esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Test on the Bond Damage of Steel and GFRP Bars Reinforcing Soft Rock Slopes
## 3 Results
### Displacement of Anchorage Section under Loading
According to Table 7, the relative difference between the steel bar and GFRP bar under soft rock conditions is about five times that under expansive soil conditions, indicating that the stress state under these two-layer conditions was different, resulting in little displacement difference between the steel bar and the GFRP bar. This is because compared with the soft rock, the void ratio of expansive soil is much larger, and the elastic modulus and strength of expansive soil are much lower, resulting in the weaker bond at the interface of the grouting body and the surrounding soil than that of the bar interface. Figure 13 shows the relationship between the axial force and the anchorage depth of the two bars. It is obvious that the effective anchorage depth of the two bars is different, with the steel bar at approximately 4.35 m away from the zero point and the GFRP bar at approximately 2.75 m away from the zero point. The distribution range of the axial force of the GFRP bars was smaller than the steel bars. The elastic modulus of the GFRP bar is approximately 1/4 of the steel bar. Therefore, under the same axial force level, the deformation of the steel bar was smaller than the GFRP bar, and the sheer force of the grouting body around the steel bar was smaller. To balance the axial force of the steel bar, a longer range of the grouting body was required to provide shear. That is, under the same load condition, the damage of the grouting body in the steel anchorage system was greater than GFRP anchorage system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Geological Conditions** & **Specimen Type** & \
\begin{tabular}{c} **Gradients of Displacement** \\ **Curve of Anchorage Section** \\ \end{tabular} &
\begin{tabular}{c} **Relative Difference/\%** \\ \(\{\frac{\left(G_{G}-G_{G}\right)}{G_{G}}\}\) \\ \end{tabular} \\ \hline \multirow{2}{*}{Soft rock} & Steel & 33.37 \(\left(G_{S}\right)\) & \multirow{2}{*}{77} \\  & GFRP & 143.86\(\left(G_{G}\right)\) & \\ \hline \multirow{2}{*}{Expansive soil} & Steel & 8.14 \(\left(G_{S}\right)\) & \multirow{2}{*}{16} \\  & GFRP & 9.67\(\left(G_{G}\right)\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistics of relative difference of various indexes. In summary, the force in reinforcing soft rock slope with steel bar is mostly borne by the bar body itself and the bar body interface, while the force in strengthening soft rock slope with GFR bar is mostly borne by the bar and the grouting body. Hence, the anchorage structure of steel bar in soft rock can generate larger interfacial relative displacement with the loading increasing than the GFRP bar in the anchorage section, even though the elastic modulus of steel is much larger than GFRP. In the expansive soil, the anchorage structure deformations of steel and GFRP bar are almost the same because of the weaker bond at the interface of the grouting body and the surrounding soil than that of the bar interface.
## 4 Flange Image Recognition Principle and Process  
In the project, the vision sensor was used to take the image of the rear of the LNG tanker, the flange area was extracted from the rear image of the tanker, and the spatial position coordinates of the flange are thus established. Then, it passed the flange position parameter to the loading and offloading arm alignment mechanism and guided the loading and offloading arm to automatically align with the flange port. The specific process was as follows:  
The first step was to binarize the image.  
The binarized global threshold was obtained by the Otsu method [16]. The Otsu method, also known as the maximum inter-class variance method, divides the image into two parts, the background and the foreground, according to the grayscale characteristics of the image, and determines the image binarization segmentation threshold by the inter-class variance between the background and the foreground. The Otsu method is suitable for most occasions where the global threshold of the image is required. The most important thing is that the calculation is simple and fast and is not affected by the brightness and contrast of the image. At the same time, the experimental object is a single flange port, which is not affected by its shortcomings.  
The second step is flange edge detection.  
In an image, the edges are where the local intensity changes are most pronounced. The purpose of edge detection is to obtain the contour of the measured object from the background so as to further analyze the contour information of the measured object. In this paper, the Canny algorithm [17; 18; 19] was used to detect the edge. Compared with the ordinary gradient algorithm, the false edge caused by noise is greatly suppressed and the edge is thinned, which makes subsequent processing easier. The third step is to fit the space circle to determine the space pose of the flange port.  
Find the circular target and perform spatial circle fitting through the three-dimensional data obtained by the interaction between the sensors to determine the spatial position and pose of the target flange.
## 2 Materials and Methods
### Multilayer Structure Simulation  
To collect adequate data for constructing the classification model, a multilayer structure simulation method is investigated in this work which is equivalent to the free space measurement. The structure includes three layers of air and MUT with the incidence angle \(\theta\) as \(0^{{}^{\circ}}\), as illustrated in Figure 2.  
According to the multilayer structure above, the ABCD matrix of the equivalent circuit for the two-port network is obtained for the whole structure by the following equations [21]:  
\[\left[\begin{array}{cc}A&B\\ C&D\end{array}\right]=\left[\begin{array}{cc}1&0\\ 0&1\end{array}\right]\left[\begin{array}{cc}\cos(k_{zn}d)&\frac{\left[\sin(k_{ zn}d)\right.}{\gamma_{d}^{TE,TM}}\\ j\gamma_{d}^{\{TE,TM\}}\sin(k_{zn}d)&\cos(k_{zn}d)\end{array}\right]\left[ \begin{array}{cc}1&0\\ 0&1\end{array}\right] \tag{7}\]  
where:  
* \(k_{zn}=\sqrt{\varepsilon_{r}k_{0}^{2}-k_{0}^{2}\sin^{2}\theta}\) is the wavenumber of MUT layer with \(k_{0}=\beta=2\pi/\lambda\) as the wavenumber of the first air layer, \(\theta\) as the incidence angle of the incoming signal, and \(\varepsilon_{r}\) as the relative permittivity of MUT;
* \(Y_{d}^{TE}=k_{zn}/\omega\mu_{r}\mu_{0}\) and \(Y_{d}^{TM}=\omega\varepsilon_{r}\varepsilon_{0}/k_{zn}\) are the admittances in TE and TM polarizations, respectively. Since the incidence angle \(\theta\) is 0\({}^{\circ}\), either \(Y_{d}^{TE}\) or \(Y_{d}^{TM}\) can be chosen for the calculation;
* \(d\) is the thickness of MUT.  
From the ABCD matrix, the scattering parameters can be deduced by the equivalent circuits for the two-port network [21] as follows:  
\[S_{11}=\frac{A+BY_{0}-C/Y_{0}-D}{A+BY_{0}+C/Y_{0}+D} \tag{8}\]  
\[S_{21}=\frac{2}{A+BY_{0}+C/Y_{0}+D} \tag{9}\]  
where \(Y_{0}=k_{zn}/\omega\mu_{0}=\omega\varepsilon_{0}/k_{zn}\) (as the incidence angle \(\theta\) is 0\({}^{\circ}\)).  
Eventually, by adopting the appropriate relative permittivity, relative permeability, dielectric dissipation, magnetic dissipation, and the thickness of an MUT, one can extract the simulated scattering parameters data of the free-space measurement based on the above procedure.
## 2 Related Work
### Clustering Approaches for Energy-Efficient NB-IoT
However, as the number of nodes increases, the probability of inter-nodes interference increases, network latency deteriorates and so does the network reliability. This is why to minimize the impact of having clusters with congested communication traffic as the NB-IoT network scales, some other approaches such as the Cluster Head Election using Fuzzy logic (CHEF) [41] are proposed. * Cluster Head Election using Fuzzy logic (CHEF): CHEF [41] proposes the use of a fuzzy logic approach as a mechanism to minimize the energy consumption of CH selection. It also reduces the inter-cluster communication energetic burden on the selected CH by rotating the CH role among nodes for more energy-balanced network performance. The energy level in each node, the concentration (number of nodes present in the vicinity), and the centrality (a value which classifies the nodes based on how central the node is to the cluster) are used as variables in the fuzzy logic rules formulation. However, the model suffers from the following drawbacks,  
1. The fact that the BS has to collect information from all the NB-IoT nodes, results in complex and multiple overheads. This results in additional energy consumption which although it is negligible for a few nodes, becomes significant as the number of nodes increases. 2. The approach does not cater to the energy consumption caused by the distance measurement between NB-IoT nodes. This is because the CH selection parameter does not feature any signal strength consideration.
## 5 Experiments  
### Datasets  
The proposed method is evaluated on the DOTA1.0, DOTA1.5, and HRSC2016 datasets. DOTA is a large-scale dataset for aerial target detection, with data collected from various sensors and platforms. DOTA1.0 includes 2806 large aerial images with sizes ranging from 800 \(\times\) 800 to 4000 \(\times\) 4000, containing 188,282 instances across 15 common categories: bridge (BR), ground track field (GTF), small vehicle (SV), large vehicle (LV), ship (SH), tennis court (TC), basketball court (BC), storage tank (ST), soccer field (SBF), roundabout (RA), harbor (HA), swimming pool (SP), and helicopter (HC). DOTA1.5 builds on DOTA1.0 by adding the container crane (CC) category and instances smaller than 10 pixels. DOTA1.5 contains 402,089 instances. Compared to DOTA1.0, DOTA1.5 is more challenging during training but also more stable.  
In this paper, the training set and validation set are used for training, while the test set is used for testing. The multi-scale parameters for DOTA1.0 are 0.5 and 1.0, and for DOTA1.5, they are 0.5, 1.0, and 1.5. During training, random flipping and random rotation parameter methods are also employed.  
Figure 11: Elliptic center sampling.  
Figure 12: The bounding box regression effect of two loss functions, demonstrating that the KLD outperforms the smooth L1. (**a**) Smooth L1 loss function; (**b**) KLD loss function.  
HRSC2016 is a challenging ship detection dataset with OBB annotations, containing 1061 aerial images with sizes ranging from \(300\times 300\) to \(1500\times 900\). It includes 436, 181, and 444 images in the training, validation, and test sets, respectively. We use the training and validation sets for training and the test set for testing. All images are resized to \(640\times 640\) without altering the aspect ratio. During training, random flipping and random rotation are employed.
# Enhancing Meteor Observations with Photodiode Detectors
## 5 Discussion
As we analyzed and discussed the results of our observations, we also noted some potential limitations and challenges of using our detector for meteor research. First, the detector proved to be extremely sensitive to moisture. The currents generated by the diodes are small enough that any additional currents resulting from moisture around the detection modules could lead to significant drift in the recorded waveforms. We recognized the need to combat this problem by using adequate ventilation and considering the possibility of heating the modules, which could prevent dew condensation and thus stabilize measurement conditions. The second problem identified is the sensitivity of the detectors to the quality of the power supply. In our initial experiments, we used a power supply without additional filtering, which resulted in the appearance of voltage spikes. These spikes were present in all four detectors and could be misinterpreted as signals from meteors, although their duration was significantly shorter. To remedy this problem, in the experiments proper, we used a network LC noise filter, which significantly reduced the number of unwanted spikes. On the other hand, analysis of the effect of temperature changes in the system on the recording of meteor phenomena showed that they are not particularly important for the quality of measurements. The change in the value of the dark current from the photodiodes turned out to be practically invisible compared to natural changes in sky brightness. This natural drift was effectively counteracted by the drift reduction circuit, mainly during the dusk and dawn. It is worth noting that the identified challenges and limitations represent an important area for further work on improving and optimizing our detector. As we collect more data and further develop the technology, we will be able to identify and minimize potential errors, which will contribute to even more precise and reliable meteor observations.
## 1 Introduction
### Problem Statement and Objectives  
Jurong City, located strategically between Nanjing (a major metropolitan city) and Zhenjiang (its administrative jurisdiction city) in the rapidly urbanizing Yangtze River Delta region of China, provides an exemplary case study for examining these dynamics. Jurong represents a typical urban interstices scenario: it is functionally integrated into the metropolitan economy of Nanjing through substantial commuting flows and economic interactions, yet administratively dependent upon Zhenjiang. This administrative fragmentation creates mismatches in spatial planning and infrastructure investment, potentially limiting development prospects and integration efficiency in Jurong.  
This study addresses the critical need for quantitative research into urban interstice dynamics by employing a data-driven approach utilizing mobile signaling data and origin-destination (OD) flow analyses. Specifically, this research aims to carry out the following: (1) quantitatively assess the spatial and functional integration of Jurong City with neighboring cities (Nanjing and Zhenjiang); (2) analyze the influence of administrative boundaries in shaping the spatial structure and intercity connectivity of urban interstices; (3) explore how Jurong balances regional integration pressures with the imperative to maintain local economic and spatial autonomy; and (4) advance international understanding of how interstitial urban areas mediate between fragmented governance and functional integration, offering transferable insights into spatial coordination and adaptive metropolitan governance across diverse global contexts.  
Through a detailed empirical analysis based on mobile signaling data, this research provides a replicable methodological framework for quantifying the spatial and functional dynamics of urban interstices. It bridges existing theoretical discussions with empirical findings, offering significant insights into the mechanisms by which administrative boundaries and market forces jointly shape urban interstitial development. The principal conclusions drawn from this study underscore the necessity of coordinated governance mechanisms, targeted infrastructure development, and policy alignment to effectively integrate urban interstices into metropolitan networks without compromising their distinct spatial identities.
## 6 Limitations and Future Work  
* Simplified Landslide Representation: This study used the centroids of historical landslide deposits as representative points. While practical, this may not fully capture landslide morphology. Future work could explore alternative point selections, such as the headscarp, or adopt polygon-based landslide datasets to better reflect their spatial extent and improve model fidelity.
* Sampling Strategy Constraints: The GLHM-based method for selecting non-landslide samples helped reduce bias, but its reliance on coarse, global-scale hazard data limits regional applicability. Future research could explore alternative non-landslidesampling strategies to improve the spatial representativeness and robustness of the dataset.
* Static Input Variables: The current models use static environmental factors, ignoring temporal triggers like rainfall or land use change. Future studies should integrate dynamic variables and time-series data to improve the predictive capability and adaptability of susceptibility models.  
Conceptualization, S.Q. and Z.T.; methodology, S.Q.; software, Z.T.; validation, Z.T., S.Q. and H.X.; formal analysis, Z.T. and S.Q.; investigation, M.B. and H.X.; resources, Z.T.; data curation, Z.T. and M.B.; writing--original draft preparation, Z.T.; writing--review and editing, H.X.; visualization, M.B.; supervision, S.Q. and D.L.; project administration, S.Q. and D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Natural Science Foundation of China Projects 42372325 and the Major Projects of Financial Science and Technology Plan of Xinjiang Production and Construction Corps (No: 2020 AA002).  
Not applicable.  
Some or all of the data, models, or code that support the findings of this study are available from the corresponding author upon request.  
The authors deeply appreciate the Editor and the anonymous reviewer for their useful comments, especially for the comments marked on the hard copy of the MS. The authors are most grateful for this support.  
The authors declare no conflicts of interest.
# Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation
## 1 Introduction
### The Argument for Eye-Tracking  
One could argue that the measurement variables acquired by eye-tracking have broad applicability, especially in cognitive science and its applications. The argument defines two types of variables: extrinsic and intrinsic variables [1]. Extrinsic variables (e.g., size) can be measured straightforwardly without the need for expert knowledge in constructing or operating the measuring instrument, whereas intrinsic variables are more challenging to access and measure, as their measurement tools make use of other variables or physics-based conversions to acquire the intrinsic value (e.g., measurement of blood pressure). In addition, not all phenomena can be measured by a device situated in the physical realm; for example, stress levels are not only expressed physically by blood pressure, heartbeat, pupil dilation, etc., but also by high-level cognitive and neurological factors such as stimulus habituation, nervous system excitability, etc. These are called constructs [2] and generallyinvolve cognitive processing, and unlike variables, are not necessarily expressed by a singular quantitative value.  
Therein lies the utility of eye-tracking: not only can it measure eye movements indirectly (non-invasively), i.e., intrinsically, it also lies at the threshold between variables and constructs. A raw data set of gaze coordinate entries (i.e., where a person looks at through a series of time segments) can be extrapolated into visual focus on stimuli (fixations), sequences of such focus (fixation scanpath and fixation revisits), and, subsequently, to provide an understanding of cognitive processing of a given visual scenery [3]. In general, the length of eye-tracking fixations, the objects that are fixated, the scanpath, and the frequency of fixation revisits all provide an insight into a person's cognitive processing (e.g., task difficulty and task-solving strategy). In specific eye-tracking tasks (e.g., scene perception, visual search, reading, and task-driven action), eye patterns exhibited by users can be analyzed in further detail depending on the task methodology. In some instances, other findings can be extracted from eye movements and eye physiology--e.g., different cognitive styles or abnormal personalities [4; 5; 6]. Given that the eyes are an outward extension of the brain [7], and that many nonverbal [8] and physiological signals [9] are communicated through them and different processing patterns are used for different visual stimuli [10; 11], they are useful data to obtain. These data can also be built on in future and in emerging technologies such as artificial intelligence [12], computer graphics, and virtual reality [13].
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 3 Methodology
### Overview of Testing Framework
#### 3.1.1 Functionality Verification  
In the functionality test, the verification focuses on testing the basic operations of a cloud system. The main goal is to verify the core service operations in a cloud system via a set of test tools. For example, the system under test in this paper is an OpenStack cloud. The Functest [44] provides a set of test cases to verify core services for operating the OpenStack cloud. If the services of OpenStack are active properly, the testing process further evaluates the operating performance of each service, e.g., the turnaround time of each test case. This performance evaluation checks if OpenStack services finish the specific operations in time. If the operations break the time limits, the relevant services work incorrectly. This paper applies Rally Sanity to check if the services fulfill the predefined SLA.  
For the test cases in Functest, this paper adopts Healthcheck and Rally Sanity to test the primary functionalities of an OpenStack cloud system. In Healthcare test suite, Connection_check is used to check the connectivity of OpenStack Endpoints. Vping_ssh tests whether the VM can be accessed via SSH from the external network. Vping_userdata is used to verify if the VMs can communicate with each other through the internal network. Rally_Sanity evaluates the operating performance of OpenStack services. The primary services include Nova, Neutron, Cinder, Keystone, and so on. Nova is used to provide computing instances. Neutron is responsible for the networking as a service. Cinder provides the block storage service. Keystone is used to provide the authentication service. These test cases of Rally_Sanity measure the time taken by each scenario to make sure that all tests fulfill the SLA.
# Optimal Gliding Trajectories for Descent on Mars
## 2 Formulation of the Optimal Control Problem
### Cost Functions  
The descent problem focuses on reducing the velocity and altitude for a specific terminal point at the final time (\(t_{f}\)). An aleatory point of 26 deg in longitude and 35 deg in latitude is selected as the terminal point. For those reasons, the specified cost function is composed of four terminal costs in order to minimize its value at the final time. So, the OCP is formulated to minimize the following:  
\[\min_{\mathbf{U}(t),t_{f}}\mathbf{J}\Big{(}t_{f}\Big{)};\mathbf{J}\Big{(}t_{ f}\Big{)}=\mathbf{J}_{1}\Big{(}t_{f}\Big{)}+\mathbf{J}_{2}\Big{(}t_{f} \Big{)}+\mathbf{J}_{3}\Big{(}t_{f}\Big{)}+\mathbf{J}_{4}\Big{(}t_{f}\Big{)}, \tag{10}\]  
for the objective functionals  
\[\left.\begin{array}{c}\mathbf{J}_{1}\Big{(}t_{f}\Big{)}=V\Big{(}t_{f}\\ \mathbf{J}_{2}\Big{(}t_{f}\Big{)}=R\Big{(}t_{f}\\ \mathbf{J}_{3}\Big{(}t_{f}\Big{)}=\big{\|}\gamma\big{(}t_{f}\big{)}\big{\|}\\ \mathbf{J}_{4}\Big{(}t_{f}\Big{)}=\Big{(}\theta\Big{(}t_{f}\Big{)}-26\Big{)}^ {2}+\Big{(}\varphi\Big{(}t_{f}\Big{)}-35\Big{)}^{2}\end{array}\right\} \tag{11}\]  
Figure 1: Aerodynamic coefficients of the spaceplane as a function of the angle of attack.  
subject to the dynamics of the system, Equations (1)-(8), and the path constraints (9). The initial conditions are presented in Table 1.