esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Phase Composition and Transformation Toughness  
The monoclinic content V\({}_{\text{m,pol}}\) in the polished surface of the composites is shown in Figure 5a. Surprisingly, the materials sintered at the highest temperature show the lowest monoclinic content (2-4 vol%), while the monoclinic content in materials sintered at lower temperature can be as high as 12 vol%. A general trend describing monoclinic content versus sintering temperature and SA6 fraction cannot be seen. Figure 5b shows the corresponding monoclinic content V\({}_{\text{m,f}}\) in fractured surfaces. In the fractured surfaces, 52-68 vol% monoclinic can be found. Here, the SA6 contents seems to have little influence, while increasing sintering temperature leads to a higher transformed fraction. The difference V\({}_{\text{f}}\) = V\({}_{\text{m,f}}\) - V\({}_{\text{m,pol}}\) is the stress-induced transformability of the zirconia materials. The highest transformability V\({}_{\text{f}}\) of up to 65 vol% can be identified in materials sintered at 1550 \({}^{\circ}\)C. High transformability implies high transformation toughness. However, the transformation efficiency X in the McMeeking formula (Equation (2)) is not known. A priori it was assumed that X = 0.27 (predominantly dilatoric) as in Y-TZP [4].  
\[\Delta K_{lc}^{T} = X/(1-\
u)V_{Z}V_{f}E\varepsilon\sqrt{h} \tag{2}\]  
(with \(X\) = transformation efficiency, \(\varepsilon\) = 0.054 = Volume change due to transformation, \(\
u\) = Poisson's ratio = 0.31, \(h\) = depth of transformed zone, \(V_{z}\) = zirconia content = 0.85).  
Figure 4: Mechanical properties depending on composition and sintering temperature for: (**a**) Bending strength \(\sigma_{\text{4 pt}}\); (**b**) Fracture resistance K\({}_{\text{ISB}}\).  
The calculation of transformation toughness increments leads to values between 4-5.5 MPa\(\surd\)m. Materials sintered at 1550 \({}^{\circ}\)C tend to the upper limit of this range. Transformation toughness is, therefore, the dominant reinforcement mechanism, but cannot fully explain the high toughness. In a previously published paper, the threshold toughness of a compositionally similar 1Y6 Ce-10A-10 SA6 composite was measured (KIO = 4.6 MPa\(\surd\)m) [25]. It can be estimated that in the present case depending on the material the R-curve dependent part of toughness amounts to KIC \(-\)KIO = \(\sim\) 6-7 MPa\(\surd\)m. Assuming toughening effects to be additive, we may, therefore, estimate the contribution of other reinforcement mechanisms, such as crack bridging, crack deflection, and microcracking at 1.5-2.5 MPa\(\surd\)m [32].
## 3 Data Synthesis and Analysis
#### 3.1.3 Firefly Algorithm  
The firefly algorithm (FA) is a swarm intelligence metaheuristic optimization technique that was first introduced by [PERSON] in 2009 [72]. According to [PERSON] et al. [73], FA has a unique capability of automatic subdivision compared with other metaheuristic search algorithms. [PERSON] and [PERSON] [74] presented a hybrid algorithm K-FA that combined the K-means algorithm and firefly algorithm. The firefly algorithm was used to find centroid for specified k number of clusters with K-means algorithm used for refining the centroid. [PERSON] and [PERSON] [75] proposed using a firefly-based clustering method to parallel K-means for handling a large number of clusters. Similar to [PERSON] and [PERSON] [74], the FA finds the initial optimal centroid, which is then refined using K-means for improved clustering accuracy. [PERSON] et al. [76] presented an integrated clustering framework combining optimized K-means with firefly algorithm and Canopies for better clustering accuracy.  
To address K-means' initialization sensitivity and local optimal convergence, [PERSON] et al. [77] proposed FCM-FA, hybridizing fuzzy C-means with a firefly algorithm for faster convergence. [PERSON], [PERSON], and [PERSON] [78] proposed a novel firefly-based K-means algorithm--FA-K-means, in which the global search capacity of the FA was used to resolve the problem of local convergence of the K-means for efficient cluster analysis. [PERSON] et al. [73] proposed two variants of the FA (IIEFA--inward intensified exploration FA and CIEFA--compound intensified exploration FA) which are incorporated into the K-means clustering algorithm for improved clustering performance. [PERSON], [PERSON], and [PERSON] [79] proposed a hybrid firefly algorithm and K-means algorithm for color image quantization. [PERSON] and [PERSON] [80] integrate a firefly-algorithm-based K-means algorithm with a firefly-algorithm-based support vector regression with wavelet transform in developing an export trade value prediction system. [PERSON], [PERSON], and [PERSON] [81] introduced a K-means and firefly algorithm hybridization for the intrusion detection system.  
[PERSON] et al. [82] proposed KFCFA--K-member fuzzy clustering and firefly algorithm, which is a combined anonymizing algorithm for protecting anonymized databases against identity disclosure in social networks. [PERSON] et al. [83] proposed a firefly-based K-means algorithm with global search capability for clustering big data. [PERSON] et al. [84] proposed a novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm (Kmeans-FFA-KELM) for the monthly reference evapotranspiration estimation in parallel computation.
# A Construction Method for the Random Factor-Based G Function
## 5 Simulation Results
### Randomness  
Randomness refers to the unpredictable nature of the appearance of each frequency point in a frequency-hopping sequence, which reflects the irregularity of the sequence during the hopping process [30]. The construction rule of the G function based on the m sequence improvement requires that all subsets be traversed in one cycle, which is a pseudo-random structure. This constraint leads to the order and high predictability of frequency selection. In contrast, the RFGF is not subject to this restriction. It allows some subsets not to be traversed in one cycle but requires that all subsets must be covered in multiple cycles. Such flexibility causes the generated frequency-hopping sequence to have a more obvious advantage in randomness.  
The power spectrum of the generated frequency-hopping sequence is typically employed to assess the randomness of the G function [31]. A flatter power spectrum indicates superior random performance of the G function and a greater degree of signal interception difficulty. The power spectra of the frequency-hopping sequences generated by the G function using the four methods are presented in Figure 7.  
In order to facilitate a more intuitive comparison of the flatness of the power spectra of the frequency-hopping sequences generated by the four methods, a statistical analysis was conducted on the standard deviations of the power spectrum value. The standard deviation is an indicator that reflects the degree of dispersion of the data and is employed here to evaluate the degree of dispersion of the power spectrum observations in relation to the mean value. The statistical results are presented in Table 3.  
A smaller standard deviation corresponds to a flatter power spectrum of the frequency sequence, indicating that the hopping sequence has better randomness. As illustrated in the table, the RFGF method exhibits significant advantages in power spectrum standard deviation, with a value of 2.3897, which is significantly lower than the minimum value of 2.4084 of other methods. This result indicates that the power spectrum of the RFGF method is flatter, reflecting its superior random performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RFGF** & **TVID-GF** & **SA-GF** & **MRCS-GF** \\ \hline Value & 2.3897 & 2.4084 & 2.4114 & 2.4296 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of numerical standard deviation of power spectrum.  
Figure 7: Frequency sequence power spectrum.
# Learn and Tell: Learning Priors for Image Caption Generation
## 5 Conclusions  
In this paper, we proposed a novel priors-based attention neural network (PANN) for the captioning task, which enhances conventional attention mechanisms. The PANN explores the probabilities being mentioned for region proposals and the part-of-speech clues for the word to be generated to help the attention module to extract needed information from numerous regions and heterogeneous feature sets, which further improves the performance of the captioning model. Extensive experiments conducted on the MS-COCO dataset demonstrated the effectiveness of our proposed method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **BLEU-1** & **BLEU-4** & **M** & **R** & **C** & **S** \\ \hline LSTM [6] & 76.7 & 34.2 & 25.2 & 52.7 & 109.6 & 18.3 \\ LSTM [6] + PANN (Ours) & **77.2** & **35.2** & **25.7** & **54.3** & **111.6** & **19.1** \\ \hline Att2 all [7] & 77.4 & 36.2 & 27.1 & 55.8 & 117.6 & 19.2 \\ Att2 all [7] + PANN & **78.7** & **37.8** & **28.6** & **57.7** & **120.3** & **21.6** \\ \hline Up\_Down [4] & 77.4 & 35.7 & 26.2 & 56.3 & 114.7 & 18.8 \\ Up\_Down [4] + PANN (Ours) & **78.1** & **36.7** & **26.8** & **56.5** & **118.4** & **19.3** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of three “base” models and “base” models with PANN on the MS-COCO “karpathy” test split. All of these experiments were trained using the same method using XE-loss. All values are reported as a percentage (%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters** & **Speed** \\ \hline LSTM [6] & 12.87M & 41.31s \\ LSTM [6] + PANN (Ours) & 14.64M & 43.27s \\ \hline Att2 all [7] & 196.97M & 53.64s \\ Att2 all [7] + PANN & 198.76M & 55.28s \\ \hline Up\_Down [4] & 149.94M & 67.43s \\ Up\_Down [4] + PANN (Ours) & 160.56M & 70.33s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Complexity analysis result of three “base” models and “base” models with PANN on the MS-COCO “karpathy” test split, where M is short for million and s is short for seconds.  
Figure 9: Performance affected by the trade-off factor of \(\lambda\). In the above figure, we show 4 main metric curves obtained by different values of \(\lambda\) sampled from 0.1 to 0.5, with a separation distance of 0.05. The above figure (**a**–**d**) show the results under metrics BLEU-4, CIDEr-D, ROUGE-L, SPICE respectively.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments, [PERSON] and [PERSON] performed the experiments and analyzed the data, [PERSON], [PERSON], and [PERSON] wrote the paper. All authors interpreted the results and revised the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Natural Science Foundation of China (Grants No. 61971296, U19A2078), SCULuzhou Cooperation Project (Grant No. 2019 CDLZ-07).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 3 Meta-Heuristic Algorithms for Energy-Efficient IoD Path-Planning Description
### Meta-Heuristic Algorithms  
In this subsection, we will briefly explain some meta-heuristic approaches that will be utilized to solve the IoD path-planning problem. In terms of prospects, each algorithm has potential applications in the context of the IoD. PSO, IPSO, GA, and ABC can be applied to optimize path planning for the IoD in various scenarios, including disaster relief, surveillance operations, and environment monitoring. These algorithms can contribute to enhancing the efficiency and sustainability of UAV missions within the IoD formation.
## 3 Mobile Antenna Design and Geometry  
Figure 1a shows the layered view of a flexible four-element MIMO antenna geometry with the common conducting ground at the center. A flexible polyamide substrate (dielectric constant of 3.5 and loss tangent of 0.0027) is used with diagonally placed antenna elements at the corner of a common ground plane. Figure 1b shows the top view where it is observed that the dimensions of the antenna are 145 \(\times\) 70 \(\times\) 0.2 mm\({}^{3}\). Four conductive antenna elements (diagonally placed) with the common center-ground and fed using a discrete port (A: feeding point and B: grounding point) are all printed on the top side of the substrate.  
The detailed view of the single element structure of the antenna is illustrated in Figure 1c, which consists of four octagonal rings embedded between two opposite sides of a T-shaped conductive layer surrounded by inverted angular edge cut L-shaped and E-shaped structures. The inset view of the solid octagonal shape located on a T-shaped branch and four hollow octagonal shapes on the right side of a T-shaped branch is shown. The latter hollow octagonal shapes are connected with two rectangular stubs separated by a distance of 0.8 mm to the vertical side edges. All the edges of the inverted L-shaped strips and E-shaped strip are cut at an angle of 45\({}^{\circ}\). The dimensions are indicated in the figure itself, where the units are in terms of millimeters (mm).  
While designing the proposed flexible single-element antenna, a few steps are followed to reach the final design, as shown in Figure 2, and their corresponding reflection coefficient results are shown in Figure 3. Initially, an inner E-shaped strip having angular cuts at the edges (step 1) is adopted. The antenna in this case shows a single band performance at a resonance of 3.8 GHz. In the second step, an additional angular edge cut inverted L-shaped conductive strip along with a vertical strip embedded with a solid octagon is inserted for enhancing the frequency response where dual-band performance is observed; however, the lower band is not in the WLAN/5G regime. Thus, in the third step, a horizontal line is incorporated above the E-shaped strip, which is interfaced with the top edge of the E-shaped strip through three vertical conductive strips. The same leads to improved frequency response; however, the wideband performance is still not achieved. So, in the final stage, four octagonal rings are inserted for achieving bandwidth and reflection coefficient performance to cover the essential WLAN/5G bands. Thus, the final step shows the simulated \(-\)10 dB impedance bandwidth spanning from (84.67%) 2.37-5.85 GHz.  
Figure 1: Four-element MIMO antenna geometry: (**a**) Layered view; (**b**) Top view; (**c**) Inset view of antenna element (all dimensions in mm).
## 4 Methodology
### Calibration of the AR Environment
#### 4.4.3 QR Code Reading  
An alternative to the visual estimation of the drone's pose is to rely on QR codes pasted on the physical drones. The HoloLens provides a built-in capability to detect and read QR codes robustly. Hence, by pasting a QR code on top of the drone, the HoloLens can detect its starting position (analogous to step 1 in the manual calibration process) automatically, with no need of human intervention. While the air-tap determines a single point and hence provides only position information, the QR code is an object, and its detection can provide both position and orientation. Hence, in conjunction with the heading provided by the drone's compass, both steps of the manual calibration process can be completed in a single QR code read.  
Naturally, a QR code pasted on a drone's top is easily readable at short distances (perhaps a maximum of 2 m), i.e., while the drone is landed and the user stands close to it. Hence, while this method can provide fast and automated calibration, it cannot be used for correcting the drone's position during flight and offsetting IMU drift, as is the case with Section 4.4.2.
# Renyi Entropy-Based Adaptive Integration Method for 5G-Based Passive Radar Drone Detection
## 6 Simulations  
Simulations were carried out to prove the importance of selecting a long enough time interval for radar detection of drones and evaluate the aforementioned entropy-based method of measuring the content contained in 5G NR signals.  
The simulation parameters are listed in Table 2 and were chosen to coincide with real-life experiments described in the further part of  
Secondly, synthetic signals compliant with the 5G NR standard were generated. The filling varied from 0 to 100% of the downlink channel slots, with random positions in the time-frequency allocation grid. The signal parameters were set as follows: channel bandwidth: 40 MHz, sampling rate: 61.44 MHz and subcarrier spacing = 30 kHz. A time delay and Doppler shift were added to simulate the reflection of a single moving target. White Gaussian noise was added with a power level referring to SNR values of \(0,10,20,30\), and 40 dB. The time interval for each calculation was 20 ms. Then, the average value of the Renyi entropy for each filling and SNRs was obtained. Results are presented in Figure 13. It can be seen that the value of the entropy increases with signal filling.  
The same simulated echo signals were applied to measure the probability of detection (\(P_{d}\)) in 5G-based passive radar as a function of signal filling. Signals were processed according to [9] with the following steps: uplink cancellation, signal filtering and clutter removal, CAF calculation and target detection (using classical CFAR algorithm). Results are  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name of the Parameter** & **Value** \\ \hline Center Frequency & 3.44 GHz \\ \hline EIRP (\(P_{t}G_{t}\)) & 73 dBm \\ \hline Receiver antenna gain \(G_{r}\) & 10 dBi \\ \hline Threshold \(D_{0}\) & 11 dB \\ \hline Integration time \(T_{\text{int}}\) & 20 ms \\ \hline Total losses \(L_{0}\) & 10 dB \\ \hline Effective noise temperature of the receiver \(T_{0}\) & 493 K \\ \hline Radar cross-section \(S_{0}\) & 1, 10, 50, 100 m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulated network parameters.  
Figure 14: Plot range—integration time.  
The results clearly show a leverage of the signal filling on the possibility of target detection. In line with expectations, the higher the resource allocation, the better the detection performance. Furthermore, the differences between high SNRs (especially those greater than 20 dB) are marginal, and one can assume that such values can be relatively easily achieved in short-range 5G-based PCL applications. The plots also show the obvious dependence of the detection probability \(P_{\mathrm{d}}\) on the assumed \(P_{\mathrm{fa}}\).  
Simulations showed the importance of sufficient integration time and resource allocation, especially for low-RCS target detection. It also evaluated the Renyi entropy as the right measure of signal resource allocation.
# Tuning the Cell and Biological Tissue Environment through Magneto-Active Materials
## 2 Mechanically Mediated Biological Processes
### Mechanical Effects on Bone Development and Healing
Besides, cancer cells exhibit gap junctional communication, which suggest cell-cell coupling and multicellular organisation. Local tissue remodelling caused by collective invasion, might contribute to invasive tumour growth and consecutive tissue destruction. In experimental models of metastasis, cancer cells survive in the blood steam and generate lung metastases [47]. In certain cancers, such as melanoma, cancer cells travel inside lymphatic vessels and increase the risk for lymphatic metastasis [48]. Because intact and coordinated cell-cell junctions are critical for mass invasion, the gain or loss of cell-cell coupling determines whether cancer cells move collectively, individually, or use transition patterns. Consequently, three-dimensional migration in tumour cells can develop through the mesenchymal phenotype (elongated cell shape) or amoeboid type (rounded cell shape). This determines the migration mechanisms which, in the mesenchymal cell, present high adhesion, high polarisation and protrusions rich in actin; while in the amoeboid they present low adhesion, low polarisation and the formation of blisters or 'blebs' in the cell membrane [49] In this context, the role that the mechanical processes play is even more relevant, since it can lead to transitions between both migration mechanisms. For example, the application of vertical compression in cells where adhesion with the substrate is low, induces a transition from mesenchymal to amoeboid migration [50]. Therefore, it can be concluded that the physical properties of the substrate or ECM, highlighting the mechanical properties among them, together with the intrinsic properties of the cells themselves, regulate the mechanisms and modes of migration [51]. This influence of the mechanical properties of the cell environment is also of great relevance for proliferation processes. The understanding of these aspects would be of great importance to advance in the control of cancerous processes, such as tumour progression and metastasis.
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 6 Conclusions and Future Work
### Future Work  
As discussed in Section 5.3, the proposed method might not perform as well on images that lack sky regions or contain darker elements. Therefore, we plan to make improvements specifically for such images. The improvements will include, but are not limited to, refining the estimation of atmospheric light by considering not only low polarization values as the sole criterion but also leveraging certain characteristics of the scene's dark channel image for a more accurate estimation. In addition, we will investigate how linear polarization information from other frequent characteristics, such as strongly polarized surfaces, may be used to estimate atmospheric light \(A\).  
Dehazing the sky region is required because fog might cause color changes in this region. However, when processing specific images, our approach may occasionally yield unrealistic dehazing effects in the sky region as shown Figure 10c. We relate this phenomenon to our direct use of the brightness from the sky region with the lowest polarization as an estimate of the atmospheric light intensity, which may have resulted in an increased dehazing impact in the sky region, according to our study. As a result, we will examine specific approaches to dealing with the sky region to ensure that the outcomes are more realistic.  
Conceptualization, S.L. and Y.L.; methodology, S.L. and B.W.; software, S.L.; validation, B.W., Y.W. and H.L.; formal analysis, Y.W.; investigation, H.L.; data curation, S.L., H.L. and Z.Z.; writing--original draft preparation, S.L.; writing--review and editing, Y.L. and B.W.; visualization, S.L., B.W. and Y.W.; supervision, Y.L. and S.L.; project administration, S.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Fundamental Research Funds for the Central Universities (grant number 3132023507) and Liao Ning Revitalization Talents Program (grant number XLYC2001002).  
Not applicable.  
The authors would like to thank [PERSON] from Changchun Orion Opto-electronics Technology Co., Ltd. for the assistance provided during the usage of the visible light polarization camera.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
DCP Dark Channel Prior  
DOP Degree of Polarization  
PSNR Peak Signal-to-Noise Ratio  
SSIM Structural Similarity Index