esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
Our mathematical models have been developed in a newly proposed similarity space instead of a metric space. It has been proven that such definitions are fully interchangeable by using various duality theorems. A similarity space is a more direct and simpler way to formulate our tasks and avoid misunderstanding, using the dual notation with the distance metric [43] in corresponding areas such as fuzzy matching, similarity search, and similarity join.  
Recently, the cited articles show that there is only one possible way to symmetrically solve the token assignment problem between records \(\mathcal{R}_{1}\) and \(\mathcal{R}_{2}\) in approximate record matching: by maximum weight matching on a bipartite graph.  
Secondly, we do not simplify the derivation of the lower bound of the Q-gram filter by applying the lower bound to the entire record as if it were a single token according to the formula in Theorem 8; rather, we derive a much more accurate deduction by assuming that the record consists of multiple tokens in Theorems 9 and 10. In this article, we have shown that maximizing the edit distance loss function among tokens under constraint \(\alpha\) is an integer linear programming problem; additionally, we find that a more precise estimation exists for the record matching. We have found such an optimal solution for the Q-gram filter that cannot be further improved.  
In our paper, we mainly focus on comparing two strings on textual similarity. Our research does not include the context of the data, which would be the intention of supervised (deep learning) or unsupervised learning (e.g., TF-IDF, BM25). The main focus of the research is to establish a baseline metric between two known records without their context of what source or document collection they come from.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Similarity** & **Time Complexity** & **Space Complexity** \\ \hline Q-gram filter + FRMS & \((1-\gamma)\cdot O(1)+\gamma\cdot O(n^{4})\) & \(O(n^{2})\) \\ \hline FRMS & \(O(n^{4})\) & \(O(n^{2})\) \\ \hline [PERSON]–[PERSON] & \(O(nm)\) & \(O(nm)\) \\ \hline [PERSON] ([PERSON] \(\delta=0.8\)) & \(O(nm)\) & \(O(nm)\) \\ \hline [PERSON] & \(O(n+m)\) & \(O(n+m)\) \\ \hline [PERSON]–[PERSON] & \(O(n+m)\) & \(O(n+m)\) \\ \hline [PERSON]–[PERSON] ([PERSON]) & \(O(nm)\) & \(O(nm)\) \\ \hline [PERSON]–[PERSON] & \(O(nm)\) & \(O(nm)\) \\ \hline [PERSON] & \(O(nm)\) & \(O(nm)\) \\ \hline [PERSON]–[PERSON] & \(O(nm)\) & \(O(nm)\) \\ \hline [PERSON]–[PERSON] & \(O(nm)\) & \(O(nm)\) \\ \hline Q-gram (2-gram, 3-gram) & \(O(n)\) & \(O(n)\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time and space complexity of selected similarity functions.
## 3 Design Methodology
### Unit Cell Effects  
The proposed antenna design is structured from SRR geometry with four unit cells to form a butterfly shape. Therefore, to study the effects of adding them to the antenna performance, the authors applied a parametric study-based CST MWS. In this simulation, the authors included the first unit cell in the antenna design, then compared it to the obtained results, in terms of S\({}_{11}\) and gain spectra, from the identical antenna design based on (two, three, and four) unit cells. We found, at the first cell, the antenna shows multiple frequency bands within the frequency range of interest as shown in Figure 3a. By increasing the number of the unit cell, a clear increase in the antenna resonance modes is observed. This is due to the fact of fading the current motion at the junction points between the transmission line and the unit cells. This fading is due to the current chocking by the inductive addition from the proposed unit cells [6]. Therefore, the antenna gain is found to be increased rapidly, due to the parallel connection between the fading points that creates an excellent  
Figure 2: Antenna performance with changing L value: (**a**) S\({}_{11}\) and (**b**) gain spectra.  
electromagnetic aperture coupling with the free space impedance [12]. This is achieved due to the fact of the connection between the unit cells on the proposed transmission line serially. In such a connection, the current motion is mostly in the same direction as the proposed unit cells. Also, adding the unit cell in this manner, two parts opposite to each other from the transmission line, creates antiparallel equalized forces that add nothing to the current motion [12] to keep the first frequency resonance at 3.5 GHz. Nevertheless, the combination between them is in a series of connection results adding the effects of them directly to each other. Since the proposed unit cell is capacitive inclusion, adding them and increasing their number may realize a significant reduction in the equivalent capacitor [8], which in turn increases the particular operating frequency band [9]. Therefore, the antenna gain is found to be about 5.5 dBi at 3.5 GHz as seen in Figure 3b.
## 2 State of the Art
### Heterogeneous Routing Protocols
#### 2.2.3 Clustering/Hierarchical Routing Protocols  
In this category, UAV nodes are grouped into clusters, in a hierarchical fashion, and within each cluster, there is a node (cluster head) that enables inter-cluster communication via other cluster heads. In these routing protocols, the selection of the node acting as cluster-head is a critical task since the overall performance of the network will depend in part on this node. Within this category of protocols, the Clustering Algorithm of UAV Networking (CAUN) [59], and Mobility Prediction Clustering Algorithm for UAV Networking (MPCA) [60] may be mentioned. The operation of CAUN is quite simple: the initial cluster is built on the ground depending on the mission application, and once the network is deployed, the cluster adapts according to real-time conditions. On the other hand, MPCA predicts network topology updates, and, in turn, determines cluster formation based on UAV mobility, allowing for more stable clusters. In addition to the protocols described above, there are other protocols belonging to the category of clustering/hierarchical routing protocols, such as Landmark Ad Hoc Routing (LANMAR) [61], Multi-Meshed Tree Protocol (MMT) [62], Cluster-Based, Location-Aided DSR (CBLADSR) [63], and Disruption Tolerant Mechanism (DTM) [64].
## Materials and Methods
### Sample Characterization  
Powder X-ray diffraction (PXRD) measurements were conducted on a Bruker powder X-ray diffractometer (AXS model D8 advance) with Cu K\(\alpha\) radiation, \(\lambda\) = 1.5418 A, 3-s step time and 0.075\({}^{\circ}\) step size. Fourier-transform infrared spectroscopy (FTIR) was measured over a range of 480-4000 cm\({}^{-1}\) using a Perkin Elmer Fourier-transform infrared spectrometer (Spectrum GX FT-IR System). Thermogravimetric analyses (TGA) were performed on a TA Instruments thermogravimetric analyzer (SDT 2960 Simultaneous DSC-TGA), with samples heated from 25 to 800 \({}^{\circ}\)C (heating rate 20 \({}^{\circ}\)C/min) under an N\({}_{2}\) atmosphere. Magnetic properties of samples were investigated using a Lakeshore vibrating sample magnetometer (VSM 7404) at room temperature (applied field \(-\)6000 to 6000 Oersted). Transmission electron microscopy (TEM) and high-resolution TEM were carried out on a JEOL transmission electron microscope (2100), operated at 200 keV. Zeta potential measurements were performed on a Malvern Instruments zeta potential analyzer (Zetasizer, Nano ZS), in water at 25 \({}^{\circ}\)C. Surface properties of sorbents were examined using N\({}_{2}\) sorption on a Quantachrome Instrument (Nova 2000e). All samples were degassed at 200 \({}^{\circ}\)C for 10 h prior to measurements. Quantification of cysteine loading in Cys-C@Fe\({}_{3}\)O\({}_{4}\) particles was obtained through combustion sulfur analysis using a LECO (CHNS-628S) elemental analyzer.
Development of Bubble Size Correlation for Adiabatic Forced Convective Bubbly Flow in Low Pressure Condition Using CFD Code  
[PERSON]  
1 School of Mechanical Engineering, Pusan National University, Busandaehak-r0 63-2, Geumjeong-gu, Busan 46241, Korea; [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (H.K.); [PERSON]L_ADDRESS] (J.J.J.)  
[PERSON]  
1 School of Mechanical Engineering, Pusan National University, Busandaehak-r0 63-2, Geumjeong-gu, Busan 46241, Korea; [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (H.K.); [PERSON]L_ADDRESS] (J.J.J.)  
[PERSON]  
1 School of Mechanical Engineering, Pusan National University, Busandaehak-r0 63-2, Geumjeong-gu, Busan 46241, Korea; [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (H.K.); [PERSON]L_ADDRESS] (J.J.J.)  
[PERSON]  
2 Korea Atomic Energy Research Institute, Daedeok-daero 989-111, Yuseong-gu, Daejeon 34057, Korea; [EMAIL_ADDRESS] 2  
[PERSON]  
1 School of Mechanical Engineering, Pusan National University, Busandaehak-r0 63-2, Geumjeong-gu, Busan 46241, Korea; [EMAIL_ADDRESS] (J.B.); [EMAIL_ADDRESS] (H.K.); [PERSON]L_ADDRESS] (J.J.J.)  
Received: 13 July 2020; Accepted: 3 August 2020; Published: 6 August 2020  
[PERSON] Abstract  
In a multidimensional two-phase flow analysis, bubble size significantly affects interfacial transfer terms such as mass, momentum, and energy. With regard to bubbly flow, the application of a simple correlation-type bubble size model presents certain advantages, including short calculation times and ease of usage. In this study, we propose a semi-theoretical correlation developed from a steady state bubble number density transport equation for predicting the distribution of local bubble size using a computational fluid dynamics (CFD) code. The coefficients of the new correlation were determined using the local bubble parameters obtained on the basis of three existing vertical air-water experiments. Finally, these were implemented in commercial CFD code and evaluated against experimental data, which showed that the proposed correlation exhibits good prediction capability for forced convective air-water bubbly flows under low pressure conditions.  
Keywords:bubble size model; interfacial transfer term; number density transport equation; bubbly flow; CFD simulation +
Footnote †: journal: Applied Sciences
# Helicopter Safe Landing Trajectory after Main Rotor Actuator Failures
## 2 Helicopter Swashplate Actuator Geometry and Reconfiguration after Actuator Failure
### Control Mixer Reconfiguration after Servo Failure  
This study focuses on the failure situation that one of the three servos is locked in its current position during flight. After certain servo failure, the original mixer, as Equation (3) shows, needs to be reconfigured. As Section 1 states, attitude of helicopter is still controllable if the vertical channel  
Figure 1: General swashplate actuator distribution. \(\gamma\) and \(\psi_{0}\) are constant angles decided by the main rotor geometry. Typically, the lateral servo is placed on the angular bisector between the forward and the aft servos on the non-rotating ring.  
is given up. By the inverse method described in [13] of Equation (3), a general mixing configuration without changing the mechanical mixer can be expressed as follows:  
Case 1 (forward actuator failure): If the forward actuator is locked, \(h_{f}\) is fixed to its current position. New cyclic pitch control is remapped using the original mixing relationship of the lateral and longitudinal cyclic pitch:  
\[\left\{\begin{array}{l}\Delta\theta_{1c}{}^{\prime}=\frac{1}{1+\cos\gamma}( \Delta\theta_{1c}+\cot\gamma\cdot\Delta\theta_{1s})\\ \Delta\theta_{1s}{}^{\prime}=2\Delta\theta_{1s}\end{array}\right. \tag{4}\]  
which results in a variation of the collective pitch of  
\[\Delta\theta_{0}{}^{\prime}=\frac{r_{\rm nr}}{2r_{r}}\frac{\cos\gamma}{1+\cos \gamma}\theta_{1c}-\frac{r_{\rm nr}}{2r_{r}}\frac{\sin\gamma}{1+\cos\gamma} \Delta\theta_{1s} \tag{5}\]  
Case 2 (aft actuator failure): If the aft actuator is locked, \(h_{a}\) is fixed to its current position. New cyclic pitch control is remapped using the original mixing relationship of lateral and longitudinal cyclic pitch:  
\[\left\{\begin{array}{l}\Delta\theta_{1c}{}^{\prime}=\frac{1}{1+\cos\gamma} (\Delta\theta_{1c}-\cot\gamma\cdot\Delta\theta_{1s})\\ \Delta\theta_{1s}{}^{\prime}=2\Delta\theta_{1s}\end{array}\right. \tag{6}\]  
which results in a variation of the collective pitch of  
\[\Delta\theta_{0}{}^{\prime}=\frac{r_{\rm nr}}{2r_{r}}\frac{\cos\gamma}{1+\cos \gamma}\theta_{1c}+\frac{r_{\rm nr}}{2r_{r}}\frac{\sin\gamma}{1+\cos\gamma} \Delta\theta_{1s} \tag{7}\]  
Case 3 (lateral actuator failure): If the lateral actuator is locked, \(h_{l}\) is fixed to its current position. New cyclic pitch control is remapped using the original mixing relationship of the collective and longitudinal cyclic pitch:  
\[\left\{\begin{array}{l}\Delta\theta_{1c}{}^{\prime}=-\frac{r_{r}}{r_{\rm nr }}\frac{1}{1+\cos\gamma}\Delta\theta_{0}\\ \Delta\theta_{1s}{}^{\prime}=\Delta\theta_{1s}\end{array}\right. \tag{8}\]  
which results in a variation of the collective pitch of  
\[\Delta\theta_{0}{}^{\prime}=-\frac{r_{\rm nr}}{r_{r}}(1+\cos\gamma)\Delta \theta_{0} \tag{9}\]
## 3 Simulations Results for Opaque Passive Antenna  
The tapered slots exhibit maximum current flow at their edges, as observed in Figure 2a,c,e. By controlling the current direction, the radiating beam can be adjusted accordingly. In state 1, the beam is radiated at an angle of \(\phi\) = 0\({}^{\circ}\) with a peak gain of 6.5 dBi, as depicted in Figure 2b. In state 2, the current deviation occurs on the left side of the tapered slot, resulting in a radiating beam at an angle of \(\phi\) = +90\({}^{\circ}\) with a gain of 4.4 dBi, as shown in Figure 2d. Similarly, in state 3, the current deviation occurs on the right side of the tapered slot, resulting in a radiating beam at an angle of \(\phi\) = -90\({}^{\circ}\) with a peak gain of 5.5 dBi, as illustrated in Figure 2f.  
The reflection coefficients of the passive antenna in states 1, 2, and 3 are depicted in Figure 3, showcasing the simulation results. The antenna shows operation within a frequency range from 17 to 42 GHz for state 1, 19 to 45 GHz for state 2, and 21 to 41.5 GHz for state 3, with an S\({}_{11}\) magnitude below \(-\)10 dB.  
Figure 4 presents the simulation results of the gain. State 1 has a gain of 6.5 dBi at 26 GHz, state 2 has a gain of 4.4 dBi at the same frequency, and state 3 has a gain of 5.5 dBi at 26 GHz. Figure 5 showcases the efficiency performance of the antenna in three distinct states across a frequency range spanning from 21 GHz to 30 GHz.  
Figure 1: Configuration and geometries of the passive antenna: (**a**) front side with metallic strips as short circuit for different states; (**b**) back side with feeding microstrip line.  
Figure 2: _Cont._Figure 4: Simulated gain of the three states of the opaque passive Vivaldi antenna.  
Figure 3: Simulations of reflection coefficient of the passive antenna operating in states 1, 2, and 3.  
Figure 2: The current surface distribution: (**a**) state 1, (**c**) state 2, (**e**) and state 3. The 3D radiation pattern at 26 GHz: (**b**) state 1, (**d**) state 2, and (**f**) state 3.
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 5 Results
### Results on SST
#### 5.2.2 PRD Types  
We selected the input sequence length of five for this experiment as the SP-CRN had the lowest RMSE of 0.86 K in the previous experiment (Figure 9). Table 3 shows the results of the three proposals compared with different settings for SP-CRN. The results for SP-CRN-1 were slightly better than for SP-CRN-1 (0,1,0) (baseline). The best result was for SP-CRN-1 (1,1,0) with an RMSE of 0.79 K. The SP-CRN-2 results indicated that the importance of a prior periodic cycle of more than four years was almost identical for an SST prediction. All the settings of SP-CRN-3 outperformed the baseline result, for which SP-CRN-3 (0,0,2) had the best RMSE of 0.55 K, and the first-order derivative played an important role. The results showed that SP-CRN-2 with more than four years and SP-CRN-3 with the depth of two drastically improved prediction accuracy. In summary, the order of the effectiveness of SP-CRN in periodicity utilization for an SST prediction was PRD-3 over PRD-2 and PRD-1.  
Figure 9: The average RMSE of comparison methods of different lengths of sequential input for SST prediction. The error bar indicates the SD of the SST.
# Combination of Robust Algorithm and Head-Tracking for a Feedforward Active Headrest
## 5 Conclusions and Discussion  
In this article, a combination of a robust algorithm and head-tracking for a feedforward active headrest has been proposed. A robust algorithm that considers head rotation based on the feedforward ANC algorithm is derived. Furthermore, a head-tracking system with several infrared rangefinders based on a Kalman filter is used to track head movements. The noise control system retrieves the filter coefficients, which are precalculated when the head is in the center of the current region based on the robust algorithm after obtaining the head position. Once the optimal filter of the ANC system is obtained, error microphones are not needed. Experiments were conducted on a model of a sleeper on a high-speed train in a semi-anechoic room. The results show that the active headrest with robust algorithm achieved 10-14 dB at the two ears when the head rotated 30\({}^{\circ}\), 60\({}^{\circ}\) and 90\({}^{\circ}\) leftwards and rightwards, when the head had no movement, and the proposed robust active headrest integrated with head-tracking achieved 10-15 dB noise reduction with head movement and rotation. This concept and the research results will help reduce the noise for sleepers on high-speed railways and other sleepers in noisy areas.  
In the experiments we conducted, the reference signal was obtained directly from the signal to the primary loudspeaker. In practice, there is no such reference signal. This will limit the performance  
Figure 6: Noise reduction with the proposed active headrest with headtracking. Different colors indicate the amount of noise reduction: (**a**) noise reduction at left ear when the head has no rotation; (**b**) noise reduction at left ear when the head rotates leftwards 60\({}^{\circ}\); and (**c**) noise reduction at left ear when the head rotates rightwards 60\({}^{\circ}\).  
of the system in real train coach environment. Multiple reference signals that are time-advance of the primary noise may be required to get good coherence with primary signals in real application. The selection of the reference signal will be discussed in detail in future work.  
**Author Contributions:** [PERSON] and [PERSON] conceived the work; [PERSON] and [PERSON] conducted the experiments; and [PERSON] and [PERSON] provided funding and support for the experiment. [PERSON] and [PERSON] are [PERSON]'s thesis supervisors and provided critical comments and guidance for the work. All authors have given final approval of the version to be published.  
**Funding:** This work was supported by the National Key Research and Development Program of China (Grant No. 2016 YFB1200503) and the National Natural Science Foundation of China (Grant Nos. 11474306, 11404367, and 11474307).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Aridity Index  
The aridity index (AI) serves as a measure to quantify regional dryness and humidity conditions, reflecting the soil water availability [41; 42]. A decrease in AI implies increasing aridity, which diminishes soil water availability, disrupting the composition, structure, and function of the terrestrial ecosystems [43]. Here, we calculated the AI to assess the relationships between vegetation productivity and greenness in drying and wetting conditions. The AI is defined as the ratio of annual precipitation to annual potential evapotranspiration, with higher values indicating more humid conditions and lower values indicating more arid conditions. This index was derived from the multiyear average (1982-2018) of the ratio of annual precipitation (P) to annual potential evapotranspiration (PET). For the precipitation, the data were sourced from [[https://www.geodata.cn/data/datadetails.html?dataguid=192891852410344&docId=2218](https://www.geodata.cn/data/datadetails.html?dataguid=192891852410344&docId=2218)]([https://www.geodata.cn/data/datadetails.html?dataguid=192891852410344&docId=2218](https://www.geodata.cn/data/datadetails.html?dataguid=192891852410344&docId=2218)) (accessed on 1 August 2023), and for evapotranspiration, the data were downloaded from [[https://www.geodata.cn/data/datadetaids.html?dataguid=34595274939620&docId=1751](https://www.geodata.cn/data/datadetaids.html?dataguid=34595274939620&docId=1751)]([https://www.geodata.cn/data/datadetaids.html?dataguid=34595274939620&docId=1751](https://www.geodata.cn/data/datadetaids.html?dataguid=34595274939620&docId=1751)) (accessed on 1 August 2023). The data's spatial resolution is 0.0083\({}^{\circ}\). An AI value lower than 0.65 was considered indicative of drought conditions [44; 45]. In this study, AI data were categorized into 6 gradients: hyper-arid(AI \(<\) 0.05), arid (AI between 0.03 and 0.20), semi-arid (AI between 0.20 and 0.50), dry sub-humid (AI between 0.50 and 0.65), humid (AI between 0.65 and 1), and hyper-humid (AI \(>\) 1.00). The mean AI value reflects the spatial variation in aridity conditions. The aridity gradient map illustrates a clear distribution of dry and wet conditions across China (Figure 1b). To match the NDVI data used in our study, we aggregated the AI data to a spatial resolution of 0.083\({}^{\circ}\).
## 5 Experimental Setup and Results
### Experiment 1--Processing Quality of Acquired Gesture  
In this experiment, different variants of one image of 'A' are selected, and hand gesture detection is processed. The gestures in all the images are identical, and only the quality is varied. Figure 9 shows the software screenshot showing the different qualities of the same image of gesture 'A'. The image quality is changed to create five more images of gesture 'A', and then these images are processed using the image detection algorithm.  
The results are shown graphically in Figure 10. Each image is processed 100 times, and the correct detection percentage is shown. It is concluded from this experiment that gesture detection is more than 93% for different image qualities. The average detection percentage of the six images is 96%.  
Figure 9: Hand gesture dataset for experiment 1.