esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Parking Block Segmentation from a Surveillance Camera Perspective
## 4 Experiments and Results
### Expected Results  
Each of the perspectives we used was subject to error sources that could damage our results. On the satellite segmentation step we can have occlusion, objects with very similar texture to parking blocks and no visible parking lines at all. See Figure 7 for a visual reference of these errors.  
Cases of Figure 7a,b, that is, when a transparent roof is occluding the parking block and when a grid like roof is near the parking lot, could be solved using a Bayesian approach (like the one used by [PERSON] and [PERSON] [12]) to learn the overall structure of the parking lot and discarding the areas that deviate too much from the learned parameters. Cases when there is no demarcation in the parking lot Figure 7c are a little bit trickier. To solve such a case we would need to change our selection criteria in the dataset and include more images of unmarked but identifiable parking lots without relying on the ground appearance, e.g., by using the parking spatial arrangement.  
On the camera perspective we can have resolution mismatch and multilevel parking lots. Figure 8a,b show these errors respectively.  
Case Figure 8a could be solved by trivially calculating a linear interpolation of a convex polygon shape. Case Figure 8b could be solved by integrating height data [33] to separate planes in the homography calculation.  
Figure 8: Parking lot examples from a camera perspective in which our method is prone to fail: (**a**) resolution mismatch, (**b**) multilevel parking lot. Red overlay stands for false negatives, blue for false positives and green for true positives. A red outline is shown around the aforementioned problems.  
Figure 7: Parking lot examples from the satellite perspective in which our method is prone to fail: (**a**) transparent roof, (**b**) grid like roof, (**c**) no visible demarcation lines. Red overlay stands for false negatives, blue for false positives and green for true positives.
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 4 Conclusions  
We designed a portable device for audio distress signal identification in urban areas. The process resulted in a database in Spanish language with the shouts recorded in a real setting with low-cost resources, a deep learning model that the validation proved successful in a real situation, and a proposed implementation of the low-cost device using a Raspberry Pi. The performance of the multi-headed CNN proved to be more accurate that other methods in detecting shouted speech and than only using raw audio features. The model was tested on two different datasets and gave positive results on both, as it can successfully distinguish between background noise and relevant data. Furthermore, a Raspberry Pi was shown to be adequate in terms of processing time to implement a device for real-time processing. Putting all the pieces together, we designed a low-cost and portable device capable of detecting audio distress signals in noisy environments. The implementation and widespread of such a device can positively affect smart cities by reducing response times in distress situations.  
Future work includes the implementation of networks with different architectures, such as a multi-headed CNN that uses the combined information of MFCCs, Mel spectrogram, and raw audio data; recurrent neural networks; and long short-term memory networks. It is important to study techniques to reduce the complexity of the network such as pruning to reduce the processing times. To provide better classification results, it is useful to expand the database using low-cost devices and real-world scenarios. Future studies should incorporate and evaluate IoT technologies into the system and recent acoustic sensors for outdoor noise monitoring [23] that have been developed. Moreover, the effect of changing sociodemographic variables can be explored, adding more age groups, education levels, and diverse cultural background in the database.  
\begin{table}
\begin{tabular}{c c} \hline Audio recollection time & 450 ms \\ \hline Pre-processing time & 43 ms \\ \hline Prediction time & 63 ms \\ \hline Memory & 635 MB \\ \hline \end{tabular}
\end{table}
Table 9: Mean computational costs of the proposed device implementation.  
**Author Contributions:** Conceptualization, Methodology, Writing-Review and Editing, J.F.G., A.E.-P., J.C.C., N.V., V.P.-G., J.D.S., A.F.Z., and L.F.G.; Software, Investigation, and Writing-Original Draft, J.F.G., A.E.-P., J.C.C., N.V., V.P.-G., and J.D.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** All authors acknowledge financial provided by the Vice Presidency for Research & Creation publication fund at the Universidad de los Andes.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Challenges in Physical Layer Security for Visible Light Communication Systems
## 1 Introduction
The physical features of the VLC transceiver components enable the eavesdropper to augment its receiver capability and overcome many of the existing PLS techniques. In light of the current state-of-the-art in VLC technology, this article presents several challenges and open problems in securing indoor VLC systems at the physical layer. The main goal of this article is to argue that the majority of PLS studies for VLC undertaken in recent years were considering naive assumptions that excessively favor the system designer and to offer novel insights into the vulnerabilities of current PLS schemes for VLC systems. In Section 2, we begin by explaining the fundamental differences in the security environments in RF and VLC systems. In Section 3, a few examples showing that an eavesdropper can break existing PLS techniques are examined. We offer thoughts on future directions and conclusions in Sections 4 and 5, respectively.
## 3 Results and Discussions
### Hysteretic Curves and Skeleton Curves
#### 3.1.3 Loading Pattern  
As the width of the wall pier of the covered bridge being investigated is extraordinarily large, the influence of the application pattern of a lateral load under a cyclic load on its energy dissipation, stiffness and strength degradation and ductility should be studied.  
The hysteretic curve and skeleton curve of the layered shell element model with different lateral load applications are presented in Figure 8. Figure 8(a.1) shows that the bearing capacity of TestA-6 is significantly greater than that of TestA-2, TestA-7 and TestA-8. This is because in the arrangement of five loading points, the loading point on the pier top corresponding to Node8 is close to the loading point on the left (see in Figure 4), resulting in extrusion and distortion, and causing stress concentration near this position and the pier bottom during the loading process. Similarly, since the loading point corresponding to Node18 is far from the loading points on both sides, this phenomenon does not occur, as shown in Figure 8(b.1). Figure 8(a.1) and Figure 8(b.1) both show that with the increase in horizontal loading displacement, the hysteretic loops of TestA-7 and TestA-8 incline to the displacement axis, but the shapes have no change and more coincidence. The skeleton curves in Figure 8(b.2) indicate that the plastic deformation capacity of each model decreases with the increase in the loading points. According to Figure 8(a.2), the skeleton curves of TestA-7 and TestA-8 are in broad agreement and there is a significant decline after the peak load, while TestA-2 shows a large deformation capacity.  
Figure 8: Hysteretic curves and skeleton curves of different lateral loading modes: (**a.1,a.2**) Node8; (**b.1,b.2**) Node18.
## 2 Materials and Methods
### AR-Based Surgical Navigation  
Our navigation method should provide AR-based surgical navigation for performing osteotomy cuts and reorientation of the acetabular fragment according to the preoperative plan. The following major steps were required: registration initialization, fine registration, tracking of the pelvis, visualization of osteotomy planes/chisels and tracking of the fragment.  
An orthopedic surgeon performed the procedure under realistic OR conditions using the Microsoft HoloLens (Microsoft Corporation, Redmond, WA, USA) as the OST-HMD. Throughout the intervention, the surgeon interacted with the OST-HMD via voice commands, to proceed to the next step of the navigation workflow as well as for displaying or hiding certain objects. Following the PAO description of [PERSON] [1], a modified Smith-Peterson approach was conducted to expose the pelvis bone. Next, two custom-made 3D printed mounts equipped with markers were attached to the pelvis and the assumed region of the fragment using small screws (Figure 4a). The markers enabled tracking of pelvis and the acetabular fragment during navigation. The mount was designed such that it did not interfere with the surgeon or the surgical instruments during the procedure and did not cause additional damage to the anatomy. The steps to be performed under AR-based surgical navigation can be subdivided into three parts: registration, osteotomy guidance and fragment reorientation guidance. An overview of the entire workflow can be found in Figure 5. In the following, a detailed description of our approach and all performed steps is given.  
Figure 4: **(a)** The pelvic and fragment mounts equipped with markers and fixated to the anatomy. **(b)** The surgeon during the procedure wearing the HoloLens.
# Photodegradation Assessment of Calicotriol in the Presence of UV Absorbers by UHPLC/MSE
## 3 Results and Discussion
### UV Irradiation of Calcipotriol  
Subsequently, the photostability of calcipotriol under UVA irradiation was evaluated in the presence of the following photoprotective agents: oxygenase, meradimate, sulisobenzene, avobenzene, dioxybenzene, and homosalate. The photostability testing of calcipotriol was performed in solutions with and without the presence of UV absorbers. The samples were exposed to UVA light for 60 min. The chromatograms of the organic UV filters under study, calcipotriol and its photodegradation products in methanolic solutions after 1 h of irradiation, are presented in Figure 1. The dark control samples were prepared in parallel by shielding them with aluminum foil to prevent light exposure. These samples were then maintained under the same experimental conditions as the irradiated samples for 1 h.  
According to ICH QIB guidelines, \"Photostability testing of new active substances and medicinal products\", assessing photostability is an essential component of evaluating the stability of active pharmaceutical ingredients and finished medicinal products [31]. However, the simultaneous application of UV filters and cosmetic products may affect the stability of the active pharmaceutical ingredient (API) under UV radiation exposure [21; 22; 23; 24]. Calcipotriol degradation was observed in the applied conditions, only in the samples containing sulisobenze--the chromatogram reveals four degradation products of calcipotriol CP-2--CP-5 (Figure 1C). In chromatogram D, peaks at retention times of 8.76 and 8.81 correspond to the degradation products of UV filter avobenzene. Likewise, chromatogram E displays a peak at a retention time of 10.68, which is attributed to the UV filter homosalate. Partial E-Z isomerization of calcipotriol was observed in the presence of meradimate (Figure 1B). In dark control samples containing calcipotriol and UV filters, calcipotriol also underwent unexpected degradation in the presence of sulisobenze, resulting in the formation of degradation products CP-2 to CP-5.  
The stability of an active substance in a drug formulation can vary compared to its stability in solution. Therefore, it is essential to conduct stability studies both on active pharmaceutical ingredients and on finished products. In medicinal products, excipients may influence the degradation kinetics of the active substance. In this study, the stability of calcipotriol in a foam formulation was assessed by irradiating samples containing the active compound, both alone and in the presence of the selected chemical UV absorber sulisobenze. The photodegradation pattern of calcipotriol in the tested foam samples, in the presence ofa UV filter, was analogous to that observed in solutions containing sulisobenzene, with chromatographic peaks corresponding to degradation products CP-2 to CP-5.
# Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
## 4 Experiment and Result Analysis
### Analysis of Experimental Result
Figure 8 (test path 1) shows that the LSTM model produces a maximum localization error exceeding 18 m. In test path 2, the LSTM and Conv1d models exhibit maximum localization errors exceeding 10 m. In test path 3, the maximum localization error for the Conv1d model also exceeds 10 m, while the LSTM model reaches an error of over 100 m. In test path 4, the maximum localization errors for LSTM and Conv1d exceed 10 m. Overall, our model significantly improves the positioning results for large distance deviations. In referring to Table 5, the proportion of localization errors greater than 3 m is considerably higher for Conv1d than for LSTM. However, the overall magnitude of outliers for Conv1d is much lower than for LSTM, indicating that the single, shallow convolutional kernel in Conv1d struggles to capture more abstract spatial information. Figure 8: The distribution of distance errors for different models across the four test paths. In it, (**1**, **2**, **3**, **4**) represent different test paths respectively, and (**a**, **b**, **c**, **d**) correspond to different positioning models respectively. On the other hand, the encoder model has its error distribution more concentrated within the 1-2 m range, with a maximum error not exceeding 7 m. This demonstrates that leveraging attention mechanisms to learn long-term temporal dependencies and global spatial relationships simultaneously is effective. Our proposed model exhibits an error distribution primarily concentrated within the 0.5-1 m range, with a maximum error not exceeding 4 m. This indicates that the multi-scale convolutional kernels in our model are capable of learning deeper local-sensitive features, effectively complementing the encoder structure to optimize localization performance further. Table 6 presents the relevant parameters that reflect the model's complexity and efficiency, including FLOPs (floating-point operations per second), which measure the number of floating-point operations the model can perform within a unit of time. A higher FLOP value indicates a more substantial computational capability of the model), MACs (multiply-accumulate operations, an important metric for evaluating model computational complexity), the number of trainable parameters (Params), and the computation time (Time) required for the model to output the positioning results. The table shows that the proposed Enconv1d model has the highest FLOPs and MACs, indicating that the model possesses strong computational capability and higher computational complexity. Although our model introduces an inevitable increase in parameters and computation time, it improves positioning accuracy from meter-level to decimeter-level while maintaining relatively high computational efficiency compared to other models. Moreover, it can meet the real-time positioning requirements with a computation time of 0.065 ms, which still falls within the millisecond range. This demonstrates that our model balances high accuracy and real-time performance.
# Signal Detection for GSTFIM Systems with Carrier Frequency Offset +
###### Abstract
In Section 2, the GSFTIM system is modeled in the context of CFO. Then, in Section 3, MMSE detectors with different CFO compensation styles are described in detail, followed by another class of developed MP detectors with outstanding performance in Section 4. Section 5 discloses the complexity analysis through theoretical analysis, while Section 6 demonstrates the effectiveness of the developed detectors through simulation results. Finally, concluding remarks are drawn in Section 7. _Notation:_\(\mathrm{E}\{\cdot\}\) and \(\mathrm{D}\{\cdot\}\) represent the expectation and variance operators, respectively. The \(N\times N\) identity matrix is denoted by \(\mathbf{I}_{N}\).
# An Efficient Numerical Approach for Field Infrared Smoke Transmittance Based on Grayscale Images
## 2 Experimental Setup  
The field trial described in this paper is illustrated in Figure 1 below. The experiment site consists of three parts: an infrared radiation target array, a smoke release site, and an observation site.  
Standard blackbodies, which can emit electromagnetic radiation as targets, were placed in lines and columns vertically to form an \(n\times m\) infrared signal array. Here, \(n\) stands for the number of columns in the target array, and \(m\) means the number of lines. The infrared signal array intends to cover most of the smoke diffusion path to provide more target information. In order to determine the size of infrared signal array, the size of the smoke cloud and atmospheric turbulence at the scene need to be taken into account.  
According to the infrared blackbody calibration theory, the geometrical resolution on the blackbodies at the given range should be at least twice but preferably four to five times the geometrical resolution of the system (IFOV) [18]. Therefore, the distance between two blackbodies in the infrared signal array was set to be five times the geometrical resolution of the system.  
The distance between the observation site and the smoke release site was selected to get the optimum spatial resolution for imaging systems. At the observation site, images of the infrared signal array during the smoke diffusion process were recorded by a thermal infrared imager. When the smoke cloud covers certain targets, the smoke transmittance can be evaluated by comparing the targets radiation before and after smoke release.
## 2 Methodologies
### Orbit and Clock Evaluation Methods  
Several methods can be utilized to evaluate the accuracy of precise orbit and clock. For example, the overlapping orbit comparison, satellite laser ranging inspection, and comparison with a reference product. The first two usually are used to evaluate the final products of IGS precise products. Thus, the third method is adopted in this paper by using the final products provided by WHU as references. The orbit differences can be expressed as,  
\[\begin{bmatrix}\Delta X\\ \Delta Y\\ \Delta Z\end{bmatrix}_{t}=\begin{bmatrix}X_{RT}\\ Y_{RT}\\ Z_{RT}\end{bmatrix}_{t}-\begin{bmatrix}X_{WHU}\\ Y_{WHU}\\ Z_{WHU}\end{bmatrix}_{t} \tag{6}\]  
where \(t\) is the epoch number; \(\left[\Delta X\ \ \Delta Y\ \ \Delta Z\right]^{T}\), \(\left[X_{RT}\ \ Y_{RT}\ \ Z_{RT}\right]^{T}\), and \(\left[X_{WHU}\ \ Y_{WHU}\ \ Z_{WHU}\right]^{T}\) are the orbit differences, real-time orbit, and final orbit vector.  
The clock products from different ACs contain a benchmark inconsistency error. In this paper, to eliminate the system inconsistency, the GPS and BDS-3 constellations use G01 and C19 as reference satellites for the single difference from other satellites [28]. The STD of the clock difference between real-time and the reference clock after a single difference is estimated. The degree of clock consistency greatly influenced the solution accuracy of PPP [29]. The PPP-B2b products are estimated using the real-time observations collected by the regional tracking stations in China. Thus, the satellites rise or fall more frequently compared with using global observations. Furthermore, it is challenging to select a stable reference BDS satellite to generate the inter-satellite differences. To assess the accuracy of PPP-B2b clock products, we re-edited the inter-satellite difference clock using the method proposed by [23]. The expression of this method can be written as,  
\[\
abla\Delta t^{s}=(t^{s}_{RT}-t^{s}_{WHU})-\frac{1}{M}\sum_{i=1}^{M}{(t^{i}_ {RT}-t^{i}_{WHU})} \tag{7}\]  
where \(\
abla\Delta t^{s}\) is the double-difference clock value; \(t^{s}_{RT}\) is PPP-B2b real-time clock; \(t^{s}_{WHU}\) is the final precise clock of WHU; \(M\) is the number of available satellites.  
Due to the observation discontinuity of the BDS satellites, the clock values estimated in Equation (7) are segmented, which affects the clock STD value. Therefore, the discontinuity is compensated by,  
\[\
abla\Delta t^{s}_{t}=\
abla\Delta t^{s}_{t}-\Delta D_{t,t-1} \tag{8}\]With  
\[\Delta D_{t,t-1}=\left\{\begin{aligned} \frac{1}{M}\sum\limits_{i=0}^{M} \Delta\
abla\Delta t^{i}_{t,t-1^{\prime}}\left|\frac{1}{M}\sum\limits_{i=0}^{M }\Delta\
abla\Delta t^{i}_{t,t-1}\right|\geq 0.1\text{ ns}\\ 0,\left|\frac{1}{M}\sum\limits_{i=0}^{M}\Delta\
abla\Delta t^{i}_{t,t- 1}\right|<0.1\text{ ns}\end{aligned}\right. \tag{9}\]  
where \(\Delta D_{t,t-1}\) is the compensation term to compensate the discontinuity in the double-difference clock; \(\Delta\
abla\Delta t^{i}_{t,t-1}\) is the inter-epoch difference clock based on double difference clock.
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 2 Materials and Methods
### Processing Methods  
The collected data had to be first converted to video; then, the individual images were separated, the position was assigned, and a 3D reconstruction of the focused area was performed. Several software tools were used to process the measured data, including one developed for this project, 360 VisualSync ([[https://github.com/Luckays/360](https://github.com/Luckays/360) VisualSync]([https://github.com/Luckays/360](https://github.com/Luckays/360) VisualSync), accessed on 7 March 2025). Figure 5 shows a graph of low-cost mobile mapping processing.  
Figure 4: Visual time synchronization on Fleming Square.  
Figure 5: Graph of low-cost mobile mapping processing.  
2.6.1. Insta360 Studio  
In this software [32], the video from the two sensors was stitched together into a single video. The data were kept without significant editing and image stabilization. The spherical video was exported in an MP4 format, an encoding format of H.264, and a bitrate of 120 Mbps. The export time of a 10 min video is around 25 min.  
2.6.2. 360 VisualSync  
The software offers several ways to process video: video parsing, time stamping, and geotagging. The software is available at [[https://github.com/Luckays/360](https://github.com/Luckays/360) VisualSync]([https://github.com/Luckays/360](https://github.com/Luckays/360) VisualSync), accessed on 7 March 2025.  
The basic function is just to parse the video into single frames at 1 s intervals. After entering the beginning time of the video captured from the visual synchronization of the first frame, the software assigns UTC time to the single frames. If a GPX file with measured coordinates is provided to the software, these coordinates and their accuracies are written to the individual images. In case of missing coordinates, the software calculates the approximate position of the images for easier alignment in the following software. Using this software, it is possible to carry out geotagging with RTK or PPK GNSS coordinates.  
2.6.3. Real-ESRGAN  
Since the images captured from the spherical camera video are of lower quality compared to taking single images, one set of images was edited by artificial intelligence tools. For this purpose, Real-ESRGAN [33] software ([[https://github.com/xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)]([https://github.com/xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)), accessed on 20 March 2024) was used, which aims to improve image quality and resolution by using machine learning. For this project, the already pre-trained realesrnet-x4 plus model was included in the 360 VisualSync app. Figure 6 shows the image before and after AI image editing. A very visible change is the unification of the plaster color and the highlighting of the edges of the window holes. Unfortunately, there is also deformation of some objects, as shown in the red circle.  
Figure 6: Difference between images: (**a**) original image; (**b**) enhanced by Real-ESRGAN; (**c**) overlap of original and enhanced images.