esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 3 Experimental Results of RF Wireless Power Transmission
### Dual-Band (2.4 and 5.8 GHz) RF WPT Experiment  
#### 3.2.1 WPT Experiment (3): With a Dual-Band Rx Antenna  
To verify whether the developed dual-band RF WPT system transmits dual-band wireless power simultaneously, as shown in Figure 1c, a dual-band RF WPT experiment was conducted with the dual-band Rx in Figure 12. This Rx antenna was positioned at the point of \(x^{\prime}=0,~{}y^{\prime}=0\), and \(z^{\prime}=1\). The total transmitted power of 2.482 GHz and 5.73 GHz was 8 W (=39 dBm) and 2 W (=33 dBm), respectively, and wireless power at both 2.482 and 5.73 GHz was radiated simultaneously with two focused beams. The dual-band power received by the Rx antenna was measured through a spectrum analyzer. The screen of the spectrum analyzer used at that time is shown in Figure 19. From this result, it can be seen that dual-band RF wireless power of 2.482 and 5.73 GHz could successfully be transmitted and received at the same time.  
Table 4 presents the measured results of the dual-band received power according to the beam-steering method. When the beam was focused on the Rx position, the measured  
Figure 19: Screen of the spectrum analyzer when transmitting 2.482 and 5.73 GHz RF power to a dual-band Rx antenna.  
Figure 18: Distribution of the 5.73 GHz received power according to the Rx position:(**a**) distance = 1 m; (**b**) distance = 2 m.  
2.482 GHz received power was 22.45 dBm (WPT efficiency = 2.197%) and the measured 5.73 GHz received power was 13.15 dBm (WPT efficiency = 1.033%), meaning that these received power levels at 2.482 and 5.73 GHz were correspondingly 0.15 dB and 0.55 dB higher than those by beam-forming. Because the distance of 1 m is in the Fresnel region for the 2.4 GHz and 5.8 GHz thinned arrays, the effect of beam-focusing may be outstanding here. Moreover, these measured results are in good agreement with the calculated results using Equation (4).
## 3 The Use of Agro-Wastes in Concrete Production
### Use of Coconut Shells and Fibers
Adding coconut fibers improved both conventional concrete and CS aggregate concrete properties; however, further studies on durability, temperature resistance, and structural behavior under various loading types are needed. Mechanical test results indicate that excessive fiber content or fiber orientation in the matrix significantly contributes to the strength properties of the mix [41]. Optimal strength performance for mortars was obtained with a 1% fiber content, though overlapping fibers in cases of high fiber volume may create failure paths under load. Although the strength of concrete decreases with increased CS aggregate content, values close to conventional concrete allow for using CS aggregates at 10-20% levels [40; 42]. Complete replacement of natural coarse aggregates with CS aggregate leads to gradual reductions in both slump and hardened properties. Incorporating FA into the cement paste improves fresh concrete workability, and adding up to 20% FA can compensate for concrete strength loss [35; 43]. Thus, using CS aggregate alongside FA is a viable alternative for environmentally sustainable concrete production [26; 30]. The mechanical properties of AW concrete show significant improvements when specific combinations are used. For example, CS concrete combined with 0.75% steel fibers exhibited a compressive strength of 47.8 MPa compared to 35.6 MPa for CS concrete without fibers [26; 43]. Additionally, the inclusion of FA improved the tensile strength by 15% and enhanced fresh concrete workability [35; 42]. The modulus of elasticity remained comparable to conventional mixes, ensuring suitability for lightweight structural applications [38]. However, challenges such as variability in material properties and long-term durability remain, highlighting the need for further research [39].
## 3 Results
### Animal Model's Characteristics  
The animal models used in the studies included in this review varied among canine (37%), porcine (27%), sheep (18%), and primate (18%) (Figure 2).  
The age of the animals was reported in eight studies. [PERSON] et al. (2021) [26] used primates aged between 6 and 9 years. [PERSON] et al. (2021) [27], [PERSON] et al. (2006) [28], [PERSON] et al. (2006) [29], and [PERSON] et al. (2019) [30] used animals that were 1 year of age or younger. [PERSON] et al. (2017) [31] used 2- and 3-year-old sheep, while [PERSON] et al. (2022) [32] used 4- to 7-year-old sheep. [PERSON] et al. (2009) [33] and [PERSON] et al. (2016) [34] did not report the age of the animals but specified skeletal-mature and young adult animals, respectively. [PERSON] et al. (2007) [35] reported amines at 1 year and 6 months of age. Only six studies mentioned the gender of the animals (n = 4 males [26; 29; 33; 34] and n = 2 females [31; 32]).  
The creation of CSBDs and the implantation of ceramic scaffolds were performed in the mandibular region in nine [26; 28; 29; 30; 32; 33; 34; 35; 36] out of the eleven studies (82%), located in either the anterior or posterior mandibular region.  
[PERSON] et al. (2009) [33] and [PERSON] et al. (2019) [30] created bone defects in the bilateral posterior mandibular region, while [PERSON] et al. (2021) [27] and [PERSON] et al. (2017) [31] utilized the calvaria region (18%). The mean follow-up period was 20 weeks in the studies using the mandible region to create the critical bone defect and 24 weeks in the calvaria region. The animal model and defect characteristics are presented in Table 1.  
Figure 2: Distribution of animal models used in the studies included in this review.
## 4 Research on Using State Estimation and Residual Life Prediction of Electrical Connectors
### Residual Life Prediction Method  
HSMM-based residual life prediction method is shown as following:  
1. Data training list  
Collecting the intermittent failure data from the whole life cycle of electrical connector and the generalized severity of intermittent failure can be extracted for data training for the residual life prediction model.  
2. Determination of the prediction model structure  
Combined with the transition characteristics of using states, the structure of the prediction model can be determined.  
3. Estimation of prediction model parameters  
Based on the observation sequences from the whole life cycle, an HSMM-based prediction model with all using states can be trained using the Parameter re-estimation algorithm. Further, the probability of using state transition \(a_{\text{ij}}\), mean of duration probability of using state \(i\), \(\mu(h_{\text{i}})\) and variance of duration probability of using state \(i\), \(\sigma^{2}(h_{\text{i}})\) can be calculated separately. Then calculating the time duration of each using state, which are denoted by \(D\) (\(h_{0}\)), \(D\) (\(h_{1}\)),... \(D\) (\(h_{\text{N}-1}\)).  
4. Prediction of residual life  
Assuming that the current using state is \(i\). let \(\text{RUL}_{i}\) be the residual useful life (RUL) starting from state \(i\). With the following recursive algorithm, the RUL of the electrical connector can be determined.  
At using state \(N-1\):  
\[RUL_{N-1}=a_{N-1,N-1}[D(h_{N-1})+D(h_{y})]+a_{N-1,N-1}[D(h_{y})]\]  
At using state \(N-2\):  
\[RUL_{N-2}=a_{N-2,N-2}[D(h_{N-2})+RUL_{N-1}]+a_{N-2,N-2}[RUL_{N-1}]\]  
At using state \(i\):  
\[RUL_{I}=a_{Ni,i}[D(h_{i})+RUL_{i+1}]+a_{i,i+1}[RUL_{i+1}]\]
## 1 Introduction
In fact, the protection or controlled release of BAM and the masking of unpleasant tastes or odors depends primarily on the structure and content of the carrier, as the carrier can act as a barrier to oxygen, water, light, and other constituents. Numerous carrier materials were tested for their suitability as carrier material for SD, and considering the hydrophobic nature of oils, carriers with good emulsifying properties are more suitable for the encapsulation and protection of this type of core [17]. For the encapsulation of lipophilic BAM by SD, gum arabic (GA) and \(\beta\)-cyclodextrin(\(\beta\)-CD) are usually used [15; 18; 19]. GA can interact with both hydrophobic and hydrophilic compounds due to its branched carbohydrate chain and the presence of glycoproteins linked by covalent bonds. \(\beta\)-CD is composed of seven glucopyranose units in the shape of a truncated cone with a molecule that is hydrophilic outside and hydrophobic inside and is suitable for the entrapment of hydrophobic compounds as it forms inclusion complexes in the hydrophobic central cavity [19]. Due to these properties, cyclodext have good biocompatibility and an excellent ability to encapsulate a variety of guest molecules [20]. The combination of carrier materials instead of using a single carrier material could lead to a higher retention of BAM due to their different properties [19]. To the best of our knowledge, there is limited information on how SD conditions affect the retention of lipophilic compounds in encapsulated SBOs. Therefore, the aim of this study was to determine the retention of fatty acids, \(\alpha\)-tocopherol, and carotenoids in the encapsulated SBOs using GA, \(\beta\)-CD, and their mixture (1:1) at optimal SD conditions in comparison to the BAM content of the SBO. In addition, the color parameters in the spray-dried powders and bioaccessibility of \(\beta\)-carotene, which has the highest provitamin A activity, were evaluated.
# Clock Ensemble Algorithm Test in the Establishment of Space-Based Time Reference
## 4 Satellite Clock Bias Prediction
### Prediction Models  
Many prediction models of clock deviation have been developed with similar accuracy, e.g., the quadratic polynomial model, the gray model, and the Kalman filter model. Their accuracy is of a similar magnitude, and the quadratic polynomial model shows to be simpler in the calculation, more stable in practice, and more accurate in short-term prediction [54]. For the above reasons, we select this model to predict clock bias in our subsequent analysis.  
The quadratic polynomial model fits the phase, frequency, and frequency drift parameters according to the physical characteristics of the satellite clock, and the fitting expression is as follows [55]:  
\[y(t)=a_{0}+a_{1}(t_{i}-t_{0})+a_{2}(t_{i}-t_{0})^{2}+\Delta_{i} \tag{9}\]  
where \(y(t)\) is the clock observation, \(a_{0},a_{1},a_{2}\) are three parameters of the satellite clock model, phase, frequency, and frequency drift, \(t_{0}\) is the reference epoch of the satellite clock model, \(t_{i}\) denotes the observation epoch. \(\Delta_{i}\) is the residual of the prediction model.  
As the most commonly used prediction model, the quadratic polynomial model is suitable for the rubidium atomic frequency standard, while for hydrogen and cesium frequency standards, the first-order polynomial model is a more appropriate choice. As a result, we use the first-order polynomial model for hydrogen and cesium clocks and the quadratic polynomial model for rubidium clocks.
## 3 Engineering Applications
### Water Diversion Project from the Three Gorges Reservoir to Hanjiang River
#### 3.2.2 Slug Test of the Water Diversion Project from the Three Gorges Reservoir to Hanjiang River
In addition, according to the qualitative analysis of the water level recovery time in Figure 10, it can be qualitatively understood that the rock mass permeability in the tested sections of WDTGH-1-1, WDTGH-4-2 and WDTGH-6-1 with a slow water level recovery is low.
## 5 Results
### Main Result on CMR  
Unlike the English dataset ReLocaR and SemEval, Chinese text is harder to understand. Table 2 gives the result on CMR. As can be seen from the experimental results, fastText, CNN, and BiLSTM+Att have little gap in the Chinese MR task. BERT greatly improves the performance by relying on the powerful ability of the pre-trained model, and the Entity BERT proposed in this paper achieves a better result by reinforcing the entity information. Compared with Entity BERT, EBGCN is about 1.2% higher in accuracy, showing a huge performance improvement and proving the significance of dependency knowledge. However, taking advantage of syntactic noise elimination, EBAGCN obtains the SOTA result on CMR, proving the validity of this work.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MODEL** & **Acc** & **Precision** & **Recall** & **F1-L** & **F1-M** \\ \hline fastText [44] & 70.0 & 70.1 & 70.1 & 71.6 & 68.8 \\ CNN [45] & 73.1 & 73.1 & 73.1 & 73.3 & 73.3 \\ BiLSTM+Att [43] & 73.1 & 73.4 & 73.4 & 73.9 & 72.4 \\ \hline BERT [16] & 81.7 & 81.6 & 81.6 & 75.4 & 87.7 \\ \hline Entity BERT (this work) & 85.3 & 85.0 & 85.0 & 78.4 & **91.9** \\ EBGCN (this work) & 86.5 & 86.6 & 86.6 & **90.8** & 82.6 \\ EBAGCN (this work) & **87.4** & **87.4** & **87.4** & 88.3 & 86.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result on self-constructed Chinese dataset CMR.
# Ecotoxicological Assessment of Microplastics in Freshwater Sources--A Review
## 5 Ecotoxicological Effects on Microplastics
### Ecotoxicological Effects on Daphnids  
_Daphnia_, a genus of planktonic freshwater crustaceans, are frequently used in ecotoxicological laboratory tests, especially _Daphnia magma (D. magma)_[121; 130; 131; 132]. Studies indicate that ingestion depends on MPs size, shape, type, and concentration [121; 130]. Thus, _D. magma_ can ingest PE particles in the range 63-75 \(\upmu\)m [130] and PET long fibers < 1400 \(\upmu\)m [121]. MPs agglomeration, occurring at higher MPs concentrations, would yield a lowering of ingestion rate [108; 121; 131; 132]. [PERSON] et al. [108] reported that elevated concentrations of PE particles decreased mobility of _D. magma_, while irregular shaped fragments (10-75 \(\upmu\)m) affected _D. magma_ more intensively than regular shaped beads (10-106 \(\upmu\)m). [PERSON] et al. [119] observed size-dependent differences in inhibitory effects; smaller PE particles are more toxic for _D. magma_ than larger ones. Analogous results were obtained for exposing _D. magma_ to PS [131]. It seems that smaller MPs affect _D. magma_ by adhering to inner and outer surfaces, impairing filtering activity, compromising gut integrity, and entering tissue and cells [107]. Besides ingestion, MPs influenced the egestion of _D. magma_ as well. The egestion is a very important function for organisms; very low egestion (or even the total absence) leads to reducing food intake and consequently starving [105; 121]. It has been reported that _D. magma_ eegests regular-shaped PE at a higher rate than irregular ones, indicating that spiky particles retained longer in the organism, and have a higher hazard effect [108; 130]. This is quite concerning because irregular MPs are commonly present in aquatic ecosystems [108]. [PERSON] et al. [120] exposed _D. magma_, _Daphnia pulex_, and _Ceriodaphnia dubia_ to MPs at various temperatures and discovered that sensitivity to various MPs may differ between species, while the acute sensitivity of _Daphnia_ sharply increases with the temperature. [PERSON] et al. [121] tested the potential effect of pre-feeding _Daphnia_ by algae (simulating real environmental conditions) and found that pre-feeding did not affect MPs uptake on daphnids growth, but had a significant influence  
Figure 4: Effects of microplastics on microalgae.  
on their mortality (pre-fed daphnids showed no increase of mortality). [PERSON] et al. [133] found that tiny PS particles have a negative influence on daphnids body size and reproduction: 67.7% of malformed offspring were observed. It is important to note that in all the above-mentioned daphnids studies, MPs concentrations were generally much higher than the environmental ones.
# Sensitivity Analysis of Numerical Coherency Model for Rock Sites
## 3 Sensitivity Study
### Numerical of Simulations  
Since random fields were used, it is important to select an appropriate number of probabilistic analyses to obtain reasonable results. The number of numerical simulations was determined by the 30 input ground motions and the number of random field realizations. To determine the optimal number of random field realizations, a convergence evaluation was conducted. Figure 5 shows the results of this sensitivity analysis. The results indicate that stable estimates can be achieved when more than 150 analyses are performed up to the 50 Hz range. Therefore, this study conducted a total of 150 analyses for the coherency evaluation, combining 30 input ground motions with five realizations.
# Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory
## 5 Conclusions  
In this paper, we have presented a new dynamic image-based visual servoing method. We proposed to solve the problem of the pursuit of a car-type vehicle by a quadrotor UAV. Under the specific conditions of this application, we have demonstrated that we can use a single point of the target object to perform the task of dynamic visual servoing. This contribution aims to reduce the computation time of the quadrotor control law. To circumvent the problem of controlling an under-actuated system and to achieve the necessary displacements generated by the visual servoing algorithm, a new flatness-based control algorithm has been integrated. The simulation results show the effectiveness of the proposed method. The proposed method provides an intriguing solution to the issue of vehicle tracking by a quadrotor UAV through the utilization of an onboard camera. In order to effectively implement this method, it is advisable to pair it with an additional algorithm that enables the precise selection of the target vehicle from among the other automobiles present. Given the aforementioned considerations, we suggest a classification algorithm based on artificial intelligence. In another context, the proposed method can be extended to solve the planning problem in the image plane for the 2D visual servoing of a quadrotor. This represents a challenge and will solve many problems related to the integration of vision in UAVs.  
Conceptualization, H.M. and K.K.; methodology, H.M. and A.A.; software, A.A., B.A.A. and S.A.; validation, H.M., A.A. and K.B.; formal analysis, K.B. and K.K.; investigation, H.M. and M.A.; resources, A.S. and M.A.; writing--original draft preparation, H.M. and K.K.; writing--review and editing, A.A. and A.S.; visualization, A.A. and S.A.; supervision, H.M.; project administration, K.K.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 223202.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: Simulation results. (**a**) Displacement along \(X\) of the target trajectory (red), the visual servo-based trajectory (gray), and the flatness-based quadrotor trajectory (blue). (**b**) Displacement along \(Y\) of the target trajectory (red), the visual servo-based trajectory (gray), and the flatness-based quadrotor trajectory (blue). (**c**) Orientation along \(Z\) (Yaw angle) of the target trajectory (red), the visual servo-based trajectory (gray), and the flatness-based quadrotor trajectory (blue). (**d**) Error evolution in pixels along \(u\). (**e**) Error evolution in pixels along \(v\). (**f**) The evolution of the roll angle. (**g**) The evolution of the pitch angle.
## 3 Analysis and Comparison of Machine Learning-Based Anomaly Detection
### Machine Learning Models
#### 3.1.4 Gradient Boosting (GB) Classifier  
The gradient boosting (GB) classifier is an effective ensemble machine learning approach for classification applications. It works by gradually integrating numerous weak learners (usually decision trees) so that each new learner corrects the errors committed by the preceding ones. During training, GB makes an initial prediction (typically the target variable's mean) and then fits a weak learner to the residuals of past predictions, changing the model's weights to reduce residual errors [47]. This procedure is continued recursively, with each new learner focusing on the remaining faults as the model's overall accuracy improves. The formula for GB, which incorporates a weak learner into the ensemble at each iteration, can be expressed as Equation (5):  
\[\hat{g}_{i}^{(t)}=\hat{g}_{i}^{(t-1)}+f_{i}(\mathbf{x_{i}}) \tag{5}\]  
where \(t\) is the iteration number and \(\hat{g}_{i}^{(t)}\) is the predicted score for the \(i\)-th training example at iteration \(t\). The weak learner, which was added at iteration \(t\), is shown by \(f_{i}\).  
This iterative process allows Extreme Gradient Boosting (XGBoost) to gradually improve its predictions by incorporating the knowledge of multiple weak learners, eventually leading to a strong ensemble model. Gradient boosting is a popular choice in many machine learning applications due to its high predicted accuracy, robustness against overfitting, and ability to capture complicated correlations in data.
## 5 Discussion
### What Are the Obstacles to the Development of UUS and How Can They Be Overcome?  
In view of the characteristics of different types of cities and the differences in geological environment, there is no direct reference model for choosing which evaluation method and what evaluation system to establish, and the corresponding technical standards can be formulated for different topographical and geomorphological features; for example, coastal cities should focus on considering the impact of hydrogeology on the development of underground space, and mountainous cities should consider the impact of sensitive geological factors. This study suggests that government departments, higher education institutions, and relevant research institutes and units should work together to tackle these problems, with the government guiding and controlling the direction, and the higher education institutions and relevant researchers working together on scientific research projects, to form a new framework of joint coordination and unified action, to constantly propose optimized and upgraded evaluation models, and to contribute to the early landing of the complete set of standards for the evaluation of the development of underground space.  
The spatial data in the process of underground space planning and construction has different sources and formats. The handling of multi-source heterogeneous data is crucial for the success of the project. However, due to local government policy regulations and market competition, many essential spatial datasets are independently maintained by separate entities. This fragmentation leads to challenges where the evaluations of underground space development fail to effectively guide construction projects, or even result in cases of \"projects leading, evaluations lagging.\" Therefore, the rational and safe development of underground space rely on the concerted efforts of various relevant departments, such as land management, comprehensive planning, architectural design, and construction technology, necessitating the establishment of interdepartmental coordination mechanisms. Additionally, to support efficient collaboration among these departments, it is imperative to establish and continuously improve a spatial data sharing platform early on, encompassing data on geological formations, soil conditions, hydrogeology, building distribution, socioeconomic factors, and more.