esa-sceva/satcom-chunk-collection huggingface dataset:
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 4 Evaluation
### Results  
As seen in all three scenarios, reliability suffers in self-similar environments. A point must be reached where the geometry of an environment is sufficiently different from other poses. Only then can the localization be reliable, as the localization remains correct after the first correctly guessed pose. In our scenarios, the user had to move the device 15-20 m until a reliable pose could be estimated. The area covered is also related to reliability as first correct localizations were observed from 50-110 m\({}^{2}\). The reliability, thus, depends strongly on the geometry of the building one tries to localize in as well as on the amount and type of environment observed. Especially buildings with multiple self-similar  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & \(\overline{\Delta t_{xy}}\)** **(m)** & \(\overline{\Delta t_{z}}\)** **(m)** & \(\overline{\Delta\phi}\)** **(\({}^{\circ}\))** & **Frame** & **Distance Walked (m)** & **Area Covered (m\({}^{2}\))** \\ \hline Scenario 1 & 0.163 & 0.041 & 0.402 & 8 & 15.46 & 111.36 \\ Scenario 2 & 0.117 & 0.009 & 1.053 & 15 & 14.25 & 58.43 \\ Scenario 3 & 0.089 & 0.013 & 0.249 & 16 & 19.57 & 50.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the localization accuracy and reliability for each scenario: \(\overline{\Delta t}\) and \(\overline{\Delta\phi}\) are the average localization accuracy for correctly localized poses. \(\overline{\Delta t_{xy}}\) is the distance error over the template-matched \(xy\) plane. \(\overline{\Delta t_{z}}\) is the distance error of the ground-plane estimation along the \(z\)-axis. Frame is the point in time when the scenario was first localized correctly, with the according distance walked and area covered at that point.  
floors would suffer from reliability problems. The more distinct geometry is observed the higher the reliability gets.  
If localization is successful, the accuracy is in the range of 5-30 cm to a manually aligned point cloud. This difference can be attributed to inaccuracies during measurement as well as to drifts during recording caused by the device IMU. The naive ground estimation has an accuracy below 5 cm. Due to the rasterization process, the accuracy is bound by the rasterization grid size. A finer grid may improve localization but will decrease performance. This offers a trade-off between accuracy and performance.  
The performance of the algorithm is acceptable. On a computer with 4 cores at 3.7 GHz, a localization procedure of the tested scenarios takes less than 100 ms. On the HoloLens itself, the procedure takes about 500-1000 ms. This allows the localization to be considered in real time, as positioning in between localization frames can be handled by existing IMU systems. Since the procedure is highly parallelizable, it is scalable for future hardware. Thus, development in hardware will improve algorithm performance.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 8 Evaluation Results
### Micro Benchmarks
#### 8.2.3 Different Number of Threads  
Another method to accelerate computation is multi-threading. Figure 9 shows that \(P_{0}\) and \(P_{1}\) increased from a single thread to 8 threads to calculate \(\Pi_{\text{rank}}\). From the results, adding threads indeed continuously accelerates the speed of FSS keys calculation. The performance improvement at 2 and 4 threads is close to 2 and 4 times, respectively, but the benefit weakens when it is further increased to 8 threads.
# Autonomous Driving Control Based on the Perception of a Lidar Sensor and Odometer
## 4 Experimental Results  
In the simulation study, the Gazebo simulator of ROS was used to realize and verify the DRL methods. Furthermore, the TurtleBot3 model of Gazebo was also utilized in the experiments. During the process, Gazebo continuously updates all environment information, including the current position of TurtleBot3, the pose and the current position accumulated by the odometer, the information of the simulation environment scanned by the lidar sensor mounted on TurtleBot3, and so on, while TurtleBot3 interacts with the environment.  
For the simulation scenarios, two kinds of environments are shown in Figures 3 and 4, and the comparison between the two scenarios is presented in Table 1. The settings were as follows:  
1. The sizes were \(2\times 2\) and \(2\times 1\), respectively;
2. The initial positions were \((-0.65,-0.65)\) and \((-0.65,0.25)\);
3. The goals (marked with red) were located at \((0.65,0.65)\) and \((0.65,0.75)\);
4. There were two and one obstacles placed in each scenario, located at \((-0.5,0.5)\), \((-0.05,-0.25)\), and \((0.015,0.35)\);
5. The unit of all values presented above was meters;
6. Three learning approaches, DQN with five states, DQN with seven states, and DDPG were considered in the simulation;
7. Each procedure with the corresponding settings was executed 50,000 times;
8. A decaying exploration was also utilized, which started from 1 and was multiplied by 0.9999 every five actions taken by the agent, after 50,000 actions were achieved;
9. The specifications of the experimental computer were: CPU: Intel Core i7-10750H; RAM: 40 GB DDR4 2666 MHz, and GPU: Nvidia GeForce RTX 2070 GDDR6 8 GB.  
Figure 3: The first experimental scenario with larger size and two obstacles.  
First, the statistics of two scenarios are shown in Tables 2 and 3, where the information regarding the smallest number of steps to reach the goal of each simulation and the corresponding training time (round) is gathered. Note that a learning procedure for a setting was executed 50,000 times (50,000 rounds) and could be terminated due to the events of a collision or reaching the goal. For example, for the setting of DQN with five states in the first scenario, the UGV only required 63 steps to reach the goal in the 23,167 th round. This is the best record for the setting of DQN with five states in the first scenario. According to the statistics, DDPG had the best performance in two scenarios. The second-best performance was obtained from DQN with seven states, and the worst with DQN with five states. Hence, if a control strategy has more action states to choose, it shows better performance in controlling the UGV, i.e., fewer steps to reach the goal and less training time to find the smallest number of steps for reaching the goal.
## 8 Implementation and Performance Evaluation  
As part of the implementation considerations, we summarize the execution pipeline of the enhanced GN-AK protocol in the following schematic from Figure 1, highlighting the integration of used techniques.  
To evaluate the efficiency of the proposed protocol, we implemented both the original GN-AK scheme and our improved variant using optimized binary field arithmetic over the NIST K-163 Koblitz curve. The implementations were evaluated on two embedded platforms with constrained computational and energy resources:  
* ATmega328P: 8-bit AVR microcontroller, 16 MHz clock, compiled with avr-gcc 11.1.0 using size and speed optimizations.
* STM32F411 RE: 32-bit ARM Cortex-M4 microcontroller, 100 MHz clock, compiled with ARM-GCC 10.3 and hardware floating-point support disabled.  
For each platform, we benchmarked the scalar multiplication operation, which dominates the runtime in both protocol variants. Each data point represents the average of 1000 iterations using fixed-size test vectors, with results illustrated in Tables 1 and 2.  
Figure 1: Detailed architecture of the enhanced GN-AK protocol.  
Memory Usage: Both implementations require under 2 KB of SRAM and less than 12 KB of flash memory.  
Side-Channel Considerations: For clarification, constant-time field operations were used where applicable, and no data-dependent branches were included in critical cryptographic routines. However, we do not claim full resistance to side-channel attacks. No formal leakage analysis or power trace collection has been performed; this remains open for future investigation.  
These results support the performance claims and demonstrate that integrating double-base chain representations with point halving can significantly reduce computational overhead while maintaining protocol correctness and implementation feasibility on embedded systems.
# Online Signature Verification Systems on a Low-Cost FPGA
## 3 Hardware Implementations
### MicroBlaze Embedded System  
Figure 4 shows an overview of the embedded system based on MicroBlaze, a 32-bit soft-core general-purpose microprocessor for Xilinx FPGAs. It can be configured to better fit the computational requirements with the possibility of providing a single-precision FPU to accelerate floating-point operations. In this approach, the algorithm is written in C programming language, and single-precision IEEE 754 format is used. The C compiler automatically translates floating-point code to FPU instructions, and, therefore, the implementation or modification of the algorithm is reduced to a programming problem. Consequently, this implementation provides the lowest development effort but the worst execution time when compared against the other alternatives. The flexibility of the system is very high since the hardware can be reused for other biometric algorithms.
# Reducing System Load of Effective Video Using a Network Model
## 2 Related Work  
### SSAS (Spatial Segmented Adptive Streaming)  
SSAS, which uses an adaptive streaming method to reduce network traffic, is set up as an FoV that transmits the user's viewing area to the center. The FoV area viewed by the user transmits the original image quality, the original image quality is transmitted to the center of the FoV, and the lower the quality of the FoV video, the less network traffic is transmitted [3].  
Figure 1 shows the user screen treated according to the SSAS method. The brightest part means highest quality; the darker the color, the lower the quality of the video.
## 2 Numerical Analysis
### Analysis of Track Irregularities in Railway Structure
For the comprehensive analysis of track irregularities of subway lines and high-speed railway lines according to adjacent excavation, the numerical-analysis results and maintenance criteria of track irregularities were compared. The analysis showed that the vertical profile, alignment, cross level, and gauge exhibited sufficient stability with respect to the adjacent excavation.
## 3 NTNs for IIoT Applications
### State of the Art in Wireless Communication and Networking Technologies for NTNs  
Although the mean volume of data transmitted to and from IIoT nodes may not be substantial, greater bandwidths may be imperative to meet the stringent latency prerequisite. Additionally, new requirements for enhanced spectral efficiency, higher throughput, and sufficient bandwidth continuously emerge. In this respect, the multiple-input multiple-output (MIMO) [59] and massive MIMO [60] technology can revolutionize NTNs by exploiting spatial diversity and multiplexing as well as serving multiple ground nodes through multi-user (MU) MIMO [61]. In this direction, the extension of the DVB--Satellite to Handheld (DVB-SH) [62] and DVB--Next Generation Handheld (DVB-NGH) [63] standards to introduce MIMO configurations has been suggested with minor modifications. Besides the above, the mm-wave frequencies have been proposed as key enablers of 5G and IoT systems to attain sufficient bandwidth [60]. As the performance of radar systems is limited by target scintillations, the application of MIMO techniques to synthetic aperture radars (SARs) can also greatly improve the resolution and sensitivity as well as detection and estimation performance of NTN-based applications for the IIoT by exploiting the diversity of target scattering [64]. Data rates in the multiigigabit regime can be achieved using free-space-optical (FSO) inter-satellite, inter-platform, satellite-to-platform, satellite-to-ground, and platform-to-ground connections [65, 66]. Additionally, non-orthogonal multiple access (NOMA) represents a promising solution to facilitate the evolution of mMTC and improve the spectral efficiency by enabling non-orthogonal data transmission and exploiting successive interference cancellation (SIC) [42]. To update the quality of service (QoS), mobile-edge computing (MEC) represents a promising candidate technology [67]. The extension of MEC by exploiting the advances of NTNs can be performed in two directions: the introduction of the mobility of the MEC nodes and the multi-hop interconnection among the MEC nodes. As cloud and fog computing and networking can promote the cooperation of diverse networks, the UAVs' energy-efficient deployment for caching data in fog-based IoT systems was described in [68], focusing on a probabilistic/randomized content placement algorithm and using stochastic geometry. Additionally, software-defined radio (SDR), software-defined networking (SDN) [69], and network function virtualization (NFV) [70] could redefine the network architecture and bring flexibility and cost-efficient deployment and runtime of customized networks. In [71], SDN/NFV and microsatellite technology were integrated into the S-IoT concept, whereas SDN/NFV-enabled flying and ground moving networks were considered in [72]. The feasibility of multi-UAV SDN-enabled drone base stations (DBS) for emergency and surveillance monitoring scenarios was also studied in [73] and emphasis was placed on the effectiveness issues and cybersecurity aspects.
## 2 Background and Related Work
### First Approach Towards Fuzzy Compliance  
In the work shown above, an activity could be only compliant or non-compliant with the protocol. In [13], we propose to consider compliance of a single activity not as a binary state, but as a matter of degree. In this approach, each activity in a protocol is associated with a compliance level. For instance, consider an activity of drug administration. There are several dimensions in which it can be compliant with the protocol: the order, or the place in which it happened in the execution trace (cf. process mining), the timing (did it happen in the right time frame after, e.g., the previous activity), the dose, etc. For this illustrative case, let us consider the dose dimension only. In this case, the compliance level can be defined using a fuzzy set with a trapezoidal membership function, e.g., as shown in Figure 2, with four clearly defined threshold points in which dose deviation is perfectly OK (compliance degree of 1) and dose deviation is completely not OK (compliance degree of 0). For the values in-between, we use linear interpolation. The use of such membership function was already advocated by [PERSON] [65] since it is easy to understand by domain experts and provides a good compromise between a so-called cointension and computational complexity. In this context, cointension indicates the degree between the explicit semantics, defined by the formal parameter settings of the model and the implicit semantics conveyed to the reader by the linguistic representation of knowledge.  
In [13], the compliance degree of the protocol refers to the average compliance levels of all the activities performed within this protocol for all patients. In this paper, we propose to extend the fuzzy compliance metric proposed in [13] by taking into consideration the patient (trace) and importance of the activities.
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 6 Experimental Results and Discussion
### Proposed Merging-Squeeze-Excitation Feature Fusion  
The performances of the proposed MSE feature fusion in Section 4 and its extensions in Section 5 are shown in the following subsections. For each proposed architecture, we will compare the accuracy scores among the merging methods (addition, maximum, minimum, and average) and DL models (LeNet5, AlexNet, and VGG16) to determine which combination offers the highest accuracy score on classifying each dataset. Thereafter, the highest accuracy scores of the proposed architectures are compared to determine the best architecture. Note that the reduction ratio \(r\) is fixed to eight for all experiments. Varying \(r\) is considered as future work.
# HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks
## 6 Limitations and Future Work  
HinPhish achieved phishing attack detection on our data set by constructing a HIN of links on a webpage, then applying a specific algorithm. However, there may still be some potential problems for its application in the targeted environment. We discuss the limitations of the system and our future work in this section.  
First, richer information is required. There are still a small number of websites that have too few connections (i.e., only one or two links, or even no links). In this case, HinPhish may exhibit a poor performance. In order to solve this problem, we plan to further crawl the secondary or tertiary links of the webpage to obtain more link relationships, in order to enrich our HIN.  
Second, better parameter settings should be obtained. As mentioned in the article, in HinPhish, we merge foreign and null links into the outlier relation; that is, we do not distinguish between them. In addition, when constructing the relationship matrix, we only provide an empirical manner of setting the weights. In theory, there may exist an optimal setting method, which can further improve the detection accuracy. Although this was not the focus of this article, we will consider using statistics or other machine learning methods to find better parameter setting methods in the future.  
Third, experiments with larger data sets and that detect real websites should be conducted. In our experiment, our sample size was only about 40,000 (including about 20,000 phishing websites and about 20,000 legitimate websites). In the future, we intend to verify the proposed method on more data sets, in order to enhance its generalization ability. In addition, we are also considering applying this method to real, unmarked websites, in order to assist security personnel in their analyses.
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 4 Experiment and Results
### Quantitative Analysis  
In this study, we selected several classic trajectory prediction methods for comparison, including Social-GAN [28], Social-BiGAT [24], Sophie [40], SCAN [41], Social TAG [17], Social-STGCNN [31], D-STGCN [23], SISGAN [27], and STGAT [9]. The comparative results of our method with these models are shown in Table 2, which lists the comparison results of the Average Displacement Error (ADE) and Final Displacement Error (FDE) for all the methods. The best results in the table are marked in bold. The comparison results indicate that on the ETH and UCY datasets, compared with these classic prediction approaches, the proposed method outperforms them in terms of their prediction performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **ETH** & **HOTEL** & **UNIV** & **ZARA1** & **ZARA2** & **AVG** \\ \hline Social-GAN & 0.81/1.52 & 0.72/1.61 & 0.60/1.26 & 0.34/0.69 & 0.42/0.84 & 0.58/1.18 \\ Social-BiGAT & 0.69/1.29 & 0.49/1.01 & 0.55/1.32 & **0.30**/0.62 & 0.36/0.75 & 0.48/1.00 \\ Sophie & 0.70/1.43 & 0.76/1.67 & 0.54/1.24 & **0.30**/0.63 & 0.38/0.78 & 0.54/1.15 \\ SCAN & 0.84/1.58 & 0.44/0.90 & 0.63/1.33 & 0.31/0.85 & 0.37/0.76 & 0.51/1.08 \\ Social-TAG & 0.61/1.00 & 0.37/0.56 & 0.51/0.87 & 0.33/0.50 & 0.30/0.49 & 0.42/0.68 \\ Social-STGCNN & 0.64/1.11 & 0.49/0.85 & 0.44/0.79 & 0.34/0.53 & 0.30/0.48 & 0.44/0.75 \\ D-STGCN & 0.63/1.03 & 0.37/0.58 & 0.46/0.78 & 0.35/0.56 & **0.29**/0.48 & 0.42/0.68 \\ SISGAN & 0.63/**0.95** & 0.58/1.62 & 0.50/1.10 & 0.31/0.68 & 0.30/0.73 & 0.46/1.01 \\ STGAT & 0.65/1.12 & 0.35/0.66 & 0.52/1.10 & 0.34/0.69 & **0.29**/0.60 & 0.43/0.83 \\ DSGAT(our) & **0.60**/0.97 & **0.34**/**0.54** & **0.42**/**0.76** & 0.31/**0.49** & **0.29**/**0.47** & **0.39**/**0.64** \\ \hline \hline \end{tabular}
\end{table}
Table 2: This table uses Average Displacement Error (ADE) and Final Displacement Error (FDE) as evaluation metrics (in meters) to compare baseline methods on the ETH and UCY datasets.  
Specifically, compared with the best-performing model, Social-TAG, our method reduces the ADE and FDE metrics by 7.14% and 5.88%, respectively. Compared with the classic Graph Neural Network method, Social-STGCNN, our method reduces the ADE and FDE metrics by 11.36% and 14.67%, respectively. On the hotel dataset, our model shows a significant advantage over Sophie and SISGAN. Although these two models enhance the model performance by introducing scene context information, making them competitive in some cases, they also introduce some scene interference, resulting in poor performance on the hotel dataset. As shown in Table 2, compared with other datasets, our method shows the most significant improvement on the univ dataset. This observation demonstrates that our directed relationship and group attention functions effectively extract interaction information in crowded scenarios.
## 1 Introduction
### GPP Products Derived from Different Models  
We collected multiple global GPP products, including four LUE GPP products, two data-driven GPP products, and two process model products, to compare with the GPP simulations in this study, as shown in Table 1. Among them, the FLUXCOM GPP, LPI-GUESS GPP, and SDGVM GPP were resampled to 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) using a bilinear interpolation method.
## 4 Vibration Properties of the Proposed Metamaterials
### Analysis of the Influence of Design Parameters
#### 4.2.2 The Influence of Metal Pin Size  
As indicated from Figure 6e,f, the change in the structure mass distribution will affect the bandgap characteristics. Figure 9 depicts the bandgap distributions of the CDCHM inserted with metal pins of different sizes, and the material of the pins is copper. The introduction of copper pins produces local resonance effect between the ring and the copper pins. With the increase in the radius of the copper pins, the local mass of the CDCHM increases, and the local resonance effect is gradually enhanced, resulting in the improvement of the bandgap properties of the CDCHM in the range under 10,000 Hz. Among them, the lower limiting frequency of the first bandgap is reduced to about 1800 Hz, and the width of the first bandgap increases obviously. The proportion of all bandgaps at 1-10,000 Hz increased from about 45.4% to 64.2%. Moreover, when the radius is increased to 8 mm, the proportion of all bandgaps reaches about 71%, and the lower limit of the first  
Figure 8: The influence of the ring radius on the bandgaps: (**a**) structure diagram of the CDCHM; (**b**) R = 4 mm; (**c**) R = 5 mm; (**d**) R = 6 mm; (**e**) vibration mode of the first gap.  
bandgap frequency is 1247 Hz, which indicates that increasing the size of the added metal pin is of great benefit to improve the vibration-suppression ability of the metamaterial without changing the overall structure size.