-LSTM\" [13]--Hierarchical RNNs with shortest dependency paths;
* \"PM-BLSTM\" [14]--Bidirectional LSTM with position-aware attention;
* \"GCNN-DDI\" [25]--Bidirectional LSTM with GNN that utilized entire dependency graphs;
* \"DREAM\" [26]--Bidirectional LSTM with GNN that utilized PageRank enhanced dependency graphs.  
The experimental results show that our Knowledge Graph-based approach that utilized BioBERT LM achieves the best overall performance for the classification of DDIs. The proposed KG schema with the domain-adapted pre-trained weights and the non-message passing MLPs are the main contributing factors, which will be analyzed in the following subsections. In the four positive classes, our approach achieves the best results in the _Advice, Effect_ and _Mechanism_ classes and a similar score in the _Int_ class. In the following sections, we additionally analyze and discuss the various components of our method and their contribution to the overall performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **System** & **Advice** & **Effect** & **Int** & **Mechanism** & \(F1_{Micro}\) \\ \hline SVM & FBK-irst & 69.20 & 62.80 & 54.70 & 67.90 & 65.10 \\ CNN & SCNN & - & - & - & - & 68.60 \\ CNN & MCCNN & 78.20 & 68.20 & 51.00 & 72.20 & 70.21 \\ LSTM & ASDP-LSTM & 80.30 & 71.80 & 54.30 & 74.00 & 72.90 \\ LSTM & PM-BLSTM & 81.60 & 71.28 & 48.57 & 74.42 & 72.99 \\ GNN & GCNN-DDI & 83.50 & 75.80 & 51.40 & 79.40 & 77.00 \\ GNN & DREAM & 84.80 & 76.10 & **55.10** & 81.60 & 78.30 \\ Our method & BioBERT-GIN & **86.45** & **78.46** & 54.80 & **82.27** & **79.14** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall performance comparison of our proposed method. All values are \(F1\) scores (%) and ‘-’ denotes the value was not provided in the published paper. \(F1_{micro}\) denotes the overall score on the four positive classes. The highest values are shown in bold.
## 5 Conclusions  
This study applied the AUED method to enhance the performance and effectiveness of the FPSO-NTE algorithm. Use of the AUED method in the proposed FPSO-NTE algorithm enabled a rapid automatic search for the best combination of four constant coefficients, namely \(\lambda\), \(\alpha\), \(\beta\), and \(\gamma\). The major contribution of this paper was the use of AUED to improve the performance of the algorithm and to obtain a robust output. The above experimental and simulation results indicate that the proposed AUFPSO-NTE algorithm achieved a higher solution accuracy compared to the FPSO and PSO-FOV proposed by [PERSON] and coworkers [34; 35], the FPSO proposed by [PERSON] et al. [36], the AFO-DPSO proposed by [PERSON] et al. [38], the HAFPSO proposed by [PERSON] et al. [39], and the PSO-based algorithm described by them [34; 35; 36; 38; 39]. Examples demonstrated that the solutions obtained using the proposed AUFPSO-NTE algorithm were more consistent, i.e., more robust. Therefore, we conclude that the proposed AUFPSO-NTE algorithm had a superior effectiveness and performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & \(\Lambda\) & \(\alpha\) & \(\beta\) & \(\gamma\) \\ \hline Rastrigin & 1.2494 & 0 & 1.6361 & 1.8209 \\ Sphere & 1.3333 & 0.4444 & 0.2222 & 0.8889 \\ Rosenbrock & 1.7778 & 1.3333 & 0 & 1.5556 \\ Griewank & 0.2765 & 1.2 & 0.4889 & 2 \\ Schwefel P1.2 & 1.3333 & 0.4444 & 0.2222 & 0.8889 \\ Quartic & 1.5111 & 0.2765 & 0.1136 & 0.8 \\ Salomon & 1.3087 & 1.6889 & 0.8592 & 0.0889 \\ \hline \hline \end{tabular}
\end{table} 
Table 24: Best combinations of the constant coefficients for each benchmark function of the proposed AUFPSO-NTE in Example (4).