esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 System Evaluation
### Security Analysis
#### 6.1.2 Search Unlinkability  
In the SEPIM design, each search query has its different encryption key. Consequently, two search queries have different encrypted search vectors \(z\) even if they are from the same query image, thereby guaranteeing the unlinkability of different search queries.
# LiDAR-Based Dense Pedestrian Detection and Tracking
## 2 Methodology
### Dense Pedestrian Tracking
#### 2.4.3 Track Management Considering Occlusion  
Usually, the purpose of the association is to match detections with predicted trajectories. In traditional trajectory management strategy, a new trajectory will be initialized if a target is detected in successive frames. An existing trajectory survives when there is detection matched with it. Alternatively, an existing trajectory will be terminated if there is not any detection matched with it for a long time. However, when a target is occluded and not detected for a long time, its corresponding trajectory will be terminated, and when the target reappears, a new trajectory will be initialized, which will cause the trajectory of the occluded target to be unstable. In order to solve this problem, we make some improvements via trajectory management considering occlusion. There are four trajectory statesfor the occluded trajectory: candidate trajectory, matched trajectory, occluded trajectory, and terminated trajectory. As shown in Figure 2, these states can transfer to each other. The corresponding conditions in Figure 2 are as follows:  
If a pedestrian is continuously detected in \(T_{1}\) successive frames, initiate a new trajectory and add it to the matched trajectories.  
If a matched trajectory has matched detection in the current frame, it keeps the state of matched trajectory.  
If a matched trajectory can not match with any detection in the current frame, and its predicted position is in occlusion, it turns to be an occluded trajectory.  
An occluded trajectory keeps its state if there is no detection matching with it in a short time, such as no more than \(T_{2}\) frames.  
An occluded trajectory turns to a matched trajectory once it is matched with a detection. A matched trajectory will be terminated if it has not matched with any detection in \(T_{3}\) consecutive frames and it was not occluded.  
An occluded trajectory will be terminated if it has not matched with any detection for \(T_{2}\) successive frames.  
The virtual scan can be used to detect occlusion [44]. After ground elimination and gridding, the scanning area is divided into free, occupied, and occluded sub-regions. When a target falls into the occluded area, it means that it will be blocked or partially blocked. If a pedestrian is missed in the occluded area, the parameter \(T_{2}\) will be enabled to judge the transition of tracking state, otherwise \(T_{3}\) is enabled.
## 7 Discussion
### Direct EAT Segmentation vs. Segmentation via Pericardium Delineation  
The two main ways to segment EAT are either to segment it directly, or to segment the pericardium and then segment EAT from the region inside the pericardium. Using the method of first delineating the pericardium, [PERSON] et al. [36] achieves a mean Dice coefficient of 0.912. This is a better result than [PERSON] et al. [38] achieved on the same dataset (AUC = 0.87), suggesting that deep learning methods benefit from delineating the pericardium before proceeding with EAT segmentation. This conclusion is also corroborated by the experiments in [PERSON] et al. [36]. When training other common segmentation networks to directly segment EAT, the best Dice coefficients the authors achieve are 76.59% for U-Net and 76.68% for Seg-Net.
## 3 Material and Methods  
MGEX collects multi-GNSS broadcast navigation messages with globally distributed GNSS receivers. These navigation messages are recorded into daily archives which are available at the IGS website: [[ftp://cddis.gsfc.nasa.gov/pub/gps/data/campaign/mgex/daily/rinex3/](ftp://cddis.gsfc.nasa.gov/pub/gps/data/campaign/mgex/daily/rinex3/)]([ftp://cddis.gsfc.nasa.gov/pub/gps/data/campaign/mgex/daily/rinex3/](ftp://cddis.gsfc.nasa.gov/pub/gps/data/campaign/mgex/daily/rinex3/)). BDS broadcast navigation messages are subsumed into merged navigation files since DOY 42, 2013. This paper retrieved six-year MGEX archived broadcast navigation message files from 2013 to 2018. Orbit accuracies of MGEX BDS-2 precise products were reported as several decimeters, one decimeter, and centimeters for GEO, IGSO, and MEO satellites, respectively [21]. As for precise clock products, about 0.8/0.3/0.2 ns precision could be obtained for the GEO, IGSO, and MEO satellites, respectively [22]. As the MGEX precise orbit/clock products are significantly better than the broadcast navigation messages, the BDS-2 precise orbit/clock products generated by Wuhan University, one major IGS MGEX contributor, were selected as the reference for broadcast navigation message performance assessment.  
After all necessary datasets were collected, a comprehensive evaluation method was proposed to assess the six-year BDS-2 broadcast navigation messages. Figure 2 shows the evaluation flowchart. Firstly, a preprocessing step was carried out to exclude invalid data. The statistics of broadcast navigation message availability were then provided. Secondly, a three-factor (URAI, space vehicle health flag, and external precise product) anomaly detection step was developed to explore all existing broadcast navigation message anomalies. Finally, a multi-SIS-URE assessment step was performed to investigate the overall BDS-2 orbit/clock performance.  
Figure 1: The second-generation of the Beidou Navigation Satellite System (BDS-2) satellite launching and deactivation status.
## 5 Results and Discussion
### Semantic-Matching Models
Using domain-specific data from the mental health field, we fine-tuned the RoBERTa model to create a new FT_RoBERTa model, which was then tested on the same 15 datasets. As shown in the scatter plot in Figure 15, the fine-tuned FT_RoBERTa model achieved an average cosine similarity score of 0.9 for semantic relevance, an improvement of 0.02 compared to the original RoBERTa model (0.88). According to the calculation in Equation (7), both models achieved a precision score of 0.87, indicating that the fine-tuned FT_RoBERTa model performed well and showed a slight improvement. Several groups of data selected through the semantic-matching model are combined with advanced natural language processing (NLP) technology to generate personalized medical advice. This advice includes treatment suggestions, recommended medications, lifestyle advice, and mental health guidance, all highly accurate, complete, and consistent,  
Figure 14: Cosine value score. Figure 13: Euclidean distance score. matching the patient's personal circumstances and derived from solutions provided by experts in the relevant medical field.
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 3 Experimental Design
### Experimental Setup and Procedures
#### 3.2.2 Cycling Test  
In the cycling test, the experimental setup was essentially the same as the shaker test, but the source of excitation was replaced by the leg of a human (male adult weighing 60 kg) performing cycling activity. The PD attached to the cantilever beam was attached to the right leg of the subject, as shown in Figure 11. As mentioned in [42], although there are many movable parts in a human body, the harvester is preferably attached to the leg of a human to maximise the energy harvesting capability.  
Measurements were taken for a range of cycling speed, in terms of revolution per minute (RPM), namely 40, 50, 60, 70, and 80 RPM. The voltage produced by the PD was similarly rectified, measured, applied to different resistances (R1, R2, R3, and R4) and stored in a storage capacitor (C1), as summarised in Table 2.
## 3 The Conceptual Framework Proposed  
### The Five-Step Process of Verification Using Living Labs  
Prior studies have suggested that the key characteristics of living labs include methods, tools, and approaches, and that living labs are suitable for developing, validating, and testing technologies [32]. However, no special methods have been studied on how to use living labs to verify certification test methods and qualification criteria. This study proposes that the use of living labs as a methodology for the verification purposes can be specified in the detailed procedures shown in Table 1. Specifically, the entire procedure consists of five steps: selecting a product item (NCP) and its test methods and criteria that need verification; analyzing the real-use environments of the product; implementing living labs based on the analysis; conducting tests or experiments for the verification in the living labs; and analyzing the results and discussing the implications. Firstly, the product item for verification can be selected in terms of the following factors: whether the quality and safety concerns about the item are high due to the novelty and the level of technological convergence; whether some of the test methods and certification criteria are not perfect enough to reduce those concerns; whether the verification requires tests or experiments in the real-use environments of the product; finally, whether the verification using living labs is actually feasible. Next, once the item and its verification targets (i.e., specific test methods and threshold criteria) are determined, the next step is to analyze the real-use environments of the product and to draw design implications for living labs. This step (Step 2) is one of the most important steps of the whole process, directly affecting the effectiveness of living labs for the verification. Since this step provides the conceptual layout of living labs, the analysis of the actual use environments should be mutually exclusive and collectively exhaustive (MECE). This is why this study focuses mainly on this step. The framework presented in this study provides an effective way to analyze the real-world environment of the product use in three aspects (i.e., user context, technological context, and physical context). The framework also helps derive the primary components of living labs and related design implications. Moreover, it guides you through how to determine the priority among the key living lab components in order to actually implement in the next step. After the living labs are configured based on the implications from the preceding analysis, planned tests or experiments are then conducted in the setting of living labs, followed by analyzing the test
# Two-Dimensional Interference Estimator with Parallel Structure for Holographic Data Storage Channel
## 6 Conclusions  
In this study, we proposed a 2D interference estimator. Firstly, we extracted the interference by the estimator using 2D VA. In this estimator, the 2D VA is used to detect the symbols, which are the sum of the product between the signal and the interference. Based on these symbols, LRI and ULI are estimated depending on the horizontal or vertical directions. With estimating LRI, we can remove it from the received signal and achieve the signal with the VI. By estimating ULI, we can achieve the signal with the HI. Therefore, we achieve the parallel structure, which includes the HI and VI. To improve the performance, we used the average function to combine the information from the horizontal and vertical processes (or HI and VI). The simulation results indicate that our proposed method can achieve a high gain compared to the models proposed in previous studies. Generally, in the proposed model, we exploit the 2D type target for extracting more accurate interference information between the equalizer output and the desired signal. Based on the target coefficients, we use interference estimator to convert 2D interference into 1D interference and utilize SOVA more effectively.  
In the near future, we will replace the SOVA with the maximum a posteriori (MAP) algorithm. This makes the extrinsic information from two detectors be supplied to the prior information of the opposite MAP detectors.  
Conceptualization, T.A.N. and J.L.; methodology, T.A.N. and J.L.; software, T.A.N.; validation, T.A.N. and J.L.; formal analysis, T.A.N.; investigation, T.A.N. and J.L.; writing--original draft preparation, T.A.N.; writing--review and editing, T.A.N. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government. (MSIT) (2021R1A2C1011154).  
Not applicable  
The authors declare no conflict of interest.  
Figure 8: BER performance of the proposed model according to the ON and OFF ratio at SNR = 15 dB.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Design, Analysis, and Simulation of RC-NB-LDPC Codes
#### 4.1.2 EXIT Chart Analysis of RC-NB-LDPC Codes
The weights are given by the coefficients of the degree distribution polynomials \(\lambda(X)\) and \(\rho(X)\) (\(\lambda(X)=\sum_{d=1}^{d_{v}}\lambda_{d}X^{d-1}\) and \(\rho(X)=\sum_{d=1}^{d_{c}}\rho_{d}X^{d-1}\)), where \(\lambda\) is the proportion of edges in the Tanner graph connected to a \(d\) degree variable node, and \(\rho\) is the proportion of edges connected to a \(d\) degree check node. Moreover, \(\lambda(1)=\rho(1)=1\). Therefore, for irregular LDPC codes:  
\[I_{ev}=\sum_{d=1}^{d_{v}}\lambda_{d}I_{ev}(d,I_{av}) \tag{14}\]  
where \(I_{ev}(d)\) is given by (14) and just \(d_{v}\) needed to be replaced in the formula with \(d\). \[I_{ec}=\sum_{d=1}^{d_{c}}\rho_{d}I_{ec}(d,I_{ac}) \tag{15}\]  
where \(I_{ec}(d)\) is given by (15) and just \(d_{c}\) needed to be replaced in the formula with \(d\). These equations can be used to calculate the EXIT curves for VND and CND. For a given SNR and a set of degree distributions, increase the SNR if the two curves intersect. Otherwise, the SNR would be decreased. The SNR that makes them exactly intersect is the decoding threshold. Figure 14 shows the EXIT curve of the designed LDPC code when the rate is 3/4. It can be seen that when \(E_{b}/N_{0}\) = 2.41 dB, the two curves do not intersect, indicating that the decoder converged and the decoding threshold is 2.41 dB. The decoding thresholds of the other rates of the designed LDPC codes and the DVB-S2 short-code are compared in Table 4. It can be clearly seen that the designed LDPC codes are better than DVB-S2 at each rate. Figure 13: Block diagram of the LDPC decode algorithm.
## 3 Results
### Quantitative Analysis of Observation Space  
#### 3.3.1 Single-Agent Observation Dispersion  
The dispersion metrics in Table 3 (calculated with K = 100 clusters) quantify the t-SNE visualizations. In Freeway and Enduro, all five agents exhibit consistently high entropy (\(H\)), effective support size (\(N_{eff}\)), and coverage (Cov), indicating they all explore the state space broadly and similarly. For example, all Freeway agents cover nearly 100% of the defined clusters. In high-variance games, these metrics exhibit significant differences between agents. In KFM, the lowest-performing agent (#5) has a much lower entropy (3.52) and effective size (33.74) compared to the others (H > 4.2, \(N_{eff}\) > 69), quantitatively confirming its limited exploration seen in the t-SNE plot. In Boxing, there is no simple correlation between performance and dispersion (e.g., agent #3 has the lowest dispersion, while agent #1 has moderate dispersion), suggesting that the quality and uniqueness of the explored region, not just its size, are critical. In Qbert, the top three agents (which all achieved the max score) have higher dispersion than the lower-performing agents #4 and #5.  
Figure 3: Qualitative t-SNE analysis of observation space representations from five distinct agents in four Atari games: (**a**) Freeway, (**b**) KungFuMaster, (**c**) Pong, and (**d**) Qbert.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Agent** & \(H\) & \(N_{eff}\) & **Cov** & **G** \\ \hline Alien \#1 & 3.41 & 30.33 & 0.52 & 0.95 \\ Alien \#2 & 3.75 & 42.70 & 0.76 & 0.97 \\ Alien \#3 & 3.77 & 43.17 & 0.75 & 0.97 \\ Alien \#4 & 3.39 & 29.81 & 0.70 & 0.94 \\ Alien \#5 & 3.60 & 36.62 & 0.60 & 0.96 \\ \hline Boxing \#1 & 3.12 & 22.58 & 0.38 & 0.95 \\ Boxing \#2 & 3.77 & 43.52 & 0.70 & 0.97 \\ Boxing \#3 & 2.82 & 16.78 & 0.26 & 0.94 \\ Boxing \#4 & 3.80 & 44.92 & 0.63 & 0.97 \\ Boxing \#5 & 3.90 & 49.29 & 0.62 & 0.98 \\ \hline Breakout \#1 & 2.90 & 18.14 & 0.50 & 0.88 \\ Breakout \#2 & 2.79 & 16.21 & 0.51 & 0.87 \\ Breakout \#3 & 2.73 & 15.40 & 0.45 & 0.87 \\ Breakout \#4 & 2.86 & 17.54 & 0.52 & 0.88 \\ Breakout \#5 & 2.73 & 15.38 & 0.38 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Single agent dispersion statistics (K = 100).
# Spatially Adaptive Regularization in Image Segmentation
## 2 Defining Local Regularization by Exploiting Spatial Information
### Regularization Based on the Mean and Median Filters  
We define a technique that is based on spatial filters that are commonly used to enhance low-frequency details or to preserve edges [31; 32]. More precisely, we combine the mean and median filters; the former aims at identifying smooth regions, where the regularization parameter can take small values, the latter aims at identifying edges, where the parameter must remain large. Mean filtering is a simple and easy-to-implement method for smoothing images, i.e., for reducing the intensity variation between a pixel and its neighbors. It also removes high-frequencies components due to the noise and the edges in the image, so the mean filter is a low-pass filter. The median filter preserves edges and useful details in the image.  
Based on these considerations, we define a weight function, as follows:  
\[\omega_{i,j}=\left\{\begin{array}{rl}|\bar{u}_{i,j}-(L_{h1}*\bar{u})_{i,j}|& \text{if }|(L_{h1}*\bar{u})_{i,j}-(M_{h2}*\bar{u})_{i,j}|<t,\\ 1&\text{otherwise.}\end{array}\right. \tag{10}\]  
where \(h_{1}\) is the window size of the mean filter \(L_{h1}\), \(h_{2}\) is the window size of the median filter \(M_{h2}\), and \(t\) is a threshold value acting as a cutoff between the two filters. Note that \(0\leq\omega_{i,j}\leq 1\) and the pixels in homogeneous regions have \(\omega_{i,j}\) close to 1. The function \(f\) in (6) is set as  
\[f(\bar{u}_{i,j})\equiv f^{\text{MM}}(\bar{u}_{i,j})=\max\left\{\frac{\lambda_ {min}}{\lambda_{max}},\,1-\omega_{i,j}\right\}\lambda_{max}, \tag{11}\]  
where MM stands for \"Mean and Median\".
## 2 Data Used for Typhoons Hagibis and Bualoi  
### Data Used  
The more realistic each of the OSSE components are, the more consistent the results will be with experiments using real observations. Thus, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) [35] data was used to generate the reference ('true') atmosphere or Nature Run (NR) of the GEO-MW OSSE and to evaluate the forecast skill scores. ERA-5, the successor to ERA-Interim, is the 5 th major atmospheric reanalysis produced by the new generation of ensemble 4D-Var data assimilation (DA) technology, which represents 10 years of progress made in modeling and data assimilation since the production of ERA-Interim. The ERA-5 dataset provides high-resolution global reanalysis data of surface and upper-air parameters at an approximate 31 km global resolution on 137 levels reaching up to 0.01 hPa and a time resolution of 1 h.  
In addition, another reanalysis dataset, Final Analysis (FNL) [36] data, was used to generate the background field of the WRFDA-4 DVar assimilation and the initial field of the control experiment in the GEO-MW OSSE. Since 1999, FNL data have been provided as the final analysis by the National Oceanic and Atmospheric Administration (NOAA), the National Centers for Environmental Prediction (NCEP) located in Maryland for researchers. NCEP/FNL reanalysis data have a horizontal resolution of 1.0\({}^{\circ}\)\(\times\) 1.0\({}^{\circ}\) and temporal resolution of 6 h (observed at 0000 UTC, 0600 UTC, 1200 UTC, and 1800 UTC), which involves 31 pressure levels in the vertical direction, including 5 pressure levels (1000, 950, 925, 900, and 850 hPa) in the boundary layer.  
To verify the track prediction of Typhoons _Hagibis_ and _Bualoi_, the typhoon best tracks provided by the Tropical Cyclone Data Center (TCDC) of the China Meteorological Administration (CMA) [37; 38] were used as the truth for typhoon prediction in the GEO-MW OSSE. Time, longitude, and latitude can be obtained from CMA data, which are available via [[https://tcdata.typhoon.org.cn/zjljsj_zlhq.html](https://tcdata.typhoon.org.cn/zjljsj_zlhq.html)]([https://tcdata.typhoon.org.cn/zjljsj_zlhq.html](https://tcdata.typhoon.org.cn/zjljsj_zlhq.html)) (accessed on 21 March 2021).
# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 3 Model Established for NSR Resilience Measurement
### Resilience Capacity Decomposition for the Arctic Northeast Route  
The resilience capacity can be defined as the capacity of a certain system to absorb, adapt to, and recover from any shock due to a disruption [23]. It is widely accepted that resilience capacities are classified as the absorptive capacity, adaptive capacity, and restorative capacity for a specified system [25; 27]. In the present study, the underlying factors pertaining to these three kinds of capacities for the Arctic NSR resilience are identified, as illustrated in Figure 7. All the factors presented in Figure 7 will be included in the developed BN.
## 3 Experiment Results and Analysis
### Nonlinear Evolution Characteristics of Seepage-Erosion Process
Through observation of the grain-size distribution of eroded soil particles, the content of particles between 0.5 mm and 1 mm remains approximately the same as the content of the same grain-size particles in stage 2, but for the coarse particles over 1 mm, its content occupies 27%, almost the twice of the content of those in stage 2. By the end of \"transition flow\" stage, around 56% soil particles have been eroded away, resulting in nearly penetrating-through of seepage channels, which promotes the flow pattern transforming into pipe flow from porous media flow [26], hence the water-outflow rate maintains a relatively stable rate under specific hydraulic pressure. In this stage, the formation of penetration-through seepage channels partially decreases the lateral erosion ability of seepage flow, which not only results in a greater transferring rate than migration rate of soil particles, but also a slowly declining of sands mass concentration all along. It should be noted that, in real geo-conditions, karst cavity may be filled with abundant karst water, or even connected to underground river or surface water, the outflow discharge could suddenly and dramatically increase after the penetration through of seepage channels, resulting in the occurrence of water-sand inrush accidents. In consequence, this period can be named as \"pipe flow\" stage. After introducing the nonlinear evolution characteristics of seepage-erosion process, the reason behind the existence of outliers in Figure 7 should be discussed here. As we mentioned above, the mesh size of perforated plate II is set as 10.0 mm, ensuring at least 90% sand particles to pass through While, the particle clogging will appear when the particles over 10.0 mm approaching perforated plate II. Moreover, with the existence of medium size particles, the clogging will still theoretically appear when 2, 4, or many of those particles approaching the open hole together, which would retard the erosion of soil particles. However, due to the dynamic evolution characteristics of seepage-erosion progress, the particle clogging has a possibility to collapse under the disturbance of dynamic flow, which abruptly increases the mass of eroded particles, resulting in a sharp growth of sands erosion rate, as well as the fluctuations of water-outflow rate and sands mass concentration.