esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
## 3 Results and Analysis
### Integrated Prediction Model
These observations are consistent with the quantitative findings reported in terms of the root mean square error (RMSE). Figure 8: Simple-LSTM and Integrated-LSTM predicted position for vehicles. When comparing the performance of the Integrated-LSTM model with that of the Simple-LSTM model, a trade-off between the accuracies of the lateral and longitudinal positioning became evident. Specifically, the Integrated-LSTM model demonstrated reduced error in estimating \(x\) but an elevated error in predicting \(y\). This observation suggests a complex interplay between the two output variables that merit further investigation. Additionally, it is important to note that the model's architecture involves a sequential two-step prediction process. Consequently, the accuracy of the second prediction step is inherently influenced by the precision of the initial step. This cascading effect of the error resembles a Bayesian decision-making scenario, where inaccuracies in early stages may be amplified in subsequent steps, compared with what might be observed in standalone models.
# Vehicular Alarm System Using mHealth Data and Lightweight Security Algorithms
## 1 Introduction  
Road traffic injuries have increased considerably and contribute to one of the main causes of death across all age groups worldwide, as reported by the World Health Organization (WHO) [1]. Long distance professional truck drivers have been identified as a high-risk group vulnerable to an increased risk of road accidents. A report claims that truck drivers have a life expectancy 10 to 15 years lower than the average male in the United States [2]. Due to a stressful workload with long periods of sedentary driving, health issues related to these drivers could result in road accidents.  
With the advancement of Internet of Things (IoT) and mobile health (mHealth) technologies, intelligent wearable devices could help monitor the health parameters of long-distance drivers in real time. However, there is a lack of trust and health education in adopting mHealth solutions that can be integrated with vehicular information systems (VIS). Since mHealth network resources haveconstraints of battery power, it is not practical to implement a full capacity security system. For example, an attacker may intervene the data transfer from the smartphone to the cloud as a man-in-the middle attack, and a time-based verification model to detect the MITM attacks [3] can be utilized to handle this situation. This paper is aimed to address these issues by applying an integrated approach to both the driver health monitoring system as well as the vehicle safety system using mHealth devices such as wearable devices utilizing a lightweight security framework. Figure 1 depicts the idea of integrating an mHealth network with a VIS that collects road and vehicular information to connect with smart devices, which are a medium to mHealth. It is intended to use the mHealth network to transfer data from sensors to a cloud server via a smartphone, which integrates the VIS into the mHealth network. Hence, the information flow and security measures are mainly focused on the mHealth application rather than the VIS, which transfers data to the smartphone.  
The paper is structured as follows. Section 2 provides the background of the study and the gap in the literature. Section 3 proposes an integrated vehicular sensor design with the use of multiple sensor devices, including vehicular sensors, mHealth wearable sensors, and smart devices of the driver. Section 4 describes the application of such a wireless sensor design to integrate data from various sensors to activate the alarm in response to emergency situations that may arise. Section 5 proposes a lightweight security algorithm and Section 6 describes the simulation results. Lastly, the conclusions and direction of future research are described in Sections 7 and 8 respectively.
## 4 Discussion  
**Misleading labels**: The object detection task's general issues are incorrect classes, imprecise coordinates of boxes, and missing boxes. BBRefinement, as well as standard object detectors, is vulnerable to the first two issues, but it is resistant to the third issue. If we consider missing labels as illustrated in Figure 7, we will penalize a standard detector during training if the detector produces predictions for such missing labels. This will lead to decreased performance. In the case of BBRefinement, if some label is missing, a cropped image is not produced. Therefore, the missing labels only decrease the training set's size and do not affect BBRefinement's performance.  
**Refinement of a dataset**: We realized an experiment in which we took GT test labels, refined them, and visualized both in an image. Surprisingly, we can claim that BBRefinement can produce more precise labels than the original GT COCO dataset. Figure 8 shows crops from the test set with inpainted boxes: green color marks the GT boxes given by the COCO dataset, and yellow color marks the labels produced by BBRefinement. We selected the images in Figure 8 as such cases, where it was evident that BBRefinement produced more precise boxes. Note that the IoU between the predictions and GT varied here around 0.8. Because the dataset was large, and eight illustrated crops were chosen selectively, we also selected six additional crops as follows. The first one had an index of 1000 in the ordered list of images, the second one 1200, the third one 1300, etc., so that the selection was not affected by our preference. They are illustrated in Figure 9. We can proudly claim that BBRefinement, although not so significant as for the previous cases, still produces in most cases more precise boxes than GT (best seen zoomed-in). In addition, we applied BBRefinement trained on COCO to the Cityscapes dataset. Again, BBRefinement made visually more precise labels than the original Cityscapes labels. This finding leads to three conclusions. First, it is ambiguous to compare high-mAP object detectors because high mAP does not necessarily mark a better detector in the meaning of real-world truth, as the labels are affected by human subjectivity and error. Next, thanks to the high number of boxes, BBRefinement can be trained in such a generalized manner that the labeling error can vanish, so that it can be used for re-labeling a dataset. Finally, there is a hypothesis that IoU between BBRefinement trained on a specific dataset and its GT labels can be used to express the quality of labels. Verification of this hypothesis is a theme for future work.  
Figure 7: The figure illustrates two images taken from the COCO dataset, where the boxes are inpainted ground truth labels. It is evident that some labels are imprecise and many of the labels are missing. This behavior can be seen mainly in images that include groups, and it is a known issue of the COCO dataset.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 4 Simulations and Results  
In this section, a set of experiments are presented to show the reduced energy use on multicore SDN controllers compared to their single-core counterparts. To validate our proposed method, many experiments were conducted. The main idea was to measure the controller's processor energy consumption during heavy load.  
All experiments were conducted using two physical hosts linked by an Ethernet 1000 baseT-FD cat5e cable. The server is a quad-core Dell PowerEdge R230 with an Intel Xeon E31220 processor, while the client is a Dell Inspiron notebook with an Intel T6670 Core2 Duo processor, both running Debian OS 9.0. Figure 7 describes the hardware installation diagram.  
Figure 7: Block diagram of experiment.  
To validate our proposal, we evaluated two scenarios: (1) a scenario with a constant throughput; and (2) a latency-aware scenario. The first scenario described in Section 4.1 uses one vswitch, two hosts and the Iperf [33] utility to generate constant traffic between the hosts passing through the controller. The second scenario described in Section 4.2 uses thirty hosts attached to two vswitches and ICMP-Echo requests to test latency keeping an average of 5 and 6 ms latency. Those scenarios were designed to put the controller under controlled load, testing for the two most critical metrics for a network: throughput and latency.  
Network real traffic patterns change during a day and over the week, mostly related to user applications demands. Part of the traffic on a DCN flows through intra-data-center connections. As stated by [PERSON] and [PERSON] [34], there is an explosion of intra-data-center data traffic in web communication and a drift toward software-defined data center. Thus, the real traffic patterns can be: (i) of many machines on the same network with request-response, such as the HTTP protocol, which we simulate using the latency-aware scenario described in Section 4.2; or (ii) big files transfers to or from storages and databases that we simulate using the throughput-aware scenario described in Section 4.1.
## 1 Introduction
It avoids the wavelength time-slot fragmentation caused by the constraints of wavelength consistency and time-slot continuity. (4) The simulation results show that the proposed strategy can achieve better performance compared with the baselines in terms of key success rate, key-updating delay, and blocking probability. The rest of this paper is organized as follows. Section 2 describes the problem of resource allocation in QKD optical networks. A time-window-based security orchestration (TW-SO) strategy is designed in Section 3. The concept of time continuous compactness (TCC) is introduced. Based on the RWTA algorithm in Ref. [21], we further consider the limitation of the wavelength resources for quantum-key channels (QKChs). Our paper optimizes the allocation of QKChs and proposes a new ReLoss-TCC based RWTA strategy for avoiding the wavelength-time-slot fragmentation in Section 4. In Section 5, we design a multi-dimensional RWTA algorithm based on TW-SO and TCC-based RWTA for data secure transmission. Section 6 shows the simulation results over different scenarios. Finally, Section 7 concludes this paper.
## 3 Data and Methodology  
### Data  
A total of 38 Sentinel-1A satellite images (ascending data) acquired from 14 October 2014 to 13 August 2019 were obtained and processed using SBAS- InSAR technology to calculate the deformation rate. The wavelength is 5.6 cm in the C-band. Sentinel-1A is a high-resolution SAR satellite launched by the European Space Agency in April 2014. It has an orbital altitude of 693 km and an inclination of 98.18\({}^{\circ}\). The satellite revisit time is 12 days for a single satellite and 6 days for the two-satellite constellation [48]. The sensor has four imaging modes and polarization methods. In this study, we selected the interferometric wide swath (IW) slant-distance single look complex (SLC) mode, with a width of 250 km and a spatial resolution of 5 \(\times\) 20 m (Figure 2); the relative orbit number is T128 (in Table 1).  
Precision orbit files were generated 21 days after GNSS data were obtained, and accuracy exceeded 5 cm. Two types of Shuttle Radar Topography Mission Digital Elevation  
\begin{table}
\begin{tabular}{c c} \hline
**Temporal coverage** & 2014.10.14–2019.08.13 \\
**Imaging mode** & IW \\
**Orbital direction** & Ascending \\
**Band** & C-band \\
**Azimuth resolution** & 20 m \\
**Range resolution** & 5 m \\
**Wavelength** & 5.6 cm \\
**Resolution** & 5 \(\times\) 20 m \\
**Polarization** & VV \\
**Average angle of incidence** & 39.3\({}^{\circ}\) \\
**Relative orbit number** & T128 \\ \hline \end{tabular}
\end{table}
Table 1: Basic information on the Sentinel-1A images.  
Figure 2: The coverage of a single Sentinel-1A SAR image.  
Model (STRM DEM) products are available: the 1 arc-second product with a resolution of 30 m and the 3 arc-second product with a resolution of 90 m [49]. The former was used in conjunction with the SBAS-InSAR for terrain phase removal and visibility analysis [18].
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 4 Study 2: Visual-Haptic Guidance Design and Evaluation
### Study Design
#### 4.1.1 Haptic Feedback Design for Target Searching Task  
Haptic feedback has demonstrated its usefulness across a range of user scenarios, including localizing out-of-view objects [21; 49; 63], guiding search tasks [60], enhancing entertainment experiences [34; 35], and aiding in car driving [17]. In dual-task environments, digital haptic feedback has been shown to improve the user performance in complex driving dual-tasks settings [22] and to promote faster responses in visual target searching [57].  
Recognizing that haptic feedback may assist in maintaining or even enhancing visual attention in dual-task settings [22], we conducted an evaluation that incorporated wearable haptic feedback, building on our evaluation of visual guidance in Study 1. In accordance with the results of Study 1, we employed the Dynamic Arrow design as the visual guidance technique, comparing it with the haptic feedback design and a combination of both visual and haptic feedback guidance methods.  
Previous studies suggest that multiple tactile units can be perceived as a single area by the user [32; 33]. As such, we were able to map the target direction behind the user on the user's back using a haptic vest device equipped with numerous tactile units, as depicted in Figure 4(b). When a target appeared behind the users in the Secondary Task, haptic feedback would simultaneously activate in the direction of the target, prompting the user to search for it behind them. In a visually demanding dual-task setting, introducing this additional interaction modality could be beneficial for reducing cognitive load [22; 57].  
Figure 5: Experimental settings, system architecture, and haptic guidance design.
# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.6 Qualitative Comparison of Extreme Images  
Since most of the evaluation indexes above are of statistical significance, it is hard to evaluate the robustness of the algorithm proposed for extremely difficult cases. Therefore, we selected some representative hard samples from datasets and performed a qualitative comparison of the algorithms to obtain an objective and comprehensive evaluation.  
In order to compare the performance of algorithms on hard samples, we randomly selected five hard samples with specular reflection, eyelid occlusion, eyelash occlusion, slanted eyes, and out-of-focus blur from the five different datasets JLU-7.0, CASIA-Iris-Lamp-v4, UBIRIS.V2, MICHE-I, and Mmu2. The three algorithms with the best results in Section 3.4.3 were used for comparison with the PFSegIris, which were MFFIris-Unet [18], PI-Unet [15], and FRED-Net [14]. Figure 14 shows the comparison of test results of PFSegIris and the other three algorithms on the five hard samples.  
As can be seen from Figure 14, the proposed algorithm shows better segmentation results than the other three segmentation algorithms on five hard samples. Since the near-infrared images are less affected by ambient light, iris contours in these images are more obvious compared with those in the visible-light images. The segmentation performance of the proposed algorithm on the visible and near-infrared spectral iris images was more balanced than in other algorithms, and the segmentation results were closer to the ground truth with finer and smoother inner and outer iris edges. Therefore, it illustrates that PFSegIris performs more robustly in the face of challenging and hard samples and can identity the location of iris regions more accurately.  
Figure 13: Positioning results comparison of PFSegIris and OSIRIS V4.1 on four iris test sets: (**a**) original images; (**b**) mask positioning images; (**c**) PFSegIris positioning images; (**d**) OSIRIS V4.1 positioning images.
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
###### Abstract  
Understanding the environment is an essential ability for robots to be autonomous. In this sense, Convolutional Neural Networks (CNNs) can provide holistic descriptors of a scene. These descriptors have proved to be robust in dynamic environments. The aim of this paper is to perform hierarchical localization of a mobile robot in an indoor environment by means of a CNN. Omnidirectional images are used as the input of the CNN. Experiments include a classification study in which the CNN is trained so that the robot is able to find out the room where it is located. Additionally, a transfer learning technique transforms the original CNN into a regression CNN which is able to estimate the coordinates of the position of the robot in a specific room. Regarding classification, the room retrieval task is performed with considerable success. As for the regression stage, when it is performed along with an approach based on splitting rooms, it also provides relatively accurate results.  
CNNs; classification; localization; mobile robots; omnidirectional images; transfer learning; regression +
Footnote †: journal: Applied sciences
# Experimental Investigation into Failure Modes of Low-Yield-Point Steel Plate Shear Walls
## 2 Horizontal Cyclic Loading Test
### Experimental Results
#### 2.2.2 Change in Bearing Capacity after Yielding  
For the purpose of reflecting the rapidity of the bearing capacity variation after the overall yielding for specimen 1 under horizontal low cyclic loading, two dimensionless quantities, \(F/F_{y}\) and \(\Delta/\Delta_{y}\), can be established. The \(F\) was the horizontal load at the peak point of the first cycle at each load level after yielding, whereas the \(\Delta\) was the top displacement corresponding to \(F\). \(F_{y}\) and \(\Delta_{y}\) were the yielding loads and yielding displacement of the structure as a whole, respectively.  
The relationship curves between \(F/F_{y}\) and \(\Delta/\Delta_{y}\) after yielding of the specimen are shown in Figure 18. The bearing capacity after yielding was on the ascending trend, whereas, when the structure was loaded in the forward direction with the top displacement \(\Delta\) of \(1.74\Delta_{y}\), the bearing capacity began to descend due to the excessive bulging of the infill panel and the compressive buckling of the upper flange of top beam at the loading end. The bearing capacity curve after yielding kept rising under reverse loading of the structure, which was attributed to the extraordinary strain-hardening characteristics of LYP steel that would merely produce relatively large plastic deformation without cracking failure in the loading process. The structural bearing capacity was still \(1.17F_{y}\) when the post-yield top displacement \(\Delta\) reached \(3.28\Delta_{y}\) under reverse loading, which illustrated that the structure remained remarkably high-strength even when the post-yield displacement was loaded to a larger multiple of the yield displacement, further indicating that the LYP SPSW possessed outstanding structural ductility.
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 3 Neighbor-Aware Non-Orthogonal Multiple Access Scheme  
As shown in our system model (see Figure 1), all IoT devices have energy harvesting capabilities. That is, they harvest the energy from external sources (e.g., sun and wind) and store the energy in their battery with the capacity \(E_{\max}\). In addition, IoT devices periodically generate data (e.g., senses temperature and monitors target) and determine whether to transmit these data to BS by consuming the energy stored in their battery. Because IoT devices exploit NOMA, each IoT device can increase its data rate by increasing its transmission power. However, excessively high transmission power can cause energy depletion and decrease the SINR of neighbor IoT devices. To sum up, it is important for each IoT device to decide whether to transmit data to BS and the transmission power to maximize the aggregated data rate of IoT devices while keeping a low energy outage probability. In addition, this decision should be made by considering the energy level and other devices' (i.e., neighbors') transmission powers. For this, a constrained stochastic game is formulated in the next section. Note that two types of decision (i.e., the decision on whether to transmit data to BS and the decision on the transmission power) can be handled by one dimension of action space, i.e., the transmission power, because the action representing zero transmission power can be interpreted as the situation where the IoT device does not transmit data to BS.
## 2 Models and Methods
### Mathematical Model
Transport equation for the turbulence kinetic energy k:  
\[\rho_{p}\left(\frac{\partial k}{\partial t}+u_{i}\frac{\partial k}{\partial x _{i}}\right)=\frac{\partial}{\partial x_{j}}\left[\left(\mu_{p}+\frac{\mu_{t }}{\sigma_{k}}\right)\frac{\partial k}{\partial x_{j}}\right]+\mu_{t}S^{2}- \rho_{p}\beta_{i}^{*}k\omega \tag{6}\]  
Transport equation for the specific dissipation rate \(\omega\):  
\[\rho_{p}\left(\frac{\partial\omega}{\partial t}+u_{i}\frac{\partial\omega}{ \partial x_{i}}\right)=\frac{\partial}{\partial x_{j}}\left[\left(\mu_{p}+ \frac{\mu_{t}}{\sigma_{\omega}}\right)\frac{\partial\omega}{\partial x_{j}} \right]+\rho_{p}\alpha_{\infty}S^{2}-\rho_{p}\beta_{i}\omega^{2}+2\left(1-F_{1} \right)\frac{\rho_{p}\sigma_{\omega,2}}{\omega}\frac{\partial k}{\partial x_{j }}\frac{\partial\omega}{\partial x_{j}} \tag{7}\]  
where:  
\[\mu_{t} =\alpha^{*}\frac{\rho_{p}k}{\omega}\] \[\sigma_{k} =\frac{1}{\frac{F_{1}}{\sigma_{k,1}}+\frac{1-F_{1}}{\sigma_{k,2}} },\quad\sigma_{\omega}=\frac{1}{\frac{F_{1}}{\sigma_{\omega,1}}+\frac{1-F_{1}} {\sigma_{\omega,2}}}\] \[\alpha_{\infty} =F_{1}\alpha_{\infty,1}+\left(1-F_{1}\right)\alpha_{\infty,2}, \quad\alpha_{\infty,1}=\frac{\beta_{i,1}}{\beta_{\infty}^{*}}-\frac{\kappa^{2}} {\sigma_{\omega,1}\sqrt{\beta_{\infty}^{*}}},\quad\alpha_{\infty,2}=\frac{ \beta_{i,2}}{\beta_{\infty}^{*}}-\frac{\kappa^{2}}{\sigma_{\omega,2}\sqrt{ \beta_{\infty}^{*}}}\] \[\rho_{p}\left(\frac{\partial u_{x}}{\partial t}+u_{x}\frac{\partial u_{x}}{ \partial x}+u_{y}\frac{\partial u_{x}}{\partial y}+u_{z}\frac{\partial u_{x}}{ \partial z}\right) =-\frac{\partial p}{\partial x}+\mu_{p}\left(\frac{\partial^{2}u_ {x}}{\partial x^{2}}+\frac{\partial^{2}u_{x}}{\partial y^{2}}+\frac{\partial^{ 2}u_{x}}{\partial z^{2}}\right) \tag{2}\]0  
\(\mu_{t}\)--turbulent viscosity  
\(S\)--modulus of the mean rate-of-strain tensor \(F_{1},F_{2}\)--blending functions  
\(\kappa=0.41\)--Karman constant  
\(\sigma_{k,1}=1.176\), \(\sigma_{\omega,1}=2.0\), \(\sigma_{k,2}=1.0\), \(\sigma_{\omega,1}=1.168\), \(\alpha_{1}=0.31\), \(\alpha^{*}=1\), \(\beta_{\infty}^{*}=0.09\), \(R_{\beta}=8\)  
\(\beta_{i,1}=0.075\), \(\beta_{i,2}=0.0828\). Further CFD calculations were performed using the LES turbulence model. The governing equations were obtained by filtered continuity and Navier-Stokes (8)-(11) equations. The Smagorinsky-Lilly model was used to model the eddy-viscosity \(\mu_{s}\).
## 4 Results and Discussion
### Electrical Performance of the Proposed Panel  
Figure 10 depicts the experimentally measured electric voltages. With a decrease in \(\Delta T\), the voltage fell dramatically. The average voltage for the 15 \(\times\) 10 TEG panel was 13.73 V, with the greatest and lowest voltages of 15.3 V and 12.51 V, respectively.  
The illustration in Figure 11 shows that the power output of the 15 \(\times\) 10 TEG panel ranged from 19.72 W to 29.49 W during the experiments, with an average power output of 23.92 W, where the resistance of the used load is 5 \(\Omega\). The panel experienced varying temperatures during the studies and as a result, it demonstrated enhanced power with higher temperature differentials.  
The greatest temperature difference (\(\Delta T\)) across the panel was 42.35 \({}^{\circ}\)C. However, when \(\Delta T\) decreased, the output power of the panel also decreased.