esa-sceva/satcom-chunk-collection huggingface dataset:
# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 2 Related Works
### Methods Based on Fusion  
Recent advancements in semantic-motion fusion for SLAM systems demonstrate significant improvements in computational efficiency and localization accuracy. Ref. [35] innovatively combines R-CNN instance segmentation with LK optical flow tracking, implementing a semantic-guided dynamic feature identification mechanism that reduces computational overhead while outperforming the original DSO algorithm. Building on this, ref. [36] develops an integrated framework combining instance-level segmentation with dense optical flow, enabling joint optimization of object motion tracking and camera pose estimation. Further developments by [37; 38; 39] employ semantic segmentation coupled with optical flow analysis to precisely eliminate interference features in highly dynamic environments, substantially enhancing system adaptability.  
Inspired by the aforementioned research, we propose a visual-inertial SLAM algorithm that integrates semantic information with multi-view geometric constraints. First, we combine ORB features and Yolov5s to estimate a relatively rough camera pose (though not precise, this estimate still provides a useful reference for subsequent processing). Then, the localization system, using the association data between the current frame and the reference keyframe, applies semantic information and multi-view geometric methods to further identify and remove feature points related to dynamic targets in the current frame, ensuring they do not participate in subsequent pose estimation. This strategy reduces the system's reliance on detection models, granting the SLAM algorithm better flexibility and scalability for different application scenarios. Compared to the existing dynamic SLAM algorithms, we significantly improve the pose estimation accuracy and demonstrate stronger robustness in indoor, highly dynamic environments.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 4 Method Implementation and Validation
### Quality of the Recovered Message  
The second proposed evaluation criterion consists in measuring the quality of the recovered secret message. An ideal system will recover the message identically to the original file. However, due to the operations of range normalisation in the transmitter and receptor modules, there will be small  
Figure 4: Imperceptibility. Correlation between host and stego signals. Results shown through a confidence interval (95%).  
Figure 5: Average results of imperceptibility, arranged by secret message and BH. Correlation between host and stego signals.  
differences between both signals. The evaluation of the recovered voice message quality is carried out through the \(PCC\) parameter between the original secret message and the one recovered in the receptor module.  
Similar to the imperceptibility evaluation, the results of the 600 tests for the quality analysis of the recovered secret message are gathered in terms of the selected host signal. Each group contains in turn the result of 60 tests, which consist of varying the secret message that is hidden (ten messages in total) and the \(BH\) value for each secret message (six different \(BH\) values). Figure 6 presents the results in confidence intervals of 95%, where it is evident that the recovered signals have soared similarities with the original secret messages, having an \(PCC\) value above 0.985 in all cases.  
To corroborate the above, the recovered secret messages are shown using 10 host signals for \(BH=\mathbf{4}\) (Figure 7).  
According to the previous results, it is evident that the recovered signals are quite similar to the original ones (perceptually identical in sight and for the human auditory system, HAS), which confirms that the quality of the recovered message does not depend on the selected host signal (\(PCC>0.998\) with a confidence range of 95%).  
Figure 6: Recovered message quality (confidence range) results, in terms of host signal.  
Figure 7: Secret messages recovered from the key/password, varying the host signal and with \(BH\) equal to 4.
# Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios
## 4 LSTM for Load Estimation and PPO for Task Offloading Strategy
### Integrated Approach for Load Estimation and Task Offloading Strategy  
In summary, we propose an integrated approach for load estimation and the task offloading strategy to minimize the system task processing delay. It includes two steps. The first step is to predict the load condition of the BS for the next time period based on the long-term load status table provided by the BS and the feedback from the current time period. This step forms the basis for the rational and efficient utilization of the BS. Utilizing an LSTM prediction model, optimizing the input dimensions and training iterations, memory units in the hidden layer, or the number of hidden layers is essential to achieve a good fit  
Figure 2: PPO-Clip for task offloading strategy flow.  
and precision in the prediction. Incorporating the additional step of obtaining feedback from each time period enhances the accuracy of predicting the BS's load condition.  
Then, based on the PPO algorithm, the agent learns to observe the current status of the VEC and reasonably divide the computing tasks in real time, that is, make decisions \(\alpha\), \(\theta\), and \(\gamma\). The offloading of computing tasks was completed to improve the QoS for users. The main algorithmic procedure is presented in Algorithm 1.  
```
0: BS load history data;  LSTM Training;  BS load estimation value \(L_{b}\in s_{t}\);  Input: Epoch \(e\), steps \(t\), \(e\), \(\lambda\), actor networks \(\phi^{Actor}\), critic networks \(\phi^{Critic}\), batch size \(m\); for\(i=1:e\)do for\(i=1:t\)do  Get the vehicle state \(s_{t}\);  Input \(s_{t}\) to Actor \(\phi^{Actor}\) and get output \(\mu\), \(\sigma\);  Get the Gaussian distribution from \(\mu\) and \(\sigma\) as policy \(\pi_{\phi}(a_{t}|s_{t})\);  Select action \(a_{t}\) from the policy \(\pi_{\phi}(a_{t}|s_{t})\);  Store \(\{s_{t},a_{t},r_{t},s_{t+1}\}\) in the buffer; if\(t\) % \(m==0\)then  Select 10 random samples;  Calculate \(L^{CLIP}(\phi)\) and update \(\phi^{Actor}\) by (23)-(25);  Calculate loss function based on Critic output \(V(s_{t})\) and discounted reward;  Update \(\phi^{Critic}\) based on the MSE loss function;  endif endfor endfor
```  
**Algorithm 1** LSTM and PPO for task offloading strategy approach
## 2 Network Model  
The STDP impulse neural network model based on the adaptive threshold uses the Leaky Integrate-and-Fire (LIF) neuron model as the network node, and the intermediate synapse information is transmitted in the form of pulses. Its basic structure is shown in Figure 1. As shown, it can be divided into pulse coding layer, neural network layer and classification layer in turn. The pulse coding layer encodes each pixel of the image into a pulse whenever the image needs to be classified. After the image is encoded into a pulse, it is trained by two neural network layers. The first neural network layer contains 100 neurons, and the second neural network layer contains 20 neurons. The neural network layer includes two layers of nerves. Between each layer of neurons, the weights of neuron connections are updated through the STDP algorithm, and the neurons of the same layer are laterally inhibited; the classification layer classifies the signals processed by the neural network layer.
## 4 Results and Discussion
### Results
#### 4.2.3 Wage-Inducing Effects
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sectors** & \multicolumn{3}{c}{**South Korea**} & \multicolumn{1}{c}{**Japan**} \\ \cline{2-5}  & **Value** & **Rank** & **Value** & **Rank** \\ \hline
1. & Agricultural, forest, and fishery goods & 0.00004 & 27 & 0.00001 & 23 \\
2. & Mined and quarried goods & **0.00341** & **1** & **0.00324** & 1 \\
3. & Food, beverages, and tobacco products & 0.00008 & 23 & 0.00000 & 28 \\
4. & Tetifile and leather products & 0.00004 & 28 & 0.00015 & 6 \\
5. & Wood, paper products, printing, and reproduction of recorded media & 0.00009 & 22 & 0.00021 & 4 \\
6. & Petroleum and coal products & 0.00002 & 31 & **0.00025** & 3 \\
7. & Chemical products & 0.00022 & 12 & 0.00018 & 5 \\
8. & Non-metallic mineral products & 0.00004 & 26 & 0.00006 & 15 \\
9. & Basic metal products & 0.00012 & 19 & 0.00006 & 13 \\
10. & Fabricated metal products, except machinery and furniture & 0.00036 & 10 & 0.00006 & 14 \\
11. & Computing machinery, electronic equipment, and optical instruments & 0.00014 & 16 & 0.00001 & 24 \\
12. & Electrical equipment & 0.00013 & 18 & 0.00001 & 25 \\
13. & Machinery and equipment & 0.00030 & 11 & 0.00003 & 18 \\
14. & Transport equipment, manufacturing services, and repair services of industrial equipment & 0.00006 & 7 & 0.00002 & 20 \\
15. & Other manufactured products & 0.00003 & 30 & 0.00001 & 22 \\
16. & Electricity and steam supply & 0.00017 & 15 & 0.00012 & 10 \\
17. & Water supply, sewage, waste treatment, and disposal services & 0.00007 & 24 & 0.00014 & 8 \\
18. & Construction & 0.00009 & 21 & 0.00009 & 11 \\
19. & Wholesale, retail trade, and commodity brokerage services & 0.0005 & 6 & 0.00003 & 19 \\
20. & Transportation & 0.00093 & 5 & 0.00008 & 2 \\
21. & Food services and accommodation & 0.00047 & 8 & 0.00000 & 31 \\
22. & Communications and broadcasting & 0.00038 & 9 & 0.00013 & 9 \\
23. & Finance and insurance & **0.00338** & 2 & 0.00003 & 17 \\
24. & Real estate services & 0.00018 & 14 & 0.00002 & 21 \\
25. & Professional, scientific, and technical services & 0.000142 & 4 & 0.00000 & 32 \\
26. & Business support services & **0.00247** & 3 & 0.00015 & 7 \\
27. & Public administration, defense, and social security services & 0.00014 & 17 & 0.00000 & 26 \\
28. & Education services & 0.00003 & 29 & 0.00000 & 27 \\
29. & Health and social care services & 0.00010 & 20 & 0.00000 & 30 \\
30. & Art, sports, and leisure services & 0.00007 & 25 & 0.00000 & 29 \\
31. & Other services & 0.000020 & 13 & 0.00008 & 12 \\
32. & Others & 0.00000 & 32 & 0.00004 & 16 \\
33. & Others & 0.01641 & & 0.01641 & \\  & & 0.00303 & & 0.01305 & \\  & & & & \\ Total (\(\Delta\)+B) & 0.04674 & 0.01908 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Wage-inducing effects of natural gas supply sector in South Korea and Japan.
## 1 Introduction
For completeness of description the reduced characteristic function \(f_{\text{m}}(\lambda)\) and the associated characteristic matrix \(\mathbf{J}_{\text{m}}(\lambda)\) of the closed-loop system are firstly derived. They have the same eigenvalues (or characteristic roots) as those of the closed-loop system and involve only control gains, time-delay parameters, and the measured open-loop receptances at the sensor/actuator coordinates. Then, one of the methods that are used to solve nonlinear eigenvalue problems, a contour integral method, is adopted to determine the required eigenvalues from the characteristic matrix \(\mathbf{J}_{\text{m}}(\lambda)\). The proposed approach does not require the knowledge of system matrices and works on \(\mathbf{J}_{\text{m}}(\lambda)\) with a significantly smaller size than that of the original system. Especially, it does not need to provide initial approximations of calculated eigenvalues, which is the major difference between the proposed approach and that in [19]. The paper is organized as follows. In Section 2, the system involved is described, and the reduced characteristic function \(f_{\text{m}}(\lambda)\) and the associated characteristic matrix \(\mathbf{J}_{\text{m}}(\lambda)\) are derived. A contour integral method is briefly reviewed and the computational procedure of the dominant eigenvalues are presented in Section 3. In Section 4, a numerical example is provided to demonstrate the proposed approach. Conclusions are finally drawn in Section 5.
## 3 Experiments and Analysis
### Experimental Analysis  
The effectiveness of three parts of the proposed approach is assessed, i.e., (1) the advantage of the feature extraction method, (2) the advantage of the ResNet18 model, and (3) the contribution of the embedding layer with the center loss function and softmax. Besides, the comparison of the performance of the described method and that of references is presented.
## 4 Results
### SBAS-InSAR Results  
The deformation rate in the Line of Sight (LOS) direction calculated by SBAS-InSAR technology is shown in Figure 9. The annual deformation rates in the study area range from \(-\)30.0 mm/y to 30.0 mm/y, where positive values indicate surface uplift and negative values represent surface subsidence. The average subsidence rate is 5.03 mm/y. Due to the hilly terrain and dense vegetation in the high-altitude regions in the study area, there were areas  
Figure 8: IV-CatBoost model ROC curve. An AUC value ranging from 0.7 to 0.9 indicates favorable model performance, signifying its heightened classification capability.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Landslide Susceptibility Class**} & \multirow{2}{*}{**Rank**} & \multicolumn{6}{c}{**InSAR Deformation Rate Class**} \\ \cline{3-6}  & & **1** & **2** & **3** & **4** & **5** \\ \hline VL & 1 & 0 & +1 & +2 & +3 & +4 \\ L & 2 & 0 & 0 & +1 & +2 & +3 \\ M & 3 & 0 & 0 & 0 & +1 & +2 \\ H & 4 & 0 & 0 & 0 & 0 & +1 \\ VH & 5 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contingency matrix applied to the LSM considering the average velocity in each cell. (The subsidence results obtained from SBAS-InSAR were classified into five categories using the natural breaks method, where higher grades correspond to greater deformation rates).  
affected by decorrelation, leading to significant data gaps. The main subsidence areas are located in the western mountainous valleys and urban residential areas of the study area.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 3 BackCom: An Effective Solution
### Different Types of BackCom
#### 3.3.4 Monostatic BackCom  
The preceding configuration of BackCom needs two separate antennas, one each for transmitter and receiver. On the other hand, some applications such as hand-held readers cannot afford two antennas, due to the additional size, complexity, and expense. Alternatively, a single antenna can be used for both transmission and reception. Such configuration is referred to as Monostatic BackCom (Mon-BackCom) [39]. Figure 10 shows the transmission and reception process using the single common antenna in the reader. However, the data rate of this type of BackCom is low due to the one-way information transfer. Exploiting two different types of BackCom, a hybrid combination is elaborated below.
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
###### Abstract  
The accuracy of cooperative localization can be severely degraded in non-line-of-sight (NLOS) environments. Although most existing approaches modify models to alleviate NLOS impact, computational speed does not satisfy practical applications. In this paper, we propose a distributed cooperative localization method for wireless sensor networks (WSNs) in NLOS environments. The convex model in the proposed method is based on projection relaxation. This model was designed for situations where prior information on NLOS connections is unavailable. We developed an efficient decomposed formulation for the convex counterpart, and designed a parallel distributed algorithm based on the alternating direction method of multipliers (ADMM), which significantly improves computational speed. To accelerate the convergence rate of local updates, we approached the subproblems via the proximal algorithm and analyzed its computational complexity. Numerical simulation results demonstrate that our approach is superior in processing speed and accuracy to other methods in NLOS scenarios.  
Keywords:wireless sensor networks (WSN); cooperative localization; non-line-of-sight (NLOS); alternating direction method of multipliers (ADMM); convex relaxation +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics are presented as mean and standard deviation (\(M\pm SD\)). Prior to inferential analyses, the assumptions of homoscedasticity and normality were tested. Homoscedasticity was confirmed using _Levine's test_, and the normality of data distribution was verified using the Shapiro-Wilk test for each variable within each group combination. All variables met these assumptions (\(p>0.05\)), supporting the use of parametric analyses.  
For inferential analysis, firstly, a two-way multivariate analysis of variance (_MANOVA_) was performed, to examine the effects of the refereeing technique (2 vs. 3) and competition sex (male vs. female) on physical and physiological demands, and secondly, to analyze the evolution of demands across game quarters, a repeated measures _MANOVA_ was conducted  
Figure 1: Antenna distribution around the basketball field.  
with the quarter (Q1, Q2, Q3, or Q4) as the within-subjects factor and the refereeing technique and competition sex as between-subjects factors. Finally, when significant multivariate effects were found, univariate analyses were conducted to identify specific differences. Bonferroni adjustment was applied for multiple comparisons to control the Type I error rate.  
The magnitude of differences was assessed using effect sizes. For _MANOVA_ results, omega partial squared (\(\omega_{p}\)\({}^{2}\)) was calculated and interpreted using the following thresholds: trivial (\(<\)0.01), small (0.01-0.05), moderate (0.06-0.13), and large (\(>\)0.14). For post hoc pairwise comparisons, [PERSON]'s \(d\) effect size was calculated and interpreted as trivial (0-0.2), small (0.2-0.6), moderate (0.6-1.2), large (1.2-2.0), or very large (\(>\)2.0).  
Statistical significance was set at \(p<0.05\) for all analyses. All statistical procedures were performed using JAMOVI statistical software (Version 2.3.21, The JAMOVI Project, Sydney, Australia). Effect sizes and their confidence intervals were calculated using the ESCI module within JAMOVI. Graphical representations of the results were created using GraphPad Prism software (Version 9.0.0, GraphPad Software Inc., San Diego, CA, USA).
# An Accurate Probabilistic Model for TVWS Identification
## 5 Experimental Results and Discussion
### Processing Time  
According to Figure 3, the proposed model has six blocks in which the information of the UHF-TV spectrum is collected and processed. The action performed in each block, a brief explanation, and its respective average time is summarized in Table 4.  
For blocks 1 and 2, the time of the collected samples saved in the DB was obtained using the RF Explorer for Windows open software. For blocks 3 to 6, the time was calculated in the Matlab script by using the start stopwatch timer command and running 500 times the mentioned script. To reduce the time in block 4, we do not calculate the intersection point of two pdf curves \(W\) and \(X\), in this case, we use the value of \(P_{f4}\) corresponding to 3%, and performing the operation explained in Equation (11), obtaining the value of \(T\).  
The total average time to find the TVWS in one reading is \(1.45\) s. To reduce it according to the detection time suggested in the 802.22 standards [10], it is possible to use big data processing techniques or use hardware with improved processing frequency, to achieve real-time detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Block** & **Action Performed** & **Observations** & **Time** \\ \hline
1 & Spectrum sensing & Antenna/RFE collect samples \(W(f)\), \(X(f)\) & 0.00 s \\
2 & Samples processing & Two RFE save samples in DB & 0.34 s \\
3 & Statistical calculations & Matlab \(\rightarrow\) calculates \(\overline{\mathbf{W}}(f)\), \(\sigma_{W}\), \(\overline{\mathbf{X}}(f)\), and \(\sigma_{\mathbf{X}}\) & 0.31 s \\
4 & Probability calculations & With a selected \(P_{f4}\), Matlab \(\rightarrow\) calculates \(M\) and \(T\) & 0.24 s \\
5 & ED performing & Matlab \(\rightarrow\)\(\overline{\mathbf{X}}(f)\) is compared with \(T\) & 0.27 s \\
6 & Results presentation & Channel’s energy lower than \(T\) are marked as TVWS & 0.29 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average time obtained in each block of the proposed model.  
Figure 19: Confidence Interval (CI) of average values for \(W(p)\) and \(X(p)\), the variations are represented in dB.
## 3 All-Optical Ultrasound Transducers and Imaging
### Development of Transducers and Imaging
#### 3.1.2 Fiber-Optic Transducers  
The AOUST using the above-mentioned thin-film FP structure achieves high performance but is still bulky, which restricts the applications where the region of interest is difficult to access. Fiber-optic AOUSTs are explored for realizing complete miniaturized transmitting-receiving transducers. The miniaturization would facilitate the insertion of the transducer in the human body for minimally invasive procedures. Currently, fiber-optic AOUSTs are mostly constructed using two optical fibers, where one is for LGUS transmission and the other is for ODUS sensing. (i) An AOUST probe is developed with its diameter <0.84 mm and a bandwidth of ~20 MHz [77]. The AOUST probe and its imaging setup are shown in Figure 7. It consists of an LGUS transmitter using a CNTs-PDMS nanocomposite coating on the end face of an optical fiber and an FP ODUS receiver on that of an adjacent optical fiber. The generation and detection fibers are within tubular metal housing. The probe is mechanically scanned to form a virtual array for synthetic aperture [91]. The lateral and axial resolutions are measured as 88 and 64 \(\upmu\)m, respectively, at a depth of 3.5 mm. (ii) An LGUS array transmitter is implemented using AuNPs with different sizes on the end face of an optical fiber [74]. A fiber-optic FP cavity is fabricated on the same fiber as the ODUS detector. One unique property of AuNPs is that their maximum absorption wavelength varies with the diameters of AuNPs. Laser with different wavelengths can be chosen to excite AuNPs with corresponding sizes at specific locations. Such excitation scheme facilitates the realization of phased array technique and ultrasound beam steering. It is not necessary to perform a mechanical scan for 3D imaging. A bundle of hollow optical fibers may also be considered as another approach to construct an LGUS array transmitter [92]. (iii) Through employing an acoustic lens for geometrical focusing, a fiber-optic LGUS transmitter that can produce a collimated ultrasound beam is demonstrated [93]. When the transmitter is integrated with a fiber-optic ODUS detector, an AOUST probe can be constructed. Since the beam is well collimated, pulse-echo ultrasound imaging can be acquired by scanning the probe without the need for image reconstruction. (iv) Another work shows that the AOUST probe can be fabricated based on a micro-opto-mechanical-system (MOMS) process [94].