esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synthetic Examples
### Simulation of Different Phase Signals  
Considering that the SSST-TD TF correlation changes with time, the phase change must be considered. In order to analyze the influence of the phase, the phase of \(y_{1}\) is advanced by \(\pi\) and \(\pi/2\):  
\[\left\{\begin{array}{l}y_{2}=\ \cos(30\times 2\pi t)+\cos\bigl{(}100\times 2\pi t +\frac{\pi}{2}\bigr{)}\\ y_{3}=\ \cos(30\times 2\pi t)+\cos(100\times 2\pi t+\pi)\end{array}\right. \tag{17}\]  
According to the previous steps, the three traditional correlation coefficient curves are shown in Figure 5. Under the influence of the phase change, the traditional correlation coefficient was consistent near 30 Hz and 100 Hz, with great differences at other frequencies.  
Figure 4: The correlation coefficient between \(x\) and \(y_{1}\) of different times.  
Figure 3: The correlation coefficients between \(x\) and \(y_{1}\) attained by (**A**) the S transform–time series decomposition (ST-TD), and (**B**) the synchrosqueezed S transform–time series decomposition (SSST-TD).  
Using the SSST-TD TF correlation analysis, the correlation coefficients of \(x\) and \(y_{2}\) and \(x\) and \(y_{3}\) are shown in Figure 6. It can be seen that the overall correlation coefficient trend was consistent with the original phase with small differences at different times. Taking 63 Hz and 100 Hz as an example, the time domain curves of the correlation coefficients, after removing the two ends, are shown in Figure 7. It can be seen that at 100 Hz, the correlation coefficient did not follow the phase change, remaining unchanged, while at 63 Hz, the correlation coefficient varied with the phase change. Therefore, compared with traditional correlation analysis, the SSST-TD TF correlation not only reflected the correlation changing with time, but also retained the correlation changing with the phases. At a high frequency, the phase changes quickly. However, when the frequency is low, the phase change will play an important role and cannot be ignored.
## 3 Establishment of Hydro-Mechanical Properties Creep Damage Constitutive Model
### Model Establishment  
According to the time-dependent damage characteristics of the creep process under the coupling effect of rock hydro-mechanical properties, the expression of the hydro-mechanical properties creep damage coupling constitutive model is as follows:  
\[\sigma=\mathbb{C}(\xi,\omega):(\epsilon-\epsilon^{p})=(1-\alpha_{2}\xi)(1- \omega)\mathbb{C}^{0}:(\epsilon-\epsilon^{p}), \tag{15}\]  
In Equation (15), the description of hydro-mechanical properties creep damage constitutive law is divided into two stages, instantaneous loading and creep, and the total strain is decomposed into the elastic strain, plastic strain and time-dependent strain to describe the following:  
\[\epsilon=\mathbb{S}(\omega,\xi):\sigma+\epsilon^{\xi p}+\epsilon^{\xi t}, \tag{16}\]  
In the equation, \(\mathbb{S}(\omega,\xi)\) is the total damage fourth order flexibility matrix under the coupling of hydro-mechanical properties, \(\epsilon^{\xi p}\) and \(\epsilon^{\xi t}\) represent the plastic strain and time-dependent strain in the coupled creep process of rock hydro-mechanical properties, respectively. The simultaneous plastic multiplier \(\lambda_{s}\) can also be divided into instantaneous plastic multipliers \(\lambda^{\xi p}\) and time-dependent plasticity multiplier \(\lambda^{\xi t}\).
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 7 Experimental Results
### Effectiveness of IoTBystander
#### 7.2.4 Statistical Reliability Analysis  
To evaluate the statistical reliability of the results in Table 8, we calculate the standard deviation (SD) and 95% confidence intervals (CIs) for the performance metrics (precision, recall, and F1-score) of each method (TER, AER, and DCA) across different device types (smart devices, dumb devices, and all devices). Table 9 shows the results.  
Figure 8: Results of comparison with related approaches: PingPong [58] and IoTAudMon [12].  
The standard deviation reflects the variability in monitoring performance across different events. For smart devices, TER and DCA exhibit a higher standard deviation (0.1816), indicating more variability, while methods applied to dumb devices, such as AER (0.0371), show much smaller standard deviations, highlighting greater consistency for non-smart devices. The 95% confidence intervals for smart devices (TER, AER, and DCA) range from [0.8239, 1.0333], indicating stable performance. In contrast, for dumb devices, AER and DCA have narrower intervals ([0.9115, 0.9919] and [0.9257, 0.9727], respectively), suggesting highly consistent results. These findings demonstrate that AER provides more stable performance for non-smart devices compared to TER, which applies primarily to smart devices.  
The results of the overall device category, which combines smart and dumb devices, demonstrate a wider range of variability. For example, TER applied to all devices has a confidence interval for precision between [0.3022, 0.6978], indicating greater variability in results across a mixed set of devices. This is further reflected in the larger standard deviations (e.g., TER: 0.4899). However, when comparing across methods, DCA provides the most stable results, with smaller confidence intervals (e.g., [0.883, 1.016] for F1-score) and lower standard deviations, particularly in the F1-score metric, which is critical for evaluating overall performance in real-world scenarios. This validates the superiority of our dual-channel approach.
## 2 Overview of Supplementary Damping Systems
### Principles of Energy Dissipation Mechanisms  
Understanding the fundamental principles behind various energy dissipation mechanisms is crucial for the effective design and implementation of supplementary damping systems.
# Autonomous Navigation Based on the Earth-Shadow Observation near the Sun-Earth L2 Point
## 2 Design of Navigation System
### The Earth-Shadow Measurement Model
According to Figure 5, the geometric relationship is given as  
\[AF\approx OF\tan\angle AOF\approx\tan\angle AOF. \tag{21}\]  
According to the inner product formula in \(\triangle AOF\), the geometric relationship is given as  
\[(\vec{E}-\vec{r})\cdot(\vec{S}-\vec{r})=\|\vec{E}-\vec{r}\|\|\vec{S}-\vec{r} \|\cos\angle AOF. \tag{22}\]  
Substituting the point coordinates, the relationship is given as  
\[\sqrt{\big{(}E_{x}-r_{x}\big{)}^{2}+\big{(}E_{y}-r_{y}\big{)}^{2}}\sqrt{\big{(} S_{x}-r_{x}\big{)}^{2}+\big{(}S_{y}-r_{y}\big{)}^{2}}\cos\angle AOF=\big{(}E_{x}-r_{x} \big{)}\big{(}S_{x}-r_{x}\big{)}+\big{(}E_{y}-r_{y}\big{)}\big{(}S_{y}-r_{y} \big{)}. \tag{23}\]  
It is considered that \(\angle AOF=\theta\), and the geometric relationships are given as follows:  
\[\begin{split}\cos\theta=\frac{\big{(}E_{x}-r_{x}\big{)}\big{(} S_{x}-r_{x}\big{)}+\big{(}E_{y}-r_{y}\big{)}\big{(}S_{y}-r_{y}\big{)}}{\sqrt{ \big{(}E_{x}-r_{x}\big{)}^{2}+\big{(}E_{y}-r_{y}\big{)}^{2}}\sqrt{\big{(}S_{x} -r_{x}\big{)}
## 3 Results
### Heavy Metal Content in American Ginseng  
The content of the heavy metals in American ginseng was within the safety limits. The content of heavy metals in American ginseng followed the order Mn > Ni > Cu > Cr > As > Cd, and Pb was not detected (Table 2). The ranges of Cu, Mn, As, Cd, Cr, and Ni contents were 2.24-5.81, 20.34-336.44, 0-1.34, 0.06-0.40, 0.58-4.07, and 2.09-9.43 mg/kg, respectively. Their averages were approximately 4.00, 120.78, 0.87, 0.19, 1.99, and 4.61 mg/kg, respectively. Compared with the limited standard value (LSV) of CHM [26], all heavy metal content was lower than the LSV, except Cd content in two samples (NG1 and NG2). The content of Mn in American ginseng was relatively high, and considerable difference was observed among all samples. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Cu** & **Mn** & **As** & **Pb** & **Cd** & **Cr** & Ni \\ \hline YG1 & \(4.26\pm 0.10\) & \(25.77\pm 2.10\) & \(1.10\pm 0.17\) & ND & \(0.14\pm 0.02\) & \(2.37\pm 0.31\) & \(2.12\pm 0.07\) \\ YG2 & \(3.85\pm 0.20\) & \(33.06\pm 3.14\) & \(0.83\pm 0.04\) & ND & \(0.06\pm 0.01\) & \(2.26\pm 0.11\) & \(2.32\pm 0.15\) \\ YG3 & \(2.24\pm 0.20\) & \(20.34\pm 1.50\) & \(1.23\pm 0.10\) & ND & \(0.12\pm 0.02\) & \(0.58\pm 0.16\) & \(2.98\pm 0.17\) \\ BG1 & \(3.19\pm 0.32\) & \(78.81\pm 1.72\) & ND & ND & \(0.23\pm 0.01\) & \(1.52\pm 0.24\) & \(3.77\pm 0.17\) \\ BG2 & \(5.48\pm 0.50\) & \(66.80\pm 2.46\) & \(1.08\pm 0.15\) & ND & \(0.14\pm 0.03\) & \(1.10\pm 0.22\) & \(3.80\pm 0.31\) \\ BG3 & \(3.81\pm 0.20\) & \(71.18\pm 1.78\) & \(1.34\pm 0.22\) & ND & \(0.21\pm 0.03\) & \(0.73\pm 0.09\) & \(4.12\pm 0.18\) \\ NG1 & \(4.94\pm 0.13\) & \(283.53\pm 2.86\) & ND & ND & \(0.40\pm 0.03\) & \(1.22\pm 0.21\) & \(5.03\pm 0.38\) \\ NG2 & \(4.98\pm 0.34\) & \(250.01\pm 8.53\) & \(1.09\pm 0.10\) & ND & \(0.35\pm 0.02\) & \(1.33\pm 0.19\) & \(9.43\pm 0.49\) \\ NG3 & \(5.15\pm 0.13\) & \(336.44\pm 0.82\) & \(1.17\pm 0.25\) & ND & \(0.26\pm 0.01\) & \(3.17\pm 0.26\) & \(6.42\pm 0.49\) \\ CG1 & \(3.46\pm 0.11\) & \(148.97\pm 8.29\) & \(0.73\pm 0.20\) & ND & \(0.25\pm 0.03\) & \(3.66\pm 0.32\) & \(7.24\pm 0.40\) \\ CG2 & \(2.26\pm 0.11\) & \(92.60\pm 2.86\) & \(1.21\pm 0.05\) & ND & \(0.16\pm 0.03\) & \(4.07\pm 0.33\) & \(5.97\pm 0.36\) \\ CG3 & \(3.42\pm 0.16\) & \(41.88\pm 2.00\) & \(1.19\pm 0.18\) & ND & \(0.14\pm 0.02\) & \(2.70\pm 0.28\) & \(2.09\pm 0.15\) \\ Average & \(4.00\) & \(120.78\) & \(0.87\) & \(0.00\) & \(0.19\) & \(1.99\) & \(4.61\) \\ LSV & \(20.00\) & NF & \(2.00\) & \(5.00\) & \(0.30\) & NF & NF \\ \hline \end{tabular} ND means that the value was not detected. NF means that the value was not found. LSV means the limited standard value [26].  
\end{table} 
Table 2: Content of heavy metals (average \(\pm\) standard deviation) in American ginseng in the study area (mg/kg).
## 3 Experiment
### Ablation Experiment
#### 3.4.3 Effect of Multi-Head Loss Block  
In order to verify the contribution of the Multi-head Loss Block proposed in this paper to the model accuracy and stability of the training process, we designed controlled experiments. We use two different methods for training. One is to train using the complete  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Backbone** & **MIoU (\%)** & **MAcc (\%)** & **PAcc (\%)** \\ \hline Backbone (HorNet) & 86.15 & 92.57 & 93.64 \\ Swin Transformer (Mask2 Former) & 86.09 & 92.62 & 93.81 \\ SFA Encoder & **87.37** & **93.36** & **94.24** \\ \hline \hline \end{tabular}
\end{table}
Table 1: SFA Encoder ablation experiment results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone** & **Impervious-** & **Building** & **Low-** & **Vegetation** & **Tree** & **Car** \\ \hline SFA Encoder & 85.45 & **87.64** & 74.18 & 93.34 & 96.25 \\ SFA Encoder + SPAC Encoder & **85.84** & 87.34 & **76.36** & **93.68** & **96.58** \\ \hline \hline \end{tabular}
\end{table}
Table 3: SPAC Encoder ablation experiment detailed results.  
model structure as described in Figure 2. The other is to remove the red line portion as described in Figure 2, without using the Multi-head Loss Block to normalize the feature map of each scale separately and using only the results of the main decoder to compute the loss used for backpropagation. After completing the experiment, we analyzed the results statistically. Figure 6 shows the changing trend of MIoU in two training ways. Table 4 records the highest evaluation criteria of the two training methods, and it is clear that Multi-head Loss Block contributes to both training efficiency and final model accuracy.
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Overview of Performance Improvement Schemes for BOCDA
#### 4.3.7 Time-Division Pump and Prove Generation Scheme to Simplify the System  
The pump and the probe lightwave must have a frequency difference around the BFS to induce the stimulated Brillouin scattering. Additionally, the difference should be swept to have the shape of the BGS. To do so, in the basic BOCDA setup, a lightwave frequency shifter or a single side-band modulator (SSBM) is set and used in the system. However, the SSBM is an expensive device. To overcome the difficulty, a \"time division pump and probe generation scheme\" has been proposed and developed [59]. In this scheme, the LD driving current waveform consists of the sinusoidal waveform to realize the SOCF function, and the square-like waveform for the \"time-division pump and probe generation\". In the latter waveform, the amplitude corresponds to the frequency difference between the pump and the probe lightwave. In practice, a modification of the shape of the square-like waveform is required, because of the frequency response of both the LD itself and the current driving circuit. The manipulation way for the shape of the square-like waveform has been proposed and used. The details are described in the next section.
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
###### Abstract  
The elliptical paraboloid array plays an important role in precision measurement, astronomical telescopes, and communication systems. The calibration of the vertex distance of elliptical paraboloids is of great significance to precise 2D displacement measurement. However, there are some difficulties in determining the vertex position with contact measurement. In this study, an elliptical paraboloid array and an optical slope sensor for displacement measurement were designed and analyzed. Meanwhile, considering the geometrical relationship and relative angle between elliptical paraboloids, a non-contact self-calibration method for the vertex distance of the elliptical paraboloid array was proposed. The proposed self-calibration method was verified by a series of experiments with a high repeatability, within 3 um in the X direction and within 1 um in the Y direction. Through calibration, the displacement measurement system error was reduced from 100 um to 3 um. The self-calibration method of the elliptical paraboloid array has great potential in the displacement measurement field, with a simple principle and high precision.  
elliptical paraboloid array; self-calibration method; vertex distance; optical slope sensor; geometric relationship; relative angle +
Footnote †: journal: Applied Physics Letters
# AnyPlace: Automatic Gaze Alignment of the Teleported Avatar for MR Collaborative Environments
## 2 Related Works
### Adaptive Control in Collaborative Environments  
As previously mentioned, natural representation to resolve the environmental differences of the shared MR space to increase the effectiveness and quality of tele-conference systems needs to be established [1]. Many researchers have been striving to align different physical layouts and adapt the teleported avatar to remote environments. [PERSON] et al. proposed an automatic alignment method merging into shared workspaces between tworemote rooms [19]. [PERSON] et al. focused on adapting the position and motion of the teleported avatar into the local MR space according to scene matching with the corresponding information [1]. Interestingly, their results, with respect to adapting to the differences in remote environments showed significantly improved experience and communication effects for participants in MR tele-conference environments.  
More recently, a consistent gaze direction in effective face-to-face communication has been recognized as one of the most important elements [20]. [PERSON] et al. introduced effects of gaze direction with respect to the varying influence of display types, such as flat and spherical displays [21]. [PERSON] et al. investigated the effects of gaze visualization of a virtual human partner (or teleported avatar) with depth information; for example, the impact of depth gaze errors for collaboration, and use of a gaze visualization method such as a ray or a cursor [22]. They found that the addition of a 3D cursor enhanced participant's performance and achieved the highest preference rating.  
No comprehensive work has been done in this area in gaze alignment of teleported avatars in different shared remote environments. However, we applied approaches in previous works, for example, scene matching and evaluation methods, to assess quality for future MR tele-conference systems.
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 1 Introduction
### Motivations and Contributions  
Although some FC-based privacy-preserving schemes have been proposed, most of them only focus on privacy-preserving data aggregation, while few schemes implement privacy-preserving dynamic billing. Furthermore, in the existing cloud-based dynamic billing article [23; 25; 33], the SP can only calculate the total electricity bill of the user in one billing cycle but cannot calculate the electricity bill generated by different types of electrical appliances. In addition, to our knowledge, there is no current work to arbitrate disputed bills. Based on the above analysis, our goal is to propose an FC-based privacy-preserving data scheme that supports dynamic billing and arbitration. Specifically, it provides the following functionalities:  
* **Privacy-preserving aggregation:** In PPDB, each SM periodically generates electricity usage data for \(l\) types of appliances. Inspired by the scheme [6], we adopt a binary encoding function to construct the \(l\)-dimensional data, then encrypt the constructed data using an additive homomorphic variant of the ElGamal cryptosystem [34] and finally send the ciphertext to the FN. The FN then employs batch verification algorithms to check the validity of all the ciphertexts and sends the valid ciphertexts to the next level after aggregation, thus effectively saving on computation and communication overhead.
* **Secure dynamic billing:** Our PPDB supports secure billing based on real-time prices, which means that our solution can provide each user with their electricity bill based on prices that change over time and also prevent privacy leaks to other sides. It is worth noting that in PPDB, the SP is not only able to calculate the total electricity bill of the user in a billing cycle, but also to calculate the cost generated by each type of appliance, respectively. Users can dynamically adjust their energy use patterns in response to price changes, thus optimizing resource utilization.
* **Dispute arbitration:** We also introduced a trusted third party as an arbitration centre (AC) to arbitrate disputes between SP and users over their electricity bills. To our knowledge, this is the first scheme that considers the arbitration of billing disputes.  
The security of our scheme is analysed under the Decisional Diffie-Hellman (DDH) assumption and Computational Diffie-Hellman (CDH) assumption, which implies that under the chosen plaintext attack, our scheme can provide strong confidentiality, and under chosen message attacks, the adversary cannot forge a valid report. For evaluating the performance of the scheme, we conduct in-depth theoretical analysis and experimental comparison. The results show that the proposed PPDB has low computation and communication overheads.
## 5 Transmission Model of Multipath QKD Network-Based Zero-Trust Relays
### Efficiency and Security Trade-Off  
To balance security and efficiency, the performance metric \(E(A,B)\) jointly integrates security probability and the delay as follows:  
\[E(A,B)=\gamma\cdot P_{T}(A,B)-\eta\cdot D(A,B) \tag{10}\]  
where \(\gamma\) and \(\eta\) are the security and efficiency scaling coefficients, respectively. Moreover, \(D(A,B)\) represents the average transmission delay. The delay \(D(A,B)\) accounts for both path length and network load and can be calculated as follows:  
\[D(A,B)=\frac{\sum_{i=1}^{n}d_{i}\cdot(P(S_{i}))}{\sum_{i=1}^{n}P(S_{i})} \tag{11}\]  
where the numerator \(\sum_{i=1}^{n}d_{i}\cdot(P(S_{i}))\) represents the expected delay, considering the security probability as a weighting factor. Secure paths (higher \(P(S_{i})\) ) have more influence on the average delay calculation, reflecting realistic path utilization.  
This approach ensures a balance between delay and security that reflects real operational conditions. The paths used more frequently (secure paths) accurately dominatethe average delay calculation, and this logically aligns with the standard QKD network optimization practices.
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 2 Problem Formulation
### Ego-Localization  
Odometry estimation plays a crucial role in accurately mapping consecutive scans of the environment. However, regardless of the precision of the odometry measurement and the sensors used, there is an inevitable build-up of global drift over time. This drift can lead to distortions in the generated map and, thus, inaccuracies in global pose estimations [9]. To mitigate such drift issues, ego-localization is employed in the form of loop closure detections to identify previously visited locations. When an already visited location is detected, a scan matching procedure is carried out between the present and past scan ormap, resulting in an adjustment of the current pose based on the matching outcome. In graph optimization approaches, loop closure transformations are included as additional edges in the graph structure.  
Various techniques are employed in the detection of potential loop closures. Some methods employ the usage of positional sensors such as GPS, while others match a new scan to a scan from a set of stored scans to determine if an edge is added to the graph optimization. One common approach utilizes the Bag-of-Words (BoW) method [43]. This technique proves valuable for efficiently comparing perception scans, a critical aspect in the detection of loop closures within SLAM and analogous systems. The BoW technique treats each scan as a collection of visual words, succinctly represented as a single vector. These visual words encompass features such as edges, corners, and texture sections, extracted through algorithms like SIFT, SURF, ORB, or a trained network.  
Initially, a dictionary is crafted by aggregating features from diverse scans and organizing them through a cluster analysis, such as k-means clustering. Each cluster center subsequently serves as a distinct 'visual word.' When a new scan is recorded, its features are extracted and matched against the visual words, or cluster centers, present in the dictionary. This results in the representation of the scan as a vector, illustrating the frequency of each visual word within the scan. During the ego-localization process, a BoW is employed to identify similarities between the current scan and known scenes, enabling the estimation of the position concerning previously visited locations. If the similarity between the current vector and a vector from the dictionary succeeds a previous defined threshold, scan matching is performed between the two scans to determine the transformation matrix and an edge is added to the graph. However, these edges have to be carefully chosen, as incorrect edges can lead to large deteriorations of the generated map [11; 12].
## 4 Results and Discussion
### Operational and Generalization Potential  
The proposed hybrid modeling framework demonstrates practical advantages. Its reliance on corrected synthetic discharge inputs significantly reduces dependence on real-time sensor data, ensuring continuity and robustness during sensor failures, equipment maintenance, or data collection gaps. This capability is particularly valuable in operational settings, facilitating consistent and reliable forecasting that supports informed decision making in real-time applications.  
The modular nature of this framework enhances its flexibility, allowing independent adjustment. Each component--the physical hydrological model, the bias correction method, and the deep learning prediction model--can be individually adjusted or replaced, offering water managers flexibility in adapting the framework to local conditions and available resources. This modularity not only improves interpretability but also supports straightforward adaptation and application across diverse geographic and management contexts.