esa-sceva/satcom-chunk-collection huggingface dataset:
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 4 Experimental Results and Analysis
### A Detailed Example for the Proposed Method
Figure 8h,i shows the marked encrypted image when the pure embedding rate are 0.5 bpp and 0.9 bpp, respectively. In the proposed method, if only the data-hiding key is available, then the embedded data can be extracted from encrypted domain without any error. If only the encryption key is available, the reconstructed image after decryption is obtained, as shown in Figure 8j,k, which are reconstructed images after decryption. We can see that the two reconstructed images are approximate replications of the original ones, which is indicated by PSNRs of 85.98 dB and 74.25 dB, respectively. In fact, the reconstructed image is overwhelming similar to the original image in our proposed method, because only some edged pixels of the reconstructed image are changed compared with original value. If the data hiding key and encryption key are all available, then the recipient can not only extract the data without any error but also obtain the reconstructed image which is same as original image, as shown in Figure 8l. Obviously, in this case, the PSNR of the reconstructed image is approximating +\(\infty\). In order to obtain the best PSNR of the decrypted image with only the decryption key for a different given embedding rate, the optimal parameters (\(m^{*}\), \(T^{*}\)) need to be chosen. As shown in Table 3, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Direction** & **Original Image** & **Figure 8d** & **Figure 8e** \\ \hline horizontal & 0.9688 & \(-\)0.0155 & 0.0077 \\ vertical & 0.9842 & 0.0044 & 0.0242 \\ diagonal & 0.9565 & \(-\)0.0104 & 0.0027 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation coefficients of two adjacent pixels in the original image and Figure 8d,e. for the image Lena, the optimal parameters are obtained under a different given embedding rate. From Equation (18), we can know that if the auxiliary information is smaller, the PSNR of the decrypted image will be higher. Generally, on the premise that the pure embedding rate is higher than given embedding rate, the larger the value of \(m\), the fewer pixels need to be marked (i.e., the pixels in \(I_{W}^{*}\)), which means when the amount of auxiliary information is smaller, then the PSNR of the reconstructed image after decryption is higher. It is observed from Table 3, when the given embedding rate increases gradually, the optimal value of \(m\) decreases in order to ensure that the pure embedding rate is higher than the given embedding rate.
# Dynamic Group Recommendation Based on the Attention Mechanism
## 4 Experiments and Analysis
### Experimental Settings
#### 4.1.2 Evaluation  
We used protocol leave-one-out, which is widely used to evaluate top-k recommendation performance, to separate the training set from the test set. Specifically, for each user or group, we randomly deleted one of its interactions for testing. In our experiment, we randomly selected 100 items without user or group interaction, and ranked the test items in these 100 items. We used two indicators--hit ratio \((\text{HR})\) and normalized discount cumulative gain (NDCG).  
HR is a commonly used indicator for calculating the balance recall rate in the top-k recommendation. The calculation equation is:  
\[\text{HR@K}=\frac{Number\ of\ Hit@K}{\text{GT}}. \tag{17}\]  
The denominator GT is the set of all tests, while the numerator represents the sum of the number of test sets belonging to each user's top-K list.  
NDCG is used to evaluate the accuracy of the top-K recommendation list. The calculation equation is:  
\[\text{NDCG@K}=\frac{\text{DCG}}{\text{IDCG}}, \tag{18}\]  
\[\text{DCG}=\sum\
olimits_{i=1}^{k}\frac{2^{rel_{i}}-1}{log_{2}(i+1)}, \tag{19}\]  
where \(rel_{i}\) represents the relevance of the recommendation result for location \(i\), while k stands for the size of the recommendation list. IDCG represents a list of the best recommended results returned by a user of the recommendation system (IDCG stands for DCG in order of rating). Use IDCG for normalization to get NDCG, the value of NDCG is between 0 and 1. The closer the result is to 1, the better the recommendation effect.
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
## 3 Design of Reconfigurable P-DFTFB
### Reconfiguration  
The reconfiguration of P-DFTFB is realized by changing the number \(M\) of the sub-bands in FB. The selection and configuration of \(M\) is key to the reconfigurable filter banks. In [20], there is an upper confidence bound (UCB)-based decision making stage to select the sub-bands number and decide the central frequency of each sub-band, which has high complexity. The decision-making method based on learning algorithms in [21] consumes a lot of time to learn. So, we propose a simple decision-making method as Algorithm 1 in Section 4.2 to select the sub-bands number \(M\) according to the last sensing result.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 2 Related Work
### Standards for Indoor Positioning, Mapping and Navigation
#### 2.3.5 IFC of BuildingSMART  
The Industry Foundation Classes (IFC), originally the buildingSMART open standard for BIM, has been adapted as the ISO 16739 international standard (ISO 16739-1:2018 [93]), which specifies a data schema (represented as an EXPRESS schema) and an exchange file format structure. The exchange format consists of definitions that are required during the life cycle phases of buildings and that are required by the various disciplines involved within the life cycle phases. The IFC includes several hundred entity classes in an entity-relationship model, which supports the semantic description and geometric representation of typical construction elements and their relationships.  
Supported by the GeoBIM benchmark project, [PERSON] et al. [94] presented a specific study of the interoperability of the IFC standard, testing 33 software packages that support the IFC and focusing on the themes of georeferencing, semantics, and geometry. The general functionalities include visualization, editing, query, analysis, and export possibilities, and so on. However, the authors did not comment on the applicability of the IFC standard for indoor map representation and navigation. [PERSON] et al. [49], however, presented a survey of indoor navigation approaches, applications, and solutions supported by IFC. These survey results indicate that the most active research direction is to generate the navigation models from IFC data. Another group of researchers have investigated the transformation from IFC to other different data models and their integrations. For example, [PERSON] et al. [95] presented a graph transformation approach from IFC to CityGML, [PERSON] al. [96] introduced a method to cope with path planning for 3D indoor space through an IFC file as input, and [PERSON] et al. [97] discussed some of the problems that occur when integrating three built environment standards: CityGML, IFC, and LandInfra.
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
## 5 Conclusions  
This study presented a comparative analysis and comprehensive evaluation of forest cover from widely-used land cover datasets in Myanmar: Sufficient forest samples over 4 years were randomly collected from GE, which are used to conduct an accurate and objective evaluation of each dataset. The similarities and differences in the spatial distribution were compared for these forest datasets. The influence on spatial consistency was further analyzed from the aspects of terrain and climatic conditions.  
The results indicate that GlobeLand30 has the best comprehensive accuracy in Myanmar, followed by the FROM-GLC series, Hansen Forest cover, and the last is the CCI-LC and GLC-FCS series. The consistent forest distribution of these datasets is best in northern Myanmar. There, the terrain is relatively high, forest distribution is continuous, and the feature types are relatively homogeneous, so the remote sensing images have a certain degree of differentiation. The mapping results are quite good, even for the data withlow spatial resolution. In contrast, the southwestern region of Myanmar is at a lower altitude and near the sea, the landscape is more complex, the mixing of forests, shrubs, and cropland is serious. This will be the key direction for future efforts in forest mapping. In addition, this study also analyzed the limiting factors (terrain and climate) affecting forest consistency. We found that the areas with the best consistency are, respectively, distributed in the range of 1000-3500 m in elevation and 26\({}^{\circ}\)-35\({}^{\circ}\) in slope, and the subtropical highland climate (Cwb) zone. Although the tropical climate is more favorable for forest growth, it is mostly distributed in lower terrain areas with fewer forests and high human activity. The higher terrain of northern Myanmar, with its Cwb climate, is more conducive to forest growth than a tropical climate.  
Nowadays, all countries in the world are facing a critical situation of forest degradation, especially in tropical countries. Comparing multiple datasets can help users to select the appropriate data in different regions to conduct research. It can provide policy recommendations in line with local forest planning. As for Myanmar, forest monitoring is still a challenging task, with data sources, landscapes, and classification methods being the main constraints to its accuracy [38; 54; 55]. In the future, forest monitoring can be improved with multi-source remote sensed data and deep learning approaches.  
Conceptualization, H.X.; Data curation, D.H.; Formal analysis, H.X.; Methodology, H.X., J.N. and D.H.; Software, J.N., C.L. ([PERSON]), B.C. and L.Z.; Validation, J.N., S.Y.[PERSON] W.H. and C.L. ([PERSON]); Writing--original draft, H.X. and J.N.; Writing--review and editing, C.L. ([PERSON]), B.C., S.Y., D.H., L.Z., W.H. and C.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Strength Characteristics  
The relationship between the shear stress and effective stress for the unreinforced expansive soil and 10% rubber fiber-reinforced expansive soil is shown in Figure 8. Here, the deviator stress of the undrained samples is lower than that of the drained samples. The difference decreases with an increase in the effective stress. Simultaneously, the effective stress of the undrained samples shifts to the left and the slope of the curve decreases owing to the pore water pressure.  
For the drained and undrained conditions, the effective cohesion under the drained conditions was higher than that under the undrained conditions. The converse was true for the effective angle of internal friction. This is attributed to the effects of the pore water pressure and creep at the fiber-soil interface. Under drained conditions, owing to the lower strain rate, the test time increased significantly, and the influence of time on the interfacial strength (such as the time-varying slip or rearrangement of soil particles) was more evident. However, the pore water in the drained conditions was drained out of the soil, the stress was sustained by the soil skeleton and rubber fibers, and the occlusion capacity  
Figure 7: Volume strain of ESR-6 and ESR-7 in the drained tests.  
between the soil particles and fibers improved. Similarly, [PERSON] et al. [30] conducted consolidation undrained and consolidation drained triaxial tests on polypropylene fiber-reinforced soil. They observed that the effective shear strength parameters of the reinforced soil were higher under the undrained conditions. [PERSON] and [PERSON] [25] evaluated the shear strength of rubber fiber-reinforced soil under undrained and drained conditions. They observed that although the angle of internal friction was approximately equal for both conditions, the cohesion under the undrained conditions was higher than that under the under drained conditions.  
The addition of rubber fibers increased the effective cohesion of the samples from 12.4 kPa (ESR-1) to 15.5 kPa (ESR-3). This corresponded to an increase of 25%. From ESR-6 to ESR-7, the effective cohesion increased from 21.3 kPa to 32.5 kPa (an increase of 52.6%). Meanwhile, the effective internal friction angle varied marginally. This is similar to the results obtained by [PERSON] et al. [34]. This indicates that rubber fibers can effectively interlock soil particles, improve the interaction between the fiber surface and soil matrix, and increase the effective cohesion [35; 36].
# Research on the Posture Control Method of Hexapod Robot for Rugged Terrain
## 4 Posture Maintenance Strategy for Heavy Rugged Terrain
### Posture Adjustment Strategy Based on Master Polygon  
#### 4.2.1 Master Polygon  
The polygon formed by the end points of each supporting foot of the hexapod robot in the torso coordinate system is called the configuration polygon (CP). Affected by the rugged terrain, the CP is generally a spatial polygon. Figure 12 shows the CPs formed by the end points of the feet when  
Figure 11: The process of adjusting the pose on severe rugged terrain: (**a**) Analysis of terrain adaption mechanism and (**b**) analysis of pseudo-light rough terrain.  
supported by three, four, and five legs. In the case of four and five legs, not all the end points of the feet are on the same plane. From the gait rules, at least three non-adjacent legs are in the supporting phase at any time, and the triangle that is stipulated to be formed by connecting the end points of the three non-adjacent legs is the master polygon (MP). A unique MP always exists in any CP during walking, or it is the LMP formed by the end of the foot of leg \(L_{2}\), \(R_{1}\), and \(R_{3}\), or the RMP formed by the end of the foot of leg \(R_{2}\), \(L_{1}\), and \(L_{3}\). The CP of the hexapod robot and the terrain jointly determine its pose, and it cannot be used as a reference plane to measure the pose of the torso because it is a spatial polygon, while MP approximates the orientation characteristics of the CP, so MP is used as the position. The reference plane for pose adjustment and the selection rules are as follows:  
\[\begin{array}{ll}if&L2\_Stance=1\&R1\_Stance=1\&R3\_Stance=1\\ &then\ LMP\_State=1;\\ &if&R2\_Stance=1\&L1\_Stance=1\&L3\_Stance=1\\ &then\ LMP\_State=1;\\ &if&LMP\_State=1\&RMP\_State=1\\ &then\ LMP\_State=1\&RMP\_State=0;\end{array}\]  
where \(L_{i\_Stance}\) and \(R_{i\_Stance}\) are the status flags of the leg support, _LMP_State_ and _RMP_State_ are the status flags of LMP and RMP respectively, and the priority of LMP is higher than that of RMP.  
Figure 12: Analysis of main support polygon: (**a**) Configuration polygon with three stance legs, identification of light rough terrain, (**b**) configuration polygon with four stance legs, and (**c**) configuration polygon with five stance legs.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
## 2 Experiment Design
### Thermal Image Processing
The absolute temperature of a selected spot does not necessary possess the information about the battery overheat as it may be affected by the ambient temperature that was computed as the average temperature of an area (white rectangle) next to the battery (see Figure 4 (right)). The overheat usually starts in one cell of a battery and then spreads to the entire battery. In order to detect overheat more precisely, we split the battery into several segments (see them visualized by the red lines in Figure 4 (right)) and the average temperature of each segment was computed. The number of segments per battery was set experimentally to 6. Finally, the ratios between the segment and ambient temperatures formed the thermal feature vector of the battery.
## 4 SOC Estimation in the Entire SOC Area
### Results and Discussion  
The EKF-based SOC estimator described in Section 4.1 is used to estimate the SOC under NEDC and DST working conditions in the entire SOC area (0-100%), respectively. The results are shown in Figures 7 and 8. Results imply that the SOC estimation error based on 2 RC and E2 RC model is similar in high SOC area, however, the SOC estimation error based on E2 RC model is less 50% of that based on 2 RC model in low SOC area. Moreover, the SOC estimation error based on E2 RC model is always less than 1% in the entire SOC area.  
Furthermore, in order to evaluate the robustness of our proposed model, we set various sensor errors and model errors to calculate the SOC estimation errors in the entire SOC area. The sensor error mainly originates from the voltage sensor and current sensor, whereas the model error has two types, namely, voltage drift or voltage noise. Reference [12] indicated that the voltage noise has no effect on the SOC error for a large time scale. Hence, the effect of the voltage drift (\(U_{drift}\)), model error (\(M_{drift}\)), and current sensor error (\(I_{drift}\)) on the SOC is considered in our study.  
Figure 9 describes the influence of various model and sensor errors on the SOC estimated by EKF estimator under NEDC and DST working conditions. Figure 9a shows the relationship between the model error and the RMSE of SOC (\(R_{\text{SOC}}\)) based on E2 RC model. We can see that as long as the \(M_{drift}\) is within \(\pm 20 mV\), \(R_{\text{SOC}}\) can be kept within 5%. Figure 9b shows the relationship between the \(U_{drift}\) and \(R_{\text{SOC}}\). The \(U_{drift}\) is generally within 10 mV according to Reference [30]. In this case, \(R_{\text{SOC}}\) can be maintained at less than 3%. Figure 9b shows the relationship between the \(I_{drift}\) and \(R_{\text{SOC}}\), indicating that \(I_{drift}\) has little effect on the \(R_{\text{SOC}}\) obtained by EKF estimator. From the above analysis, we can see that the proposed battery model can achieve satisfactory accuracy in a wide range of model and sensor errors, which implies that our proposed model has good robustness in the SOC entire area.  
Figure 6: Schematic of extended kalman filter (EKF).  
Figure 8: _Cont._  
Figure 7: State-of-charge (SOC) estimation results under NEDC working condition. (**a**) SOC estimated value; (**b**) SOC error.  
Figure 8: SOC estimation results under DST working condition. (**a**) SOC estimated value; (**b**) SOC error.  
Figure 9: Influence of model and sensor errors on SOC estimated by EKF estimator under NEDC and DST working conditions. (**a**) Relationship between model error and SOC error. (**b**) Relationship between \(U_{drift}\) and SOC error. (**c**) Relationship between \(I_{drift}\) and SOC error.
###### Abstract  
In this study, BaZrO\({}_{3}\):Eu\({}_{0.025}\) powder was synthesized through the solid-state reaction method with two different heating processes. For the one-step process, the BaZrO\({}_{3}\):Eu\({}_{0.025}\) powder was heated to 1000 \({}^{\circ}\)C-1400 \({}^{\circ}\)C for 2 h. For the two-step heating process, the BaZrO\({}_{3}\):Eu\({}_{0.025}\) powder was pre-heated at 800 \({}^{\circ}\)C for 2 h, after being cooled to room temperature and ground, then the powder was heated to 1000 \({}^{\circ}\)C-1400 \({}^{\circ}\)C again for 2 h. The optimum excitation optical wavelength of BaZrO\({}_{3}\):Eu\({}_{0.025}\) powders were 271 nm and the maximum intensity of the emission spectrum of BaZrO\({}_{3}\):Eu\({}_{0.025}\) powders increased with increasing heating temperature. The all-heated BaZrO\({}_{3}\):Eu\({}_{0.025}\) powders showed two strong orange emission bands peaking at the wavelengths of 574 and 596 nm and two weak emission bands peaking at 620 and 650 nm. As the heating temperatures were 1300 \({}^{\circ}\)C and 1400 \({}^{\circ}\)C, the maximum intensity of one-step heating BaZrO\({}_{3}\):Eu\({}_{0.025}\) powders appeared at the \({}^{5}\)D\({}_{0}\)-\({}^{7}\)F\({}_{1}\) (574 nm) and the maximum intensity of one-step heating BaZrO\({}_{3}\):Eu\({}_{0.025}\) powders appeared at the \({}^{5}\)D\({}_{0}\)-\({}^{7}\)F\({}_{1}\) (596 nm) transitions of Eu\({}^{3+}\) ions, respectively.  
Keywords:BaZrO\({}_{3}\) phosphor; Different heating processes; Europium; Photoluminescence property +
Footnote †: journal: Applied Physics Letters
## 4 Discussion
### Limitations and Future Directions  
Multiple factors limit the generalizability of the present report. First, the findings of this study are limited by a small sample size, and participants in our sample were generally healthy adults. Future investigations would benefit from increased sample sizes and the inclusion of persons from clinical populations, such as individuals with neurodegenerative conditions like amyotrophic lateral sclerosis (ALS), especially when investigating AAC-BCI paradigms. Second, though we sought to address problematic adjacent ERP overlap with jittered SOI, it is difficult to estimate baseline effects of overlap in RSVP without fundamentally altering the paradigm. Slowing down presentation rates (as we did in our non-experimental 1 Hz slow calibration) often results in increased fatigue, and the cognitive demands of the task may be qualitatively different from those at faster presentation rates (e.g., sustained attention, vigilance, or stimulus discrimination). Future testing would benefit from exploration of alternative designs that address overlap, but do not alter presentation rate, such as presenting RSVP stimuli at different spatial locations [49], or something akin to a modified matrix speller. Lastly, there were limitations in our analyses. Our online ERP classifier used for copy phrase was restricted to a window of 0-500 ms, which did not wholly match our offline measurements of N200 and P300, although these different approaches did correlate significantly. Our use of a regularized discriminant in the RSVP paradigm, generally speaking, might not match results from similar manipulations of other visual presentation paradigms, like a matrix speller, or results from machine learning approaches other than the regularized discriminant. As well, the logistic regression approach used to classify our alpha attenuation effect was clearly ineffective. Logistic regression is a relatively simple approach to class prediction, and it is likely that more sophisticated methods would demonstrate better performance. Likewise, it is possible that the temporal windowing constraints used for offline alpha analyses were not as well suited to our classifier. Future work should seek to identify an optimal approach to classification for the alpha attenuation measure.