esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Erosion Prevention Model by Bio-Cement Sand
## 3 Results and Analysis
### Results and Analysis of Sink Scour Model
Fine particles were mobilized from the sand box by water flow; concurrently, larger particles settled within the bedload funnel while fine and micro-sized particles remained suspended in the downstream flume. As water flow velocity increased progressively, cemented sand \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Number** & **Dry Density (g/cm\({}^{3}\))** & **Critical Shear Stress (N/m\({}^{2}\))** & **Starting** & **Depth of** \\  & **(g/cm\({}^{3}\))** & **Stress (N/m\({}^{2}\))** & **Velocity (m/s)** & **Erosion (cm)** \\ \hline CF-A & 1.59 & 0.107 & 0.182 & 5.4 \\ CF-B & 1.59 & 0.519 & 0.435 & 3.5 \\ CF-C & 1.59 & 1.766 & 0.886 & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of water tank test for strengthening sandy soil slope with microbial spraying. Figure 10: The relationship between erosion rate and shear stress. particles began to lose their shear strength and were systematically eroded; consequently, ongoing soil particle loss resulted in numerous pits developing on the surface (e.g., II), ultimately leading to deep wide gully formation (e.g., III). Under conditions maintaining consistent scouring intensity and duration after six applications of microbial reinforcement, the sand slope exhibited relative flatness without any pit formations; moreover, water clarity within the model was significantly preserved (e.g., IV). With further increases in water flow speed, a minor impact crater (e.g., V) first manifested near the edge of the sandbox amidst downstream flows. As water velocity continued to rise slightly, affecting this crater's dimensions, overall structural integrity remained intact, with no significant erosive damage observed (e.g., VI). The degree of erosion on the slope is influenced by the weight of soil particles, the strength of the bond between these particles and the slope, as well as the intensity of water flow acting upon it. In unreinforced or minimally reinforced sandy slopes, smaller soil particles are easily dislodged by water flow, leading to significant loss of material and resulting in surface gullies and pits. Conversely, sandy slopes with a high degree of microbial reinforcement benefit from two key mechanisms: firstly, microbial slurry binds small soil particles into larger aggregates; this consolidation significantly enhances erosion resistance and mitigates gully formation. Secondly, calcium carbonate precipitated during microbial reinforcement forms a continuous hard shell on the surface of sandy soils, further bolstering their anti-erosion capabilities.
## 2 Methods: The SEMI Project
### Hardware and Software Architecture  
The technology used in SEMI is similar to that of previous systems, but extends auditive with visual interaction: the subject, in this case, interacts mainly with the visual channel, integrated with the auditory.  
In this system we used the Kinect SDK, which provides the absolute spatial coordinates of the joints. In order to make to extrapolate features invariant to spatial position (angles, rotations etc.) we included a module for performing geometrical transformations on them. These features are then linked to graphics and sound with various modalities (Figure 2).  
The team has developed four distinct applications, each one devoted to intervention on a particular aspect of the disorder:  
1. Repeat the movements
2. Guess the movements
3. Connect the dots
4. Guess the card  
All of this four applications are based on a common software framework, dedicated to the extraction of gestural features. As we can observe in Figure 2, the most important part of this framework is the geometrical processor, which is responsible of the translation of the Kinect data (joints coordinates) into gesture parameters. The basic algorithm, related to gestures tracking techniques, can be described as follows:  
* Sensor-relative to body-relative coordinate system transformation: the incoming spatial coordinates, relative to the Kinect position, are transformed into body relative coordinates, so that the gesture extraction is not dependent from the subject's position in the room.  
We chose the spine-shoulder as the origin of the subject's coordinates system, and the line between the shoulder as the reference angle.  
* Normalization: the relative body joints coordinates are normalized respect to the subjects' height. In this way gestures speed is independent from the biometric measures.
* Smoothing: all the parameters computed in the previous steps are then filtered using a moving average filter, in order to make them more stable. Moreover, all real-time detected outliers measures (due to sensor related limitations and errors) were discarded and replaced with the last valid value. The threshold of this outlier detection has been found empirically during the development, testing it in various conditions and with different test subjects. The threshold is normalized to the subject's height, using the same methodology described above.  
After these steps, the detected parameters are ready to be mapped (by the Mapper module) into application specific commands and functions (e.g., move the hand cursor in \"repeat the movements\" or \"connect the dots\", see Figures 3 and 4). Beside this real-time application, all detected parameters (speed, angles etc.) can be saved locally, for a further processing and detailed movement analysis, which can be useful for clinical evaluation based on human behavior sensing methodologies, or healthcare applications in general, also for different kind of pathologies or conditions.  
Figure 2: SEMI Software Architecture.
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 3 Proposed Methodology
### Evaluation Metrics  
The dataset was divided into training (80%) and testing (20%) subsets. The confusion matrix's four metrics--True Positives (TP), False Positives (FP), False Negatives (FN), and True Negatives (TN)--were then used to assess the model. In this example, FP stands for false alarms set off, and FN stands for cases that went unnoticed. Three essential performance indicators, Accuracy, Precision, recall, and F1-score, were chosen for evaluation. Recall shows the percentage of correctly identified instances, Precision concentrates on the rate of false alarms, and Accuracy shows the percentage of correct forecasts. By using their harmonic mean to combine Precision and Recall, the F1-score provides a fair assessment [47; 48; 49].  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN} \tag{6}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{7}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{8}\]  
\[\text{F1-score}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{9}\]
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 5 Experimental Result
### Impact of Re-Authentication Time
#### 5.5.1 Decision and Score-Level Fusion  
Table 5 shows the averaged EERs of the decision and score-level fusion for different re-authentication time. As shown in Table 5, longer re-authentication time yields significantly better performance. For the re-authentication time of 29 s, the score-level fusion of max-score shows the EER 3.55%. On the other hand, the score-level fusion of the mean score achieves the EER of 5.11%, followed by the decision-level fusion with 5.86%.  
Among these three mechanisms, the score-level fusion of max-score works the best for each re-authentication time. This result indicates that impostors can hardly imitate genuine users completely.
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
## 3 Results
### Experimental Results for the Toy Train
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Trial**} & **Ground Truth** & **IMU Raw Error vs.** & \(\mathbf{10\times 10\times 1}\) **CNN Error** & \(\mathbf{6\times 6\times 1}\) **CNN Error** & \(\mathbf{3\times 3\times 1}\) **CNN Error** \\  & **Distance (m)** & **Ground Truth** & **vs. Ground Truth** & **vs. Ground Truth** & **vs. Ground Truth** \\ \hline
1 & 15.89 & 43.80\% & 4.53\% & 7.11\% & 13.72\% \\
2 & 15.92 & 43.28\% & 5.15\% & 6.16\% & 11.62\% \\
3 & 16.20 & 40.37\% & 1.30\% & 1.60\% & 6.17\% \\
4 & 15.22 & 43.36\% & 2.96\% & 5.19\% & 12.16\% \\
5 & 15.91 & 45.76\% & 5.03\% & 8.49\% & 17.35\% \\
6 & 16.15 & 47.12\% & 4.77\% & 6.13\% & 14.24\% \\
7 & 15.78 & 47.85\% & 3.42\% & 6.59\% & 14.58\% \\
8 & 16.22 & 43.34\% & 3.21\% & 2.10\% & 6.91\% \\
9 & 15.76 & 44.86\% & 0.51\% & 7.80\% & 13.71\% \\
10 & 15.68 & 45.54\% & 1.28\% & 7.21\% & 11.54\% \\ \hline \hline \multicolumn{5}{c}{Average error} & 44.53\% & 3.21\% & 5.84\% & 12.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Distance and percentage error results for EPSON IMU. Figure 7: Two\(-\)Dimensional Train tracks drawn from EPSON IMU raw data, \(10\times 10\times 1\), \(6\times 6\times 1\), and \(3\times 3\times 1\) CNN.
## 4 Results and Discussion
### Navigation Using Aruco Markers
#### 4.2.2 Second Test Case  
The authors tried to solve the problem occurring during the first test case. For the first problem, they improved the feedback based on the comments of the PVI. As shown in Figure 14, markers should be installed in a different style to solve the second problem. Instead of adding one marker at each interest point, eight markers are installed with the same ID. This implementation makes detection easier and solved the third and fourth problems. It also helps PVI of different heights to detect markers easily. The authors count the steps in the same way as the PVI walk to solve the fifth problem. The authors tried to manage all situations and conditions raised during the testing phase of the prototype. Users  
Figure 13: Screenshots of our prototype: (**a**) A screenshot of a blindfolded person while testing our prototype on the first floor; (**b**) a screenshot of a PVI while testing our prototype on the fourth floor.  
have tested the prototype several times by selecting a starting point and destination. PVI found it easier to detect markers faster than before. Using this arrangement of markers can be detected easily while moving their hands rapidly. Additionally, they found the audio feedback to be satisfactory.
# Experimental Study of Compression Behavior on Monolayer FFF Samples
## 4 Conclusions  
It was demonstrated that FFF PLA monolayers for compression testing can be systematically manufactured and subjected to mechanical tests. All specimens manufactured for the experimental were inspected for their macro-geometrical defects and a post-test analysis of the specimens revealed the failure modes of the specimens, with the following results:  
* The compression strength values were very similar for all three types of specimens, with the helicoidal trajectory type performing slightly better than the other types. As the specimen was both a closed geometry as well as fairly uniform due to the missing fixed layer transition point (as it was present compared to circular trajectories), this was expected.
* The compression modulus of elasticity varied significantly for the different types of specimens. While it was highest for Type C specimens (which had added stiffness due to the layer transition point), the helicoidal routes had a severely reduced modulus.  
Figure 16: Type H specimen. (**a**) Sample 6 (130.6 Mpa). (**b**) Sample 4 (139.8 Mpa).  
Figure 17: Type S specimen. (**a**) Sample 1 (130.6 Mpa). (**b**) Sample 3 (135.6 Mpa).  
Type C specimens had a tendency to buckle asymmetrically after reaching the compression strength due to the anisotropy caused by the starting points of the layers. Type H specimens normally buckled symmetrically, which is desirable, as it means that the specimen type has a more homogenous cross-section. Helicoidal trajectories were deemed the most suited for AM monolayer compression tests.  
Conceptualization, C.D.-W., P.F.M.A. and M.B.; project administration, J.M.V.-M.; experimental methodology, C.D.-W., P.F.M.A. and M.B.; investigation, C.D.-W.; data processing, C.D.-W., P.F.M.A. and M.B.; formal analysis, C.D.-W., P.F.M.A. and M.B.; project review, P.F.M.A. and J.M.V.-M.; writing--original draft preparation, C.D.-W. and M.B.; writing--review and editing, P.F.M.A. and J.M.V.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
This work has been developed under the support of the Mechanical Engineering and Industrial Design Department and the Vice-Rector's Office for Scientific Policy of the University of Cadiz.  
The authors declare no conflicts of interest.
## 3 Experimental Investigations
### Damage Scenarios  
To create BVID in the tested sample, two drop impact tests were performed in a sequence. The first impact with 8 J energy was introduced to the bottom part of the specimen. After the self-healing monitoring period, another drop with 5 J energy was introduced on the opposite side of the PZT. The scheme of the specimen with the position of impacts is presented in Figure 10.
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
###### Abstract  
As a possible implementation of a low-power wide-area network (LPWAN), Long Range (LoRa) technology is considered to be the future wireless communication standard for the Internet of Things (IoT) as it offers competitive features, such as a long communication range, low cost, and reduced power consumption, which make it an optimum alternative to the current wireless sensor networks and conventional cellular technologies. However, the limited bandwidth available for physical layer modulation in LoRa makes it unsuitable for high bit rate data transfer from devices like image sensors. In this paper, we propose a new method for mangrove forest monitoring in Malaysia, wherein we transfer image sensor data over the LoRa physical layer (PHY) in a node-to-node network model. In implementing this method, we produce a novel scheme for overcoming the bandwidth limitation of LoRa. With this scheme the images, which requires high data rate to transfer, collected by the sensor are encrypted as hexadecimal data and then split into packets for transfer via the LoRa physical layer (PHY). To assess the quality of images transferred using this scheme, we measured the packet loss rate, peak signal-to-noise ratio (\(PSNR\)), and structural similarity (SSIM) index of each image. These measurements verify the proposed scheme for image transmission, and support the industrial and academic trend which promotes LoRa as the future solution for IoT infrastructure.  
LoRa; wireless sensor network (WSN); low-power wide-area network (LPWAN); image encryption; peak signal to noise ratio (\(PSNR\)); structural similarity index measure (SSIM) Article
## 6 Simulations
### Results and Discussions
#### 6.3.2 Numerical Results of Semi-Supervised Learning-Based Method
(**a**) Performance of AMT with temporal-shifting augmentation. (**b**) Performance of AMT with amplitude-scaling augmentation. Figure 7: Supervised positioning performance under different samples and augmentation methods. (**a**) Performance of supervised learning(SL) method only. (**b**) Performance of SL method with temporal-shifting augmentation. (**c**) Performance of SL method with amplitude-scaling augmentation. Figure 9: A visualization form to show the distribution of UEs. The red dots represent the actual user locations, while the blue dots represent the predicted user locations. Meanwhile, we compare the convergence of the four methods and record the tendency of the positioning error corresponding to 90 percentile CDF. During the training process, we can observe from Figure 10 that although there is a small difference in the positioning error of AMT, \(\Pi\) Model and Temporal Ensembling, the convergence of AMT is significantly faster than the other two methods, while the convergence curve seems to be smoother. This implies that under the condition of limited training time, the AMT model demonstrates obvious advantages over the other two referenced consistency regularization methods. During the training process, we also recorded the tendency of positioning error in Figure 11. The utilization of AMT can be understood through the training curves. The models with EMA weighting (represented by the orange curves in the bottom row) exhibit more accurate predictions compared to the student models (represented by the blue curves) after an initial period. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**DSSL Method**} & \multicolumn{3}{c}{**CDF Percentile of Position Errors (m)**} & \multirow{2}{*}{**ME(m)**} \\ \cline{2-2} \cline{4-6}  & **50\%** & **67\%** & **80\%** & **90\%** & \\ \hline Proposed AMT with amplitude scaling & 0.79 & 1.03 & 1.27 & 1.63 & 0.90 \\ Proposed AMT with temporal shifting & 1.18 & 1.53 & 1.85 & 2.27 & 1.29 \\ Temporal Ensembling with amplitude scaling & 0.82 & 1.06 & 1.33 & 1.65 & 0.93 \\ Temporal Ensembling with temporal shifting & 1.20 & 1.54 & 1.88 & 2.33 & 1.31 \\ \(\Pi\) Model with amplitude scaling & 0.84 & 1.10 & 1.36 & 1.74 & 0.96 \\ \(\Pi\) Model with temporal shifting & 1.20 & 1.54 & 1.89 & 2.33 & 1.32 \\ Pseudo label & 1.45 & 1.87 & 2.29 & 2.93 & 1.65 \\ Supervised only & 1.71 & 2.19 & 2.76 & 3.33 & 1.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of position accuracy under 1000 labeled samples. Figure 11: Positioning errors of teacher and student models under 1000 labeled samples. Figure 10: Tendency of position errors. When the EMA-weighted model is employed by the teacher, it leads to enhanced outcomes in semi-supervised scenarios. A virtuous feedback cycle appears where the teacher model (orange curve) improves the student model (blue curve) through the application of consistency cost. Simultaneously, the student model contributes to the improvement of the teacher model via EMA.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 5 Results and Discussion  
### Rotordynamic Results Comparison Between Two numerical Methods  
The benefit of the orbit decomposition method is the ability to quickly calculate the transient fluid-induced forces and rotordynamic coefficients. In order to validate the present method, its rotordynamic results are compared with those of the traditional transient CFD method. According to a previous study [13], to capture the linear and small motion characteristics, the major semiaxis \(a\) is set to be \(0.1\,C_{r}\), and the minor semiaxis \(b\) is set to be \(0.5\,a\).
# Many-Objective Optimization for Anomaly Detection on Multi-Layer Complex Interaction Networks
## 4 Results
### Datasets--Networks  
For the synthetically generated network, we applied the Erdos and Renyi models since these are standard network generation models that are also simple to interpret. For the real-world networks, we utilized a range of datasets with different sizes and characteristics, also covering different domains. We applied the following datasets, as summarized in Table 1, which are also publicly available:  
1. a standard multiplex network dataset from Aarhus university [64; 65],
2. the Kapferer Tailor Shop multiplex network [66; 67],
3. a multi-layer network modeling world trade [8; 29; 68], and
4. a multi-layer social network induced by social media (FriendFeed, Twitter, Youtube) [1; 3; 69].
## 6 Conclusions  
This study first analyzed the principle of IMU positioning and EKF technology. Then, based on the traditional UWB/IMU tight combination methods, an improved tight combination positioning model was proposed, and three new modules were proposed. The distance information obtained by the second time integral of the IMU accelerometer was used to optimize the UWB ranging information and convert the location information as UWB output and EKF input.  
The combination model reduced the positioning deviation at each moment and the overall positioning standard deviation. Furthermore, based on the proposed dual-loop cumulative error model and modern control theory, it was proven that the error of the proposed model satisfied the controllability. In this study, an IMU was used as an auxiliary sensor to optimize the ranging data of the UWB, but the positioning accuracy still mainly depended on the ranging accuracy of the UWB.  
In the future, new research can introduce visual positioning technologies to monitor environmental occlusions and other conditions in real time, utilize map information created by computer vision to improve data fusion technology, and perform the real-time correction of ranging information to achieve further high-precision positioning in complex indoor environments.  
Conceptualization, W.Z. and R.Z.; methodology, H.Z.; software, Z.Z. and W.Z.; validation, formal analysis, and investigation, R.Z., B.W. and J.L.; resources, data curation, writing--original draft preparation, writing--review and editing, and visualization, W.Z., Y.F. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 72074170). Project of Tongji University Innovative Design and Intelligent Manufacturing Discipline Group (No. D2201).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 2 Approaches for Inference Time Reduction
### Pruning  
Pruning is a method suitable for producing models that perform more accurately, while being faster with less memory cost. The visual information is highly spatially redundant, and thus can be compressed into a more efficient representation. Pruning is distinguished in two categories: weight pruning and filter (channel) pruning. Both weight pruning and filter pruning are shown in Figure 6.  
In weight pruning, individual parameters (connections), and hence the weights, are being removed, generating a sparse model that preserves the high-dimensional features of the original network. The work of [18] suggested weight pruning with a three-step method in which first the network is being trained to learn important connections. In the second step, unessential connections are being pruned. Lastly, the network is trained again to fine-tune the weights of the remaining connections. This way, network pruning can reduce the number of connections by \(9x\) to \(13x\) without reducing its effectiveness [18; 19].  
Filter (or channel) pruning is also a very successful type of method which leads to successful results. By removing filters that have negligible effect on the output accuracy alongside with their corresponding feature maps, the computation costs are significantly reduced [20]. [PERSON] et al. [21] proposed an efficient method based on channel pruning which improves inference time, while preserving the accuracy of the network. Overall, this approach puts emphasis on the efficiency with the cost of losing information used in the network at the spatial and functional level resulting in reduced effectiveness.