esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments
## 4 Experiments and Discussion
### Results and Analysis  
By utilizing the 5G wireless channel, with a 100 MHz bandwidth at the 3.5 GHz band and 40 MHz at the 2.1 GHz band, the uplink peak rate can reach 418.5 Mbps, which is slightly lower than the theoretical value of 450 Mbps. Figure 9 illustrates the average uplink rates under different received signal powers and compares two scenarios: utilizing the 3.5 GHz band without super uplink and employing super uplink, which includes the 3.5 GHz and 2.1 GHz bands. The results clearly demonstrate that the adoption of super uplink significantly enhances the upload capability of the mine 5G system, providing a crucial foundation for high-definition video transmission in unmanned driving. Additionally, Table 2 presents the speed test results for 1080P and 4K video transmission, showcasing their capability to fully support unmanned mining applications.  
By adjusting the local processing ratio from 0 to 100%, the average latency was tested under two scenarios: returning within the same base station and returning across base stations, as illustrated in Figure 10. It is evident that when transmitting 1080P video, the average latency is minimized at a 40% local processing ratio, achieving a controlled latency of 11.8 ms. Similarly, for transmitting 4K video, the average latency is minimized at a 30% local processing ratio, resulting in a controlled latency of 18.3 ms. Both of the results mentioned above are less than 20 ms, meeting the demands of video transmission for un  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Video Resolution** & **Single Stream Rate** & **Average Rate** \\ \hline
1080P & 2 Mbps & 13.8–14.5 Mbps \\
4K & 6 Mbps & 39.4–43.2 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Speed test results for 1080P and 4K video transmission.  
Figure 9: Uplink rates under different received signal powers.  
Thanks to the improvement in information transmission, the downlink latency can now be controlled within 20 ms, which is essential for remote control.  
Compared to previous schemes, our solution substantially enhances high-definition video transmission for unmanned driving in mines, effectively fulfilling the demands for low latency and multiple channels. However, the high utilization of transmission resources may pose a challenge by competing with other systems in the mine, a concern that will be promptly addressed as the mine infrastructure evolves. We conducted experiments on remote-controlled unmanned driving, as depicted in Figure 11. To avoid encroaching on other network services, only five out of seven video streams were utilized during the experiment.
# Application Layer Protocol Identification Method Based on ResNet
## 4 Experimental Results and Discussion
### Experimental Results and Analysis  
This section tests and evaluates the proposed method based on the evaluation metrics in Section 4.3. It is divided into four parts: (1) determination of optimal de-limiters, (2) generation of frequency tables for feature data blocks, (3) training performance under different deep learning models, and (4) testing the test set using a unified model and hyperparameters and analyzing the test results.
# Visible Light Communications through Diffusive Illumination of Sculptures in a Real Museum
## 4 Results and Discussion
### PER Analysis and Quality of Communication Channel
In the low-level signal configuration (Figure 4b) the RX trace features a much less defined eye pattern, and the signal amplitude approaches a value close to the comparator threshold T. This induces a larger jitter in the digitised signal (\(\sim\)8 \(\upmu\)s). However, a dedicated high-frequency digital filtering process introduced in our software improves the discrimination of transitions in the digitised signal, and successful VLC communications could be established even for SNR as low as 15 dB.
# An Integration Model for Flux Density Distribution Formed by a Heliostat
## 4 Analysis and Discussion
It calculates the ellipse Gaussian error distribution of a reflected ray generated by the ellipse Gaussian distribution of a surface error, which is suitable for more general cases. However, it consumes many computing resources. The calculated results of the HELIOS system were compared with the experimental data of a single heliostat in New Mexico [22], and the central profiles of the calculated results and the experimental data were presented. The difference between the actual data and the calculated results was approximately 1.5%. From the central profiles, the calculation error of the HELIOS system is not significant. However, the comparison of the flux density distribution involves interpolation in two directions. In the worst case, interpolation will bring about an error of approximately 2%. There is still a significant error between the calculated results of the HELIOS system and the actual data. The numerical method proposed by the HELIOS system and [PERSON] has an essential assumption as a convolution method. It is necessary to assume that all reflected rays are reflected from the center of the heliostat and projected onto the receiver plane to calculate the flux density of the receiver plane. A large calculation error will occur when the distance between the receiver plane and the image plane is large. Only when the distance between the heliostat and the receiver plane is close to the focal length is the calculation result of the convolution method accurate enough. However, the direct integration method does not need to introduce this assumption in the calculation, and the error is smaller than the indirect integration. The application range of direct integration is wide. Even if the distance between the heliostat and receiver plane is unequal to the focal length, the flux density distribution can be calculated accurately. For the convolution method we proposed before [10], we calculated the intercept factor of ten heliostats mentioned in Section 3.2. The calculated results of ten heliostats are superior to that of the UNIZAR function [3] and the HFCAL function [4]. The indirect integration's average, minimum, and maximum differences are 2.24%, 1.3%, and 2.7%. The indirect method simulates well when the intercept factor is lower than 95%, so, in most cases, it outperforms the UNIZAR and HFCAL functions. When the intercept factor is lower than 95%, the elliptic Gaussian model proposed in this paper is consistent with the indirect integration method. When the intercept factor is higher than 95%, we can see the limitations of the convolution, and the elliptic Gaussian model performs better. As a result, the average absolute difference in the elliptic Gaussian model is 1.11% smaller than the indirect integration method. Furthermore, in Heliostat #9 and #10, where the indirect integration method performs the worst, the absolute difference is only 1.3%. This result embodies the advantage of the direct integration method. This paper has only one assumption about the sun shape, which is as close to the actual physical image as possible.
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 2 The NSBAS Processing Chain Used in the FLATSIM Service
### Time Series Inversion  
The time-series inversion of the line-of-sight displacement follows the principle described in [18; 19; 52], and depicted in Figure 6. We jointly solve for both the total phase delay time series and a temporally smoothed time series. The first one, delivered by FLATSIM, includes both line of sight (LOS) displacement and residual atmospheric phase screen, whereas the second, for which temporal fluctuations at short time scales induced by atmospheric turbulence are filtered out, only serves as a regularization, in case we get disconnected data subsets in the unwrapped interferograms network. Unwrapping errors are corrected iteratively, by searching for the optimal integer number of phase cycles to be added in areas detected as incorrectly unwrapped. For this step, the correction is prioritized on interferograms characterized by a large residual between the initial and reconstructed line-of-sight displacement maps. After a few iterations, the root mean square misclosure map is computed for the whole dataset, together with a root mean square misclosure for each interferogram, and for each acquisition [52]. Using the total phase delay maps at each time step, an average LOS velocity map is calculated by a simple pixel-by-pixel linear least-square regression in the temporal dimension. The velocity map is then slightly improved by iteratively reducing the weight of the acquisitions with large deviations from the trend of the mean displacement.  
Figure 6: Time-series processing. The time series inversion provides, from the input stack of interferograms, maps of cumulative displacement, together with quality indicators. The mean velocity, obtained by linear regression through the time series, is shown in a wrapped color scale.
# A Parcel Transportation and Delivery Mechanism for an Indoor Omnidirectional Robot
## 2 Materials and Methods  
The main material used in this work is an existing omnidirectional mobile robot developed by the Automation and Robotics Laboratory at the Universitat de Lleida [38]. Figure 1 shows the robot, which is based on a tall (1.75 m) and thin central structure that supports a screen used as a robot face and two arms with 4 degrees of freedom each. The motion system of the mobile robot provides omnidirectional mobility capabilities [39; 40], so the robot can rotate and translate simultaneously, being able to maneuver in small spaces. The motion system of the robot has a diameter of 0.56 m, which defines a cylindrical collision-free space around the robot. The omnidirectional mobile robot requires a navigable area with a diameter of 0.66 m of free space around the robot in order to be able to move and follow any trajectory combining translation and rotation. The unrestricted use of arm gestures may require a cylindrical collision-free space around the robot with a diameter of up to 1.50 m.  
Figure 1: Mobile robot with the location of its main sensors highlighted: LIDAR (blue), RGB-D cameras pointing at the ground (red) and to the front (green and yellow), and PIR sensors (orange).  
The mobile robot performs autonomous navigation. It creates a map of the environment using Simultaneous Location and Mapping (SLAM) based on the Iterative Closest Point (ICP) algorithm [41; 42] and follows any path defined in the map by using the A* algorithm [43]. The data required by these processes are gathered by a 2D LIDAR (Hokuyo UTM-30 LX, Osaka, Japan) and an RGB-D camera (Creative Senz3D, Tokyo, Japan). The LIDAR is located in the robot base, while the RGB-D camera is ground-oriented to detect obstacles in front of the robot, holes in the floor or stairs. Two supplementary Creative Senz3D RGB-D cameras provide additional information of the environment around the robot: one camera is in the upper part of the robot, above the screen, while the other is 1 m from the floor. Both cameras are aimed at detecting and discriminating between adults and children in front of the robot. The robot also includes a set of passive infrared (PIR) sensors to detect human activity. The location of all these sensors is highlighted in Figure 1.  
The mobile robot was originally designed as an assistance tool [38], but it has also been used in other applications, for instance as a walk-helper device [44]. Thanks to its versatile motion system, parcel delivery is regarded as a potential application. The mobile robot also includes a passive suspension system [45] that reduces the transmission of vibrations generated by the wheels, making it suitable for transporting delicate packages.
## 5 Instrument Used in Milan and the Specific Mapping Approach Solution Applied to the Case Study
### Possible Improvements of the Global Accuracy  
As cited in Section 5.2, SLAM-based devices tend to drift along long paths due to the very structure of the algorithm. In the case of building surveys such as those in the Milan project, where the development paths are limited to a maximum of 100 to 300 m, maximum deviations of around 10-20 cm have typically been observed.  
In order to keep these deviations under control, the post-processing software of the data acquired with Heron(r), i.e., the Heron Desktop(r) software, allows users to insert control points or control scans (i.e., georeferenced point clouds that are part of static scans) before generating the point cloud. However, such an approach would have required defining a control topographic network for each building complex, effectively doubling the survey costs. One approach that is generally employed in SLAM systems to contain the drift in surveying trajectory estimation is the aforementioned method of performing closed-loop survey geometries. This would have posed a significant constraint for building surveying activities, forcing, for example, the surveying of the stairs of a building, up and down, i.e., two times.
## 4 Conclusions  
Due to the multivariable and nonlinear nature and the presence of time-varying unknown parameters in the X-Y pedestals, using adaptive control schemes is essential to controlling the X-Y pedestals in earth stations for LEO satellite tracking. Hence, this paper proposed an adaptive predictive functional control approach based on unstructured system identification. In the proposed algorithm, Laguerre functions are utilized for system modeling, while PFC is the control law. The results of applying this method to the X-Y pedestal for LEO satellite tracking were compared with the case of using the classic PID controller. Simulation results confirmed that, in comparison with the PID controller, the proposed algorithm achieves a higher accuracy, better control performance, reduction of X-Y pedestal tracking error in LEO satellite tracking, and requires less control effort. One of the main advantages of the proposed adaptive control scheme is its desirable performance against the interference effects of X and Y channels. This feature is obtained by the consideration of these effects in the system identification at every moment, which affects the system model in the next moment.  
Conceptualization, R.D.T.; methodology, R.D.T. and H.G.; software, R.D.T. and H.G.; validation, D.-E.C. and G.C.; formal analysis, D.-E.C. and G.C.; investigation, H.G.; writing--original draft preparation, R.D.T. and H.G.; writing--review and editing, D.-E.C. and G.C.; funding acquisition, D.-E.C. and G.C. All authors have read and agreed to the published version of the manuscript.  
This work has been funding by University POLITEHNICA of Bucharest through the PubArt program.  
Data Availability StatementData sharing not applicable.  
The authors declare no conflict of interest.
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 7 Conclusions  
This work presented a pair of modules that together predict the traffic of users and allocate them at base stations within a realistic urban scenario. The proposed system was quantitatively tested through a secondary test dataset and performed satisfactorily in predicting traffic of users and improved on a previous method of allocating users to base stations by using the predictions of the traffic prediction module. In future work, we would like to address the main limitation, that being the data. We aim to train our system with more diverse and larger real-world datasets, hoping for both better performance and accurate predictions into further time windows. This, however, would also require stronger hardware and a more sophisticated approach to absorb the amount of data that we aim for. Another goal is the deployment of this system in an active 5G network to test its capabilities with real-time data so we can study and understand the challenges of user traffic prediction in a live 5G environment.  
**Author Contributions:** Conceptualization, I.K., I.L., D.T., K.S., A.G., and C.B.; methodology, I.K., I.L., and D.T.; investigation, I.K., I.L., and D.T.; data curation, I.K., I.L., and D.T.; writing--original draft preparation, I.K., I.L., and D.T.; writing--review and editing, I.K., I.L., D.T., K.S., A.G., and C.B.; supervision, K.S., A.G., and C.B.; project administration, K.S., A.G., and C.B.; funding acquisition, K.S., A.G., and C.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I) under the \"2 nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers\" (Project Number: 02440).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 1 Introduction
Inspired by the cascade design pattern in object-level tasks, [PERSON] et al. [26] proposed a high-quality IS network (HQ-ISNet). This method combines a high-resolution feature pyramid network (FPN) [27] with a cascade network, making full use of multilevel feature maps effectively to interpret complex backgrounds in RS images. Subsequently, [PERSON] et al. [28] proposed the consistent proposals of IS network (CPISNet) to improve the feature-extraction method and integrated a cascade network with residual connections into their network to further improve the accuracy of aerial image mask prediction. The development of the attention mechanism [29; 30] has had a great impact on RS image IS. The attention mechanism is an important approach in image processing which can automatically select the most relevant or important parts of the input data by learning weights. It allows the model to focus on specific regions or features, emphasizing areas that contain crucial information, thereby improving recognition accuracy. [PERSON] et al. [31] proposed a SAR ship IS method based on collaborative attention mechanisms which combines the advantages of multiple attention mechanisms and can effectively extract the instance masks of objects in the image. [PERSON] et al. [32] introduced a contour-refinement network that works from coarse to fine. They utilized an attention-based feature pyramid subnetwork to enhance the prediction capability for small objects. The contour extracted from the coarse branch was further refined through the fine branch by learning edge features to achieve fine-grained building contours. [PERSON] et al. [33] proposed a multiscale feature pyramid network which applied the attention mechanism to multiscale features to improve the performance of multiscale ship detection in high-resolution SAR images. Unfortunately, when some
## 3 Results and Discussion
### Kinetics of In Vitro Drug Release from Chrysin-Loaded Microemulsion  
The in vitro release profile of colloidal drug delivery systems is one of the most important physicochemical characteristics for predicting their in vivo performance, for correlating it to the carrier microstructure, and for designing formulations with the desired properties [39]. With these goals in mind, in vitro drug release tests were carried out to verify the vehicle ability to release the drug and understand the rate at which this phenomenon occurs. As shown in Figure 2, chrysin release from CS-ME through a synthetic cellulose acetate membrane was continuous and slow, grew up to 12 h without the burst effect, and then achieved a plateau, corresponding to the total percentage of chrysin released from CS-ME of 31.21%. Such a release profile was probably due to the drug confinement in droplets coated by a surfactant monolayer and/or to CS-ME thermodynamic activity or partitional capacity between the oil and aqueous phases [40].  
A linear regression analysis on the experimental data of the cumulative amount of chrysin released (\(R_{t}\), \(\mu\)g/cm\({}^{2}\)) was conducted to identify the kinetic model that best described drug release. For this purpose, three different models, namely zero-order (\(R_{t}\) versus time), first-order (\(\ln R_{t}\) versus time), and Higuchi (\(R_{t}\) versus square root of time) models were tested, which provided correlation coefficients (\(t^{2}\)) of 0.9907, 0.9249, and 0.9503, respectively. These results, which confirm zero-order kinetics for chrysin release from CS-ME [34], are typical of drug delivery systems suitable for prolonged pharmacological action and resistant to disaggregation, with the active substance being released slowly and regardless of the initial drug concentration [41]. The most important examples of such systems are those employed for the delivery of antibiotics, antidepressants, and medicines for the maintenance of heart and blood pressure and for pain control [42].  
The release coefficient (\(K_{r}\)), calculated from flux and initial drug concentration using Equation (3), was 11.2 \(\mu\)g/cm\({}^{2}\) h, and the lag time for chrysin release to achieve a constant rate was only 9.8 min. Although the in vitro release studies through artificial membranes only provide an indication of the behavior of drug-loaded microemulsions in terms of vehicle ability to deliver the drug, they are usually considered as a satisfactory and unique tool for highlighting the role of NE components and microstructure in drug release. Based on these considerations, the values of percent release, lag time, and \(K_{r}\) obtained in this study for CS-ME can be considered promising for further studies.
## 2 Method
### Governing Equation
By expanding Equation (7) in the X, Y, and \(Z\) directions, the differential equations governing the components of the secondary electric field can be obtained as follows:  
\[\frac{\partial}{\partial y}\left(\frac{\partial\mathbf{E}_{y}^{s}}{\partial x}- \frac{\partial\mathbf{E}_{x}^{s}}{\partial y}\right)-\frac{\partial}{\partial z} \left(\frac{\partial\mathbf{E}_{x}^{s}}{\partial z}-\frac{\partial\mathbf{E}_{x}^{s}} {\partial x}\right)-i\omega\mu_{0}\sigma_{xx}E_{x}^{s}=i\omega\mu_{0}(\sigma_ {xx}-\sigma_{0})E_{x}^{p} \tag{8}\]  
\[\frac{\partial}{\partial z}\left(\frac{\partial\mathbf{E}_{z}^{s}}{\partial y}- \frac{\partial\mathbf{E}_{y}^{s}}{\partial z}\right)-\frac{\partial}{\partial x} \left(\frac{\partial\mathbf{E}_{y}^{s}}{\partial x}-\frac{\partial\mathbf{E}_{x}^{s}} {\partial y}\right)-i\omega\mu_{0}\sigma_{yy}E_{y}^{s}=i\omega\mu_{0}\big{(} \sigma_{yy}-\sigma_{0}\big{)}E_{y}^{p} \tag{9}\]  
\[\frac{\partial}{\partial x}\left(\frac{\partial\mathbf{E}_{x}^{s}}{\partial z}- \frac{\partial\mathbf{E}_{z}^{s}}{\partial x}\right)-\frac{\partial}{\partial y} \left(\frac{\partial\mathbf{E}_{z}^{s}}{\partial y}-\frac{\partial\mathbf{E}_{y}^{s}} {\partial z}\right)-i\omega\mu_{0}\sigma_{zz}E_{z}^{s}=i\omega\mu_{0}(\sigma_ {zz}-\sigma_{0})E_{z}^{p} \tag{10}\]  
In the conductivity tensor \(\mathbf{\sigma}^{*}\), the diagonal components \(\sigma_{xx}\), \(\sigma_{yy}\), and \(\sigma_{zz}\) represent the principal conductivities along the x-, y-, and z-directions, respectively. It can be observed that, for a vertically anisotropic medium, although the conductivities along the principal axes differ, the current density in each direction is only related to the conductivity along that corresponding principal axis. Therefore, Equation (7) can still be discretized using the same method as that employed for isotropic media.