esa-sceva/satcom-chunk-collection huggingface dataset:
# An Interactive Transient Model Correction Active Sonar Target Tracking Method
## 3 Simulation Verification and Offshore Testing
### Offshore Testing
#### 3.2.2 Results and Analysis  
Assuming an RMSE of 100 m for the target's radial distance measurements and an RMSE of 1.0\({}^{\circ}\) for the azimuth measurements, both the conventional KF tracking method and the TMC tracking method were used to track the underwater target. A comparative analysis was conducted on the target's X-axis distance, Y-axis distance, radial distance, and azimuth obtained from the true values, measurements, KF tracking results, and TMC tracking results. The results are shown in Figure 7.  
Figure 6: True target trajectory and sonar measurement values.  
Figure 7: \(Cont\).  
Figure 7: Comparison of Target’s X-axis, Y-axis, Radial Distances, and Azimuths. (**a**) Comparison of X-axis distances, (**b**) Comparison of Y-axis distances, (**c**) Comparison of radial distances, (**d**) Comparison of azimuths.  
As can be seen from Table 3, after using KF tracking processing, the RMSE of the target's position distance and azimuth were reduced by 186.07 m and 0.73\({}^{\circ}\), respectively, compared with the RMSE of the sonar measurements. Furthermore, when the TMC tracking method was applied, the RMSE of the target's position distance and azimuth were further reduced: compared with the KF tracking method, the RMSE of the target's position distance was reduced by 36.08 m, and the RMSE of the azimuth was reduced by 0.25\({}^{\circ}\).  
MSE analysis of the offshore testing results was carried out, and as can be seen from Table 4, the TMC tracking method reduced tracking error, and performance was due to the KF tracking method.  
Through analysis of the offshore testing data, it can be seen that the TMC tracking method can modify the target motion state equation in real time, so as to make it as close to the real target motion state as possible, rather than tracking according to the established target motion state equation as the KF method. Therefore, the TMC method can improve target tracking accuracy. When analyzing the offshore test data, if the measurement error of sonar on underwater target changes greatly, the performance of the TMC method may be affected. This is because, in a test, if the measurement error of sonar on an underwater target position changes greatly due to various factors (obviously beyond the  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Measured Values** & **KF Results** & **TMC Results** \\ \hline
**MSE of Radial Distance** & 50,354.2 & 38,001.1 & 36,578.9 \\ \hline
**MSE of Azimuth** & 4.15 & 1.71 & 1.12 \\ \hline \end{tabular}
\end{table}
Table 4: MSE of offshore testing.  
Figure 8: Comparisons of distance and azimuth errors. (**a**) X-axis distance errors, (**b**) Y-axis distance errors, (**c**) Position distance errors, (**d**) Azimuth errors.  
normal measurement error range of sonar), the TMC method cannot accurately determine whether this change is caused by a change in the submarine target motion state or an increase in measurement error.
## 5 Conclusions  
This paper validates the Cloud-RAN model with the split between MAC and PHY over Ethernet with additional reliability using MPC. As proof-of-concept for the proposed solution, we present an end-to-end Cloud-RAN testbed that integrates user equipment, RU, DU, CU, and a fronthaul network. We first analyse the performance of MPC in terms of latency and jitter using different packet sizes. The results were satisfying in terms of average RTT latency being compliant with the requirements of 3 GPP, that is, staying below 500 \(\upmu\)s. From the latency constraint perspective, the findings in this chapter demonstrate how the size of the packet has a significant effect on RTT. To lower the RTT, it is very important to choose the right size of the packet.  
Then, we compared the performance of MPC with MPD from a probability of error perspective. The experimental results show that MPC achieved an error probability of \(10^{-5}\) at a latency lower than MPD, that is, a latency reduction of 73 \(\upmu\)s. However, when one of the paths is simulated with an extra delay, MPC achieved an error probability of \(10^{-5}\) at a latency 20 \(\upmu\)s later than MPD. Nevertheless, MPC can deliver an error probability of \(10^{-5}\) while staying below the acceptable latency on the fronthaul for the MAC-PHY split, in addition to the advantage of MPC over MPD in terms of reducing fronthaul load. The results obtained in this paper are consistent with the results reported in [12, 13] in validating the effectiveness of MPC, opening the possibility to implement MPC as a means of improving reliability in a Cloud-RAN system.  
The deployment described in this paper is an experimental facility that may easily be expanded to a larger scale testbed in terms of network architecture, fronthaul network topology, and system traffic load.  
[PERSON] is the primary author; [PERSON] contributed in terms of problem formulation, providing ideas, and writing. Both authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the EU H2020 Primo-5G project. Additional support for this research is received form The Engineering and Physical Sciences Research Council (EPSRC) industrial Cooperative Awards in Science & Technology (iCASE) award and from BT and the EPSRC infrastructure grant, INITAITE (\(EP/P003974/1\)).
## 3 Retrieving Private Data with Optimization Processes on Homomorphic Encryption Schemes
### The Partitioning Process  
Since the data stored on the Server have no dependencies with each other, the Server can split the dataset \(\mathsf{X}\) into \(\alpha\) independent parts. We can then conduct the labeled PSI once for each part and send the result of each PSI operation back to the Client so that the Client will receive \(\alpha\) split-PSI operation results. There is an intersection if any split-PSI operation result is \(0\) and vice versa. After applying the Partitioning-based optimization, the complexity of the labeled-PSI protocol is given in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Ciphertexts that the Client** & **Number of Ciphertexts that the Server** & **Multiplication Depth of Applying** \\
**Needs to Send to the Server** & **Needs to Send Back to the Client** & **the HE Scheme** \\ \hline \(\left|\,\mathsf{Y}\,\mathsf{I}\,\mathsf{\cdot}(2^{\ell}-1)\,\left(\left| \mathsf{log2}(\left|\,\mathsf{X}\,\right|)/\ell\right|+1\right)\) & \(\left|\,\mathsf{Y}\,\mathsf{\cdot}(2^{\ell}-1)\,\left(\left|\mathsf{log2}( \left|\,\mathsf{X}\,\right|)/\ell\right|+1\right)\right|\) & \(\left[\mathsf{log2}(\left|\,\mathsf{X}\,\right|)/\ell\right|+1)\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Complexity of the Labeled PSI Protocol after Applying All Hashing-based Optimization (with s slots in each bucket).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Ciphertexts that the Client** & **Number of Ciphertexts that the Server** & **Multiplication Depth of Applying** \\
**Needs to Send to the Server** & **Needs to Send Back to the Client** & **the HE Scheme** \\ \hline \(\left|\,\mathsf{Y}\,\mathsf{I}\,\mathsf{\cdot}(2^{\ell}-1)\,\left(\left| \mathsf{log2}(\left|\,\mathsf{X}\,\right|)/\ell\right|+1\right)\) & \(\left|\,\mathsf{Y}\,\mathsf{\cdot}(2^{\ell}-1)\,\left(\left|\mathsf{log2}( \left|\,\mathsf{X}\,\right|)/\ell\right|+1\right)\right|\) & \(\left[\mathsf{log2}(\left|\,\mathsf{X}\,\right|)/\ell\right|+1)\right]Although the HE-related multiplication depth is indeed improved after applying the partitioning process, the communication cost of the Server to the Client has also increased by \(\alpha\) times. In our case, \(|Y|\) is a small value; we can still alleviate the increased communication cost due to partitioning. For this, we can use the following modulus switching technique.
# Discrete Integral Optimal Controller for Quadrotor Attitude Stabilization: Experimental Results
## 4 Experimental Results
### Comparative Analysis of Energy Consumption between OIC and Conventional PID Controller  
This experimental protocol enables a detailed evaluation of the efficacy of the integral control algorithm within an LQR control system applied to UAVs, providing empirical evidence of its performance, robustness and energy efficiency compared to a conventional PID controller. Total energy consumption was computed using potential and kinetic energies. This calculation was performed for both the tuned heuristic PID controller and the OIC optimal controller proposed in this research work. The total energy behavior for each scenario is illustrated in Figure 18. Furthermore, Table 1 presents the electrical energy savings comparison between the controllers. These data were derived from a series of 30 experiments.  
As evident in Table 1, the OIC (Optimal Integral Controller) improved the electrical energy consumption by 53.05% compared to a conventional PID controller. This proves that the use of a LQR (Linear Quadratic Regulator) controller in conjunction with integral action can save energy in a four-rotor aerial robot. In addition, it provides robustness to the control loop, enabling it to absorb unmodeled dynamics or external disturbances, as has been demonstrated in Section 4.2. Although these experiments were conducted in a controlled environment, it is clear that the control algorithm will work efficiently in outdoor flights, proving that the robot state approximation is accurate and the robot model is correctly parameterized.  
Figure 16: Numerical approximations of torque control signals using the OIC when the quadcopter was disturbed.  
Figure 17: Force control signal \(u(t)\) using the OIC applied when the quadcopter was disturbed.
## 2 Materials and Methods
### Methodology
#### 2.5.3 Estimation of Distance from Source-to-Site, PGA and Intensity  
[PERSON] and [PERSON] [37] developed the attenuation equation to calculate PGA. The required data for PGA estimation can be collected from the USGS earthquake catalog. Firstly, there is a requirement to calculate the source to site distance based on the epicentral distance according to the catalog, as presented in Equation (3). Similarly, the intensity values for each earthquake were estimated using Equation (5). Various researchers have proposed several attenuation equations, yet in this study we applied [PERSON] and [PERSON]'s equation [37]. Source to site distance can be estimated based on the following:  
\[\mathrm{D}=(\mathrm{E}2+7.32)0.5 \tag{3}\]  
where D = distance from the source to site and E = epicentral distance.  
[PERSON] and [PERSON] [37] presented the PGA estimation equation as:  
\[\mathrm{PGA}=10(0.249*M-\mathrm{Log}(\mathrm{D})-0.00255*D-1.02) \tag{4}\]  
where M = Earthquake magnitude. Many other attenuation laws can be implemented in a probabilistic seismic hazard assessment (PSHA) [38; 39; 40]. [PERSON] and [PERSON] [37] developed the general equation of regression, which is popular for its regional and worldwide use. However, the modified Mercalli intensity (MMI) was calculated from PGA values using the proposed equation:  
\[\mathrm{MMI}=1/0.3*(\log 10~{}(\mathrm{PGA}*980)-0.014 \tag{5}\]  
where the PGA unit is G (Gal).  
PGA variation and MMI variation were obtained for the 1800 events. Similarly, PGA and intensity variation were estimated for Odisha. We considered 32 locations from south to north that had a specific PGA value and intensity. All locations were chosen with STFT analysis and 32 was the minimum point to conduct the analysis. If we consider all values as peaks of a signal, then the PGA and intensity variation at all locations can be understood through STFT analysis. However, PGA and intensity variation were not involved in probability assessment, as these are hazard factors. In the next stage, seismic amplification mapping susceptibility conducted to identify high ground shaking locations.
# A Survey on Machine Reading Comprehension--Tasks, Evaluation Metrics and Benchmark Datasets
## 2 Tasks
### Discussion on MRC Tasks
#### 2.2.3 Machine Reading Comprehension vs. Other NLP Tasks  
There is a close and extensive relationship between machine reading comprehension and other NLP tasks. First of all, many useful methods in the field of machine reading comprehension can be introduced into other NLP tasks. For example, the stochastic answer network (SAN) [40; 41] is first applied to MRC tasks and achieved results competitive to the state of the art on many MRC tasks such as the SQuAD and the MS MARCO. At the same time, the SAN can also be used in natural language processing (NLP) benchmarks [42], such as Stanford Natural Language Inference (SNLI), MultiGenre Natural Language Inference (MultiNLI), SciTail, and Quora Question Pairs datasets. For another example, [PERSON] et al. (2017) [43] regards the document-level multi-aspect sentiment  
Figure 5: The relations between machine reading comprehension (MRC), question answering (QA), natural language processing (NLP) and computer version (CV).  
classification task as a machine understanding task, and proposed a hierarchical iterative attention model. The experimental result of this model outperforms the classical baseline in TripAdvisor and BeerAdvocate datasets.  
Secondly, some other NLP research results can also be introduced into the MRC area. [PERSON] et al. (2018) [44] solved the task of non-English reading comprehension through a neural network translation (NMT) model based on attention mechanism. In detail, the paragraph question pair of non-English language is translated into English using the neural machine translation model, so that the English extraction reading comprehension model can output its answer, and then use the attention weights of the neural machine translation model to align the answers in the target text. Extra knowledge can also be introduced into MRC tasks. The authors of SG-Net [45] used syntax information to constrain attention in the MRC task. They used the syntactic dependency of interest (SDOI) to form an SDOI-SAN and have achieved state-of-the-art results on SQuAD 2.0 challenge. [PERSON] et al. (2020) [46] summarized more than 150 deep learning text classification methods and their performance on more than 40 popular datasets. Many of the methods mentioned in this article have been applied to MRC tasks.  
Thirdly, MRC can be used as a step or component in the pipeline of some complex NLP tasks. For example, machine reading comprehension can be used as a step in open domain QA [35]. And in many dialogue tasks, machine reading comprehension can also be regarded as a part of pipeline [8; 38; 47].
## 4 Field Experiment
### Results of 3D Modeling Utilizing the Proposed Internal Observation Method  
Safety management at construction sites can be vulnerable to accidents owing to the extensive deployment of personnel engaged in a variety of tasks over a large area. Site safety management faces limitations owing to the shortage of personnel to oversee expansive areas. Therefore, utilizing 3D models of construction sites is effective for simplifying the observation of spaces within the construction safety net that are typically difficult for safety managers to access and monitor. The 3D model was developed using improved images to aid safety management.  
Because the original image was 2D, only one side of the climbing formwork was observable, and the internal examination was hindered, owing to the presence of the construction safety net (Figure 16a). The image processing method was applied to 100 original images to enable the identification of internal objects, and these were combined via 3D modeling. The internal components of the climbing formwork, known for their limited accessibility and observation by safety managers, became visible from various perspectives through the application of 3D modeling (Figure 16b). It can be clearly confirmed that the inside of the climbing formwork, which could not be seen in the original image due to the safety net, could be observed through a 3D model using the improved image by the image processing method. Therefore, it can be seen that the proposed method is effective. It is noted that certain distortions were encountered during the 3D modeling process, which were attributed to insufficient image information for accurately matching the corresponding areas in the 3D model. This issue can be resolved by utilizing UAVs equipped with high-quality cameras or improving location accuracy by adding GCP.
# Performance Analysis of UAV-IRS Relay Multi-Hop FSO/THz Link
## 4 Numerical Results
This degradation is attributed to the utilization of serial IRS-UAV configurations as relays; a higher count of serial relays elevates the error probability within any given relay, thereby diminishing the overall channel capacity. Furthermore, the figure indicates that systems incorporating IRS technology exhibit superior performance compared to those without. Specifically, when the IRS count is set to four and the number of links to ten, the system's outage probability significantly outperforms scenarios where IRS is absent and the link count is four. This demonstrates that IRS deployment can effectively counteract the performance decline associated with an increased number of links. Figure 8: Variation curves of channel capacity with different modes, pointing errors, and atmospheric turbulence. Figure 9: Variation curves of channel capacity for different numbers of reflecting surface elements.
## 6 Simulation and Experiment
### Experiment
#### 6.2.2 Path Following for Sinusoidal
This experiment of curve path following illustrates the fact that _APLOS_ offers the satisfactory curve tracking performance compared with _LOS_. The look-ahead distance of _APLOS_ is adapted to the change of curvature. Meanwhile, _APLOS_ can effectively offset the influence of drift angle. Therefore, the feasibility and effectiveness of _APLOS_ for tracking complex paths are verified in practice.
## 4 Experiments and Analysis  
### Evaluation Metrics  
Two main metrics, F1 score and unweighted accuracy (UA), are used in this paper to assess the performance of the emotion feature encoding network. Below is an introduction to these two metrics.  
1. F1 score: The F1 score is a measure that takes into account both precision and recall, providing a balanced assessment of the model's performance. It is the harmonic mean of precision and recall, making it robust for datasets with imbalanced classes. Specifically, a higher F1 score indicates that the model maintains precision while achieving a higher recall, which means it handles the imbalance between positive and negative cases better. The equation for F1 is as follows. \[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}};\] (5)Unweighted accuracy (UA): Unweighted accuracy refers to the average accuracy of the model across all categories, without considering the number of samples in each category. This allows unweighted accuracy to fairly assess datasets with imbalanced classes, unaffected by sample distribution. By using unweighted accuracy, we can gain a more comprehensive understanding of the overall performance of the model across different categories. Its equation is as follows.  
\[Accuracy=\frac{\mathrm{T}}{\mathrm{T+F}}, \tag{6}\]  
where T represents the correctly predicted sample count, and F represents the falsely predicted sample count.  
For the emotion evaluation network, mean squared error (MSE) is chosen as the primary evaluation metric. MSE measures the average of the squares of differences between the model's predicted outputs and the teachers' scores. In the context of emotion evaluation, MSE provides a measure of the model's regression performance on emotion values.  
The selection of these evaluation metrics aims to provide a comprehensive understanding of the model's accuracy, recall, and adaptability to imbalanced data, offering a thorough assessment of model performance.
# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 4 Experiments and Results
### Comparative Evaluation  
Comparative evaluation with other automatic methods is carried out on Dataset II, and the results are listed in Table 4. SFCME-LSE outperforms existing methods in terms of the HD, with \(1.20\pm 0.66\) mm and \(1.18\pm 0.70\) mm for MAB and LIB detection, respectively. Additionally, our method has a competitive result over those published methods in terms of the JM and PAD. Moreover, SFCME-LSE has an important advantage of a simple implementation without the time-consuming extraction of complex texture features.  
Finally, a qualitative assessment is performed by comparing the segmentation and the ground truth on several exemplary frames extracted from Dataset I and Dataset II, as displayed in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MAB**} & \multicolumn{3}{c}{**LIB**} & \multicolumn{3}{c}{**Time(s)/Frame**} & \multicolumn{1}{c}{**Hardware**} \\ \cline{2-9}  & **JM** & **HD** & **PAD** & **JM** & **HD** & **PAD** & **PAD** & \\ \hline \multirow{3}{*}{SFCME-LSE} & 0.83 & 1.20 & 0.12 & 0.78 & 1.18 & 0.16 & 11.09 & Xeon 3.5 \\  & (0.10) & (0.66) & (0.11) & (0.10) & (0.70) & (0.15) & & GHz \\ \cline{1-1}  & 0.75 & 1.32 & 0.12 & 0.77 & 1.46 & 0.16 & & \\ \cline{1-1}  [20] & (0.13) & (0.99) & (0.12) & (0.13) & (1.23) & (0.15) & — & — \\ \cline{1-1}  & 0.83 & 1.27 & 0.12 & — & — & — & 272.92 & Core 4, \\ \cline{1-1}  & (0.09) & (0.67) & (0.13) & — & — & — & 2.67 GHz \\ \cline{1-1}  & 0.84 & 1.22 & 0.13 & — & — & — & — & — \\ \cline{1-1}  & (0.10) & (0.72) & (0.15) & — & — & — & — & — \\ \cline{1-1}  & \multicolumn{1}{c}{—} & — & — & (0.72) & 1.70 & 0.22 & & Core 2 \\ \cline{1-1}  & \multicolumn{1}{c}{—} & — & — & (0.12) & (1.09) & (0.14) & 13 & Duo, 2.13 \\ \cline{1-1}  & \multicolumn{1}{c}{} & _0.91_ & _0.85_ & _0.06_ & _0.92_ & _0.67_ & _0.05_ & & \\ \cline{1-1}  & _(0.07)_ & _(0.60)_ & _(0.07)_ & _(0.06)_ & _(0.52)_ & _(0.06)_ & & \\ \cline{1-1}  & _0.87_ & _1.14_ & _0.11_ & _0.86_ & _1.04_ & _0.10_ & & \\ \cline{1-1}  & _(0.11)_ & _(1.00)_ & _(0.14)_ & _(0.10)_ & _(0.55)_ & _(0.10)_ & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed SFCME-LSE method with other published automatic methods. Empty cells mean that the researchers have not given the corresponding result.  
Figure 7: Bland–Altman plots (the first column) and the linear regression results (in the second column) for the comparison of areas inside the border detected by the proposed method and the ground truth.
# Beyond 100G: All-Optical Processor for High-Capacity Access\(\sim\)Networks
## 1 Introduction
In this manuscript, we propose a novel approach to mitigate the limitations of migrating towards higher-speed PONs while exploiting the benefits of IM/DD solutions by introducing an all-optical processor (AOP). The AOP leverages the benefits of optical signal processing to enhance system efficiency and reduce power consumption compared to traditional digital methods. Specifically, we explore the dual functionality of the processor in performing all-optical equalization and chromatic dispersion compensation. This study presents a comprehensive analysis of the processor's design, operational principles, and system validation, demonstrating significant improvements in signal quality and overall network performance. The rest of the paper is organized as follows: In Section 2, we present an overview of the standards for 50G and the evolution towards 100G PON. Section 3 describes the operating principle and circuit design of the proposed AOP. In Sections 4 and 5, we detail the experimental setup and results. Finally, Section 6 concludes the work.