esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Survey of Extended Resolution Effects for SAT Solvers on the Pigeonhole Principle
## 6 Conclusions  
In theory, extended resolution enables polynomial solving of the pigeonhole principle. However, few works have clarified the effectiveness of extended variables on today's CDCL (i.e., general resolution-based algorithm) SAT solvers. We investigated the \"practical effect\" of the extended resolution on the pigeonhole principle by prioritizing the extended variables in the solver's search in two ways. Combining with these methods, the PHP instances with the extended variables were solved within a shorter processing time by MiniSAT 2.2 and Glucose 3. While fully extended instances had clearer performance gain, partially extended ones had moderate effects. Even though it is not clear our methods could fully utilize the extended variables, our results indicate that we cannot attain good performance gain on the partially extended instances. Note that the method of extension is under the ideal condition, and there cannot be definite guidelines on how to add the extended variables in practice. Although it is difficult to add appropriate auxiliary variables and utilize them, our results exhibit the possibility of performance enhancement by extension. We believe that the facts in this paper create a stir to the practical usefulness of the extended resolution for instances other than the PHP.  
This work was supported by JSPS KAKENHI Grant Number JP17K12742 and JP21K12023.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The datasets generated during the current study are not publicly available but are available from the corresponding author on reasonable request.  
**Acknowledgments:** We appreciate all the researchers giving helpful comments on this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 4 Experiment Evaluations
### Data Description  
The proposed scheme is evaluated on the dataset using Wi-Fi RSS measurements collected on the third and fifth floor of the library environment, as shown in Figure 5, at Universitat Jaume I in Spain [30]. The total area of both floors is about 308.4 m\({}^{2}\). All the samples were collected by a trained person using a Samsung Galaxy S3 smartphone over the span of 15 months. During the first month, 15 offline datasets were collected, and we utilized them to train our model. The model performance is evaluated on the 75 online databases that were collected during the whole 15 months. Each floor contains 24 reference points and 106 test points.
# Holographic 3D Display Using Depth Maps Generated by 2D-to-3D Rendering Approach
## 4 Discussion  
In Figure 5a, objects A and B are the same size, but are placed at different shooting distances. Thus, their sizes in the captured 2D image are different, \(siz_{A}\) and \(siz_{B}\), respectively. For simplification, only the image sizes in the \(x\)-direction \(x_{A}\) and \(x_{B}\) are discussed. The pixel value of the CGDM changes linearly along the vanishing line. Thus, the CGDM values for these two objects, \(depth_{A}\) and \(depth_{B}\), are proportional to their sizes. From Equation (1), the relationship between the real shooting distance and the pixel value of the CGDM can be expressed as:  
\[(depth_{A}-depth_{B})\ \propto\ (x_{A}-x_{B})\ =-fx\frac{dis_{A}-dis_{B}}{dis_{A} dis_{B}} \tag{9}\]  
When \(\Delta depth\) is infinitely small, Equation (9) can be rewritten as:  
\[k=\frac{\Delta depth}{\Delta dis}\propto\frac{1}{dis^{2}} \tag{10}\]  
As the shooting distance increases, each increment in the CGDM gray scale represents a larger change in depth, as shown in Figure 5b. The human eye's perception of depth information wanes as the observation distance increases. Thus, human factor engineering is fully considered during the design of the CGDM.  
Figure 4: Reconstructions of 3D scenes by 2D-to-3D rendering method at depths of 155 mm, 160 mm, 165 mm and 170 mm, respectively.  
Figure 5: The relationship between shooting distance and gray scale in CGDM. (**a**) Variation of CGDM with shooting distance (same size objects). (**b**) Normalized gradient of CGDM variation with shooting distance.  
In this study, the average processing time for the image classification, CGDM calculation, and CGH generation are 9.67, 87.33, and 1201.67 ms, respectively. The total calculation time is 1298.67 ms, which limits the application of the proposed approach in dynamic 3D display. Considering that both the calculation of CGDMs and the generation of CGMs can be realized by deep learning [43], further optimization of the calculation time would be practical. Combining the proposed method with deep learning network is the future direction of the work.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 2 Preliminaries
### Hyperchaotic System  
Since the hyperchaotic system contains more than two positive Lyapunov exponents, its chaotic properties are better, and its trajectory is more unpredictable. When a hyperchaotic system is applied  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Chaotic Maps**} & \multicolumn{11}{c}{**Thresholds**} \\ \cline{2-13}  & **0.02** & **0.04** & **0.06** & **0.08** & **0.10** & **0.12** & **0.14** & **0.16** & **0.18** & **0.20** & **0.22** & **0.24** & **0.26** \\ \hline The LFHCM & 0.36 & 0.55 & 0.66 & 0.71 & 0.75 & 0.76 & 0.81 & 0.82 & 0.84 & 0.85 & 0.84 & 0.86 & 0.85 \\ \hline The Logistic map & 0.35 & 0.47 & 0.53 & 0.53 & 0.55 & 0.56 & 0.57 & 0.59 & 0.27 & 0.56 & 0.54 & 0.53 & 0.54 \\ \hline The Fraction map & 0.15 & 0.23 & 0.28 & 0.33 & 0.37 & 0.38 & 0.41 & 0.42 & 0.45 & 0.47 & 0.46 & 0.47 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Approximate entropy (ApEn) values of 1D chaotic maps.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistical Tests** & \(p\)**-Value** & **Result** \\ \hline Frequency & 0.3699 & Pass \\ Block frequency & 0.7872 & Pass \\ Cumulative sums & 0.6584 & Pass \\ Runs & 0.2645 & Pass \\ Longest run & 0.6385 & Pass \\ Rank & 0.2022 & Pass \\ Fast fourier transform & 0.3492 & Pass \\ Non-overlapping template & 0.6286 & Pass \\ Overlapping template & 0.5575 & Pass \\ Universal & 0.6917 & Pass \\ Approximate entropy & 0.5434 & Pass \\ Random excursions & 0.5753 & Pass \\ Random excursions variant & 0.5349 & Pass \\ Linear complexity & 0.4639 & Pass \\ Serial & 0.3972 & Pass \\ \hline \hline \end{tabular}
\end{table}
Table 3: NIST SP800-22 Revla test results of binary streams generated by LFHCM.  
to image encryption, the cryptosystem has a larger key space. The 4D hyperchaotic system utilized in this paper [29] is defined as follows:  
\[\left\{\begin{array}{l}\dot{\mathrm{x}}=\lambda(\mathrm{y-x})+\beta_{1}\mathrm{w }\\ \dot{\mathrm{y}}=\alpha\mathrm{x-xz}+\beta_{2}\mathrm{w}\\ \dot{\mathrm{z}}=-\sigma\mathrm{z+xy}+\beta_{3}\mathrm{w}\\ \dot{\mathrm{w}}=-\delta\mathrm{x}\end{array}\right., \tag{5}\]  
where \(\lambda\), \(\alpha\), \(\sigma\), \(\delta\), \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\) are control parameters of the system. Only when \(\lambda=35\), \(\alpha=35\), \(\sigma=3\), \(\delta=5\), \(\beta_{1}=1\), \(\beta_{2}=0.2\), and \(\beta_{3}=0.3\), the system exhibits hyperchaotic behavior. Moreover, the phase trajectory of the system is displayed in Figure 3.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 2 Frequency-Reconfigurable Antennas
### Mechanically Frequency-Reconfigurable Antennas
#### 2.2.2 Antennas Based on Metallic Micromesh  
In Reference [63], a semitransparent and flexible antenna which consists of a series of tortuous micromesh structures is presented. The proposed antenna can be stretched up to 40% without any problem. The mesh parts are made of copper and are placed on PDMS. The antenna's frequency reconfigures from 2.94 GHz to 2.46 GHz by increasing the tensile strain through mechanically elongating the meandering line of the antenna.
# Caroenoid Production from Microalgae: Biosynthesis, Salinity Responses and Novel Biotechnologies
## 4 Potential Applications of Internet of Things (IoT) in Carotenoids Production
By combining the data from sensors with simulation models, the IoT platform can predict growth trends and provide cultivation suggestions to maximize biomass production [102]. Afterwards, automation units of the IoT are essential to achieve condition debugging at the right time after data analysis, such as a suitable feeding strategy [103]. As for cultivation with salt-stress treatments, the IoT will facilitate picking the right timepoint to conduct salinity pressure to adequate microalgal cell density. For the downstream processing of carotenoid production, it is particularly critical to distinguish microalgal cells from other contaminants in the culture medium. Since traditional turbidimeters detect both microalgal cells and pollutants, the measurement results are often not accurate enough [104]. As fluorescence only comes from chlorophyll pigment of alive cells, convenient chlorophyll fluorescence sensors have been designed and widely applied in in situ detection of microalgal cells with high efficiency and accuracy [105; 106]. Take the astaxanthin production from _C. zofingiensis_ as an example. _C. zofingiensis_ can synthesize astaxanthin in adversity and store it in lipid droplets [55]. As it can take up glucose for rapid growth, it is more likely to be contaminated by bacteria during the actual cultivation process. Chlorophyll fluorescence sensors can visibly separate microalgae from bacteria and cell debris, which can be applied in quantification of microalgae. Additionally, bioproducts content can be non-invasively detected _in situ_, such as with a triacylglycerides (TAG) observer [107]. As for cultivation with salt-stress treatments, the IoT will facilitate in distinguishing cell debris under salt stress to accurately harvest and quantify microalgal  
Figure 4: Characteristics of IoT and their potential utilizations in microalgae biorefinery. UP, upstream processing; DOWN, downstream processing. cells, and a TAG observer can also display astaxanthin content to achieve preliminary evaluation of productivities. The IoT, as an intelligent system, can also choose the best extraction method after the target product has been separated, which saves time while ensuring purity and extraction efficiency.
## 3 Results  
### LSP Characteristics of Three Products  
#### 3.1.1 Spatial Patterns of LSP of LAI/FVC/GPP  
Using the LAI/FVC/GPP data in 2018, we display the spatial patterns of the LSP in the TRSR (Figure 5). In general, the patterns are elevation-dependent, and vegetation growth in northwestern parts of the study areas (i.e., Zhiduo, Qumalai, and Golmud counties) occurs a little later than the other areas. Most pixels' SOS there begin after 135 DOY, while EOS happen before 275 DOY. Eastward from the Zhiduo county, the SOS advances from May to April, and the EOS postpones from September to October. In other words, with the increasing elevation in the TRSR, the SOS delays from the southeast to northwest, while EOS is in advancement.  
Despite the similar spatial pattern exhibited by the three various products, there are still noteworthy variances in the estimation of LSP metrics from different products (Table 2). In the estimations of SOS, 42.22% and 39.80% of the pixels indicate that the phenological differences of LAI_FVC and FVC_GPP are more than 10 days; while in the EOS estimates, these percentages are 41.92% and 32.62%, respectively. In brief, at least 35% of the pixels in the study area represent a significant difference between FVC and other products in LSP estimation, which are mainly located at the Golmud, Zhiduo, Qumalai, and Gonghe counties. However, 95.38% and 85.50% of the pixels prove that the phenological differences between GPP and LAI are within 10 days, respectively. This phenomenon implies that the LSP estimations by GPP and LAI enjoy better agreements in the vast majority of the study area (more than 90%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Phenological**} & \multicolumn{3}{c}{**SOS**} & \multicolumn{3}{c}{**EOS**} \\ \cline{2-7}
**Differences** & **LAI\_FVC** & **FVC\_GPP** & **GPP\_LAI** & **LAI\_FVC** & **FVC\_GPP** & **GPP\_LAI** \\ \hline
0–5 & 28.51\% & 29.41\% & 74.99\% & 31.45\% & 37.34\% & 61.02\% \\
5–10 & 29.27\% & 30.79\% & 20.39\% & 26.63\% & 30.04\% & 24.48\% \\
10–15 & 20.98\% & 23.50\% & 3.59\% & 19.75\% & 19.24\% & 10.25\% \\
15–20 & 11.89\% & 9.93\% & 0.72\% & 9.32\% & 7.05\% & 2.49\% \\ \(>\)20 & 9.35\% & 6.37\% & 0.31\% & 12.85\% & 6.33\% & 1.76\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistical distribution of absolute difference (days) of SOS and EOS between every two products.  
Figure 4: Comparison framework of (**a**) tendency and (**b**) correlation among three products.
## 2 Model Test
### Test Scheme
#### 2.1.2 Test Platform and System  
The model platform size was 4 m \(\times\) 1.5 m \(\times\) 1.2 m, as shown in Figure 1. During the heating procedure, the tunnel was initially filled with soil. The filled soil was made up of river sand, clay, quartz sand, gravel, and fine sand with the following mass ratio: 1:4:2:1.5:1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Similarity Ratio** & **Calculation Method** \\ \hline Length & \(C_{I}\) & 30 & Comprehensive factor method \\ Density & \(C_{\rho}\) & 1 & - \\ Heat conductivity & \(C_{\lambda}\) & 1 & - \\ Specific heat & \(C_{c}\) & 1 & - \\ Temperature & \(C_{T}\) & 1 & - \\ Thermal diffusivity & \(C_{a}\) & 1 & \(C_{a}=\frac{C_{\lambda}}{C_{\lambda}C_{\epsilon}}\) \\ Time & \(C_{t}\) & 900 & \(C_{t}=\frac{C_{\lambda}^{2}}{C_{\epsilon}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model scales.  
Figure 1: Schematic diagram of the model test: (**a**) plan of the model platform and (**b**) A-A cross section.  
Figure 1 presents the measuring point arrangement of the test model. Nine measuring points were set every 50 cm along the axis of the tunnel, numbered \(Z_{1}\)-\(Z_{9}\) along the route. At measuring points \(Z_{2}\), \(Z_{3}\), \(Z_{5}\), and \(Z_{7}\), four groups of measuring points were arranged along the radial direction of the tunnel, namely, \(A_{E}\), \(B_{E}\), \(C_{E}\) and \(D_{E}\). At Section A-A, six measuring points were arranged above and below the tunnel position, among which the spacing of the five measuring points near the tunnel was 5 cm and that of the other was 10 cm, as shown in Figure 1b. This deployment was reasonable and could meet the requirements for detecting the distribution range of temperature changes.  
In this test, a temperature loading and monitoring system was designed, as shown in Figure 2. Hot water circulation was used to load the temperatures, and insulation was placed on the bottom of the heating pipes to stop heat loss. Pt100-type platinum resistance thermistors were used to measure the temperature with an accuracy of 0.1 \({}^{\circ}\)C. The temperature was read (every 10 min) directly with a MEACON multichannel temperature recorder. The measuring points were arranged at the inlet and outlet to monitor the water temperature. To obtain a stable temperature boundary condition, the temperature loading system only needed to adjust the water temperature.
## 3 Results and Discussion  
### Cell Concentration (Isolated Species) and Urease Activity  
According to the BLAST analysis result, the identified species are shown in Table 5. Urease activity is closely related to the bacterial cell growth and it was revealed that a high bacterial cell concentration enhanced the amount of calcite precipitated by the MICP method. Moreover, it was concluded that the urea hydrolysis rate was directly proportionate to the concentration of bacteria, which is an important factor for the success of the MICP application [23; 24]. On the basis of these findings, the generation of urease from the isolated species could be a growth correlated framework that favors for sand solidification. Figure 3a represents the cell growth, whereas Figure 3b,c illustrate the urease activity of the G1, G2, and G3 species as whole-cell, cell pellet, and supernatant, respectively. From Figure 3a, it can be observed that initially the bacterial cell growth increased with time and then  
\begin{table}
\begin{tabular}{c  
became stable and, conversely, the urease activity increased initially and then decreased, making a bell-shaped profile in between 3-6 days for the G1, G2, and G3 species.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Code Name** & **Species** \\ \hline G1 & _Microaccous_ sp. \\ G2 & _Pseudoalteromonas_ sp. \\ G3 & _Virghiacillus_ sp. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Identified strains from the collected soil sample.  
Figure 3: The results showing the (**a**) cell growth and (**b**) urease activity of the isolated strains including the (**c**) cell pellets and supernatant.  
It was shown that the urease activity of the bacterial whole cell, cell pellet, and supernatant was closely related to the presence of protease in the cell [25]. However, it is well established that urease activity of different bacteria species depends on each species, as well as some environmental factors, which is similar to this study. Earlier studies have also reported that the urease from a few species such as _Pararhodobacter_ sp. Appeared to have very high stability with extensive cultivation [26]. However, our results revealed that the G2 species of bacteria showed a dissimilar tendency for urea hydrolysis even under extended cultivation compared to the G1 and G3 species. The possible reasons for varying the urease activity might be osmotic pressure and resuspension of the cell, which might release enzyme into the solution from cells. However, further study needs to investigate the actual reasons.  
The findings of this study are consistent with earlier findings and could contribute as useful information for the sand solidification process by MICP, considering the bacteria population increase via centrifuge and/or applying the re-injecting method of bacteria before urease activity becomes null.
## 2 Materials and Methods
### Influence of the Plasma Species H\({}_{2}\)O\({}_{2}\) on Cell Adhesion  
To find out more about the mechanism underlying the action of PAM on the HA pericellular coat and the impairment of the cell adhesion, we incubated HaCaT cells in DMEM supplemented with 150 mg/L hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) for 3 h, stained cells from the supernatant for HA, and measured the HA fluorescence intensity by flow cytometry. In addition, we monitored the living cell adhesion of HaCaTs in DMEM containing H\({}_{2}\)O\({}_{2}\) (H2O2) over 1.5 h using the xCELLigence system. As shown in Figure 6, both the HA pericellular coat and the adhesion of the cells were impaired by H2O2, but not as strong as by PAM. The fluorescence signal of labeled HA was significantly lower for cells in H2O2 vs. DMEM, but not as low as for PAM60 (mean fluorescence channels 139, 107, 113, and 75 for DMEM, H2O2, PAM30, and PAM60, respectively, see also Figure 1). The cell adhesion was significantly reduced in H2O2 vs. DMEM (normalized cell index 0.231 vs. 0.397, respectively). We could deduce from this  
Figure 4: Online monitoring of HaCaT cell adhesion in PAM30, PAM60, and control media up to 1.5 h. Note that PAM impaired the cell attachment process significantly and to a greater extent than the HAdase-cleavage did. (xCELLigence Real-time Cell Analysis, normalized cell index; mean \(\pm\) s.e.m., n = 3, Mann–Whitney test, **\(p\) < 0.01 PAM30 vs. DMEM, #\(p\) < 0.01 PAM60 vs. DMEM). Abbr.: DMEM: Dulbecco’s modified Eagle’s medium, PAM30: 30 s plasma-treated DMEM, PAM60: 60 s plasma-treated DMEM, AAM: 60 s argon gas-treated DMEM, HAdase: DMEM with hyaluronidase.  
Figure 5: Cell viability of HaCaT cells after cultivation in the different PAM or control media for 48 h. The cell viability was decreased in PAM depending on the treatment time used for generation of PAM (mean \(\pm\) s.e.m., n = 3, unpaired \(t\)-test, *\(p\) < 0.05, ***\(p\) < 0.001). Abbr.: DMEM: Dulbecco’s modified Eagle’s medium, PAM30: 30 s plasma-treated DMEM, PAM60: 60 s plasma-treated DMEM, AAM: 60 s argon gas-treated DMEM, HAdase: DMEM with hyaluronidase.  
that the adhesion of HaCaT cells was impaired by H2O2 but not as strongly as by PAM30 and PAM60 (normalized cell index nearly 1.9 for both, see also Figure 4).  
With these first additional investigations, we could show that the effect of PAM on HaCaT cells was not solely mediated by the relatively longer-lived reactive species H\({}_{2}\)O\({}_{2}\) contained in PAM. The impairment of the cells in H2O2 was not as strong as in PAM. Future experiments will shed light on further species involved in the mechanism of action of the complex mixture of PAM on the HA coat.
# Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition
## 4 Experiments
### Comparison with State-of-the-Art Methods  
Our MTC-Former is evaluated against state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120, and InHARD datasets The symbol \"-\" denotes cases where results were not reported in the original papers. As shown in Table 4, on the NTU RGB+D dataset, our MTC-Former with four-stream fusion achieves 93.1% (X-Sub) and 97.1% (X-View) accuracies, achieving the best performance. On the NTU RGB+D 120 dataset, compared to GCN-based methods [14], our MTC-Former achieves competitive results. Moreover, our MTC-Former outperforms transformer-based methods [19; 22], CNN-based methods [43; 44], and SIT-MLP [45]. Compared with the lightweight model [14; 18; 19; 44; 45; 46], our model demonstrates significant performance advantages. In summary, our method achieves high performance while maintaining a low model parameter count.  
To further evaluate the performance and generalization ability of the proposed method in different scenarios, we conducted comparisons with other state-of-the-art methods on the industrial human action recognition dataset InHARD. As shown in Table 5, the proposed method achieves a Top-1 recognition accuracy of 88.3% on the InHARD datasets, outperforming transformer-based methods TSM and VideoSwin. Furthermore, our approach utilizes skeleton-based modality for human action recognition, which not only delivers superior recognition performance but also maintains lower parameter count. These results demonstrate that our method exhibits excellent generalization capability in industrial production scenarios.  
Figure 12: Confusion matrix of activities on the NTU RGB+D 120 dataset: (**a**) denotes the confusion matrix across 120 classes; (**b**) represents the six action categories with higher classification accuracy, corresponding to action categories 95 to 100, which involve body movements; (**c**) represents the six action categories with lower classification accuracy, corresponding to categories 71 to 76, which involve detailed object-interacted movements.
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 4 Experiments and Discussion
### Denser Representations
#### 4.3.1 Geometric Shapes of Feature Representations  
**The representations learned by ST-GCN after self-distillation have more complex geometric shapes that are linearly separable.** Different ways for projecting the extracted representations to 2D space were tried, namely principal components analysis (PCA) [24], t-distributed stochastic neighbor embedding (T-SNE) [25; 26] and \(k\)-nearest-neighbor-graphs (\(k\)-NNG) [27] (Figure 8). PCA generates clusters with mixed classes. The projections by T-SNE and \(k\)-NNG are more impressive; actions with similar semantics such as drinking water and making a phone call are close, and vice versa. Because PCA tends to draw the clusters with linear separable features well, this demonstrates that the representations learned by ST-GCN have more complex geometric shapes.