esa-sceva/satcom-chunk-collection huggingface dataset:
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 4 Smart Mobility Services and Applications
### Autonomous Vehicles
#### 4.4.2 Vehicle Sensors  
Sensors will play a key role in asserting AVs and in the confidence that citizens will have in these vehicles and the safety they can offer. The sensors will be an essential part of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication and will feed Advanced Driving Assistance Systems (ADAS) in the recommendations given to drivers. Examples of these recommendations include warnings for the driver to stay in the central part of the road, avoiding drowsiness or distraction when managing cruise control, or in more complex situations that cross information from the outside weather with the driver's condition (calmer, more aggressive), or with their heartbeat, which are among many other aspects that can be monitored in order to collect as much information as possible to create a safe driving environment that drastically reduces the number of accidents.  
In order to collect the information mentioned above, it is necessary to have several sensors in the vehicles, namely environment sensors which are responsible for collecting information from the environment surrounding the vehicle [86]. Within the environmental sensors, there are cameras and remote radars placed in front of the vehicle, long-range radars that allow the detection of objects in front of the vehicle when it is at high speed and Lidar Laser Scanners that allow the detection of objects at medium distances. It is normal for each of these sensors to be placed in different locations on the vehicle because of the effects of time, the detection angle and the maximum distance at which an object is identified. Table 5 summarizes the vehicle's main sensors, location and their purpose.  
Figure 7: Level of automation of autonomous vehicles.
## 5 Simulations and Results
### Pipeline Inspection  
In the second simulation, we assumed that a pipeline inspection was carried out. Then, both the static sensors and mobile sensors were taken into account. The amount of static sensors was still 100, and that of the mobile sensor was 1.  
This mobile sensor was actually a handheld terminal that can be carried by an inspector. The travel rate of the inspector was set to be 2 grids per round. When the inspector arrives at a grid that has  
Figure 6: Energy consumption _versus_ simulation time in normal production monitoring.  
Figure 7: Information gain _versus_ simulation time in normal production monitoring.  
a static sensor node, he would check the sensor node and send the status back to the base station with the help of the mobile sensor. It took about 500 rounds for the inspector to complete the whole pipeline inspection.  
The dump energy of the mobile sensor's battery is shown in Figure 8. Each time the mobile sensor was selected as a cluster header, the power consumption would increase accordingly. That's because not only the data from the mobile sensor, but also the data from the cluster member, was transmitted to the base station during such a procedure. It can be concluded that the less time the mobile sensor has been selected as a cluster header, the more dump energy its battery will preserve till the end of the pipeline inspection.  
In this simulation, we care more about whether this mobile sensor would be selected as a cluster header during the pipeline inspection. The results show that when using LEACH, the mobile sensor would be selected as a cluster header for 13 times. When using CWCA, the times that the mobile sensor is selected as a cluster header would be related to the weight of \(\alpha_{3}\). Once fixed \(\alpha_{1}=\alpha_{2}=0.2\), and changing the weight of \(\alpha_{3}\) and \(\alpha_{4}\), there would be a result such that the smaller \(\alpha_{3}\) is, the more likely it is the mobile sensor would be selected as a cluster header, as shown in Figure 9.  
Figure 8: The dump energy of the mobile sensor’s battery when using CWCA.  
Figure 9: The relationship between \(\alpha_{3}\) and the total times that a mobile sensor has been selected as a cluster sensor.
## 4 Results
### CYGNSS Derived SMC from ICESAT2 and SMAP/Sentinel1  
We further compute CYGNSS SMC estimates employing BSR from ICESat-2 and compare the performance against CYGNSS SMC with SMAP BSR (CYGNSS \(\cap\) ICESat-2 \(\cap\) SMAP). The results are  
Figure 8: The sensitivity of the reflectivity correction (\(R_{rl}^{2}\) vs \(\Gamma_{rl}\)) at a constant incidence angle (\(\theta=15^{\circ}\) and \(\theta=30^{\circ}\)) for different BSR and VOD values.  
Figure 9: From top to bottom, SPs validation test (CYGNSS \(\cap\) SMAP \(\cap\) ICESat-2) using ICESat-2 roughness on 1 (panels **a–c**), 3 (panels **d–f**), 4 (panels **g–i**), and 6 (panels j–l) April 2019. In the left column (panels **a,d,g,j**), the scatter plot shows SMAP versus CYGNSS SMC estimates [cm\({}^{3}\)-cm\({}^{3}\)] for the subset validation-test employing ICESat-2 roughness. In the middle column (panels **b,e,h,k**), the same subset as seen with SMAP roughness. In the right column (panels **c,f,i,l**), the corresponding locations for each subset. Northern regions (summer monsoon and dry winter, Cwa) are in blue and southern regions (without dry season, Cfa) are in red.  
shown in Figure 9, where the right panels show the resulting locations of each SP. The corresponding scatter plots for each case are shown in the left (CYGNSS SMC with ICESat-2 BSR) and the middle (CYGNSS SMC with SMAP BSR) panels (Figure 9). We divide the analysis for northern regions and southern regions. Higher SMC values are observed when employing ICESat-2 BSR. We can observe a clear difference range between SMAP and ICESat-2 BSR data, where the high sensitivity of ICESat-2 BSR ranges from 0 to 30 m, and that of SMAP BSR from 0 to 20 cm (see color bars in Figure 2). As a result, the corresponding correction to attenuation of vegetation increases as ICESat-2 BSR increases.
## 3 Simulations
### Influence of Circular Target Size  
Gaussian noise is incorporated into the circular target profile and the light stripe used for calibration. The noise level is set to \(\sigma\) = 0.6 mm. The target is placed twice and its diameter varies from 10 to 70 mm at intervals of 10 mm. The relative errors of the calibration data obtained at different target diameters are presented in Figure 9. This shows that the calibration precision increases as the circular target diameter increases from 10 to 50 mm. Moreover, the relative error decreases at target diameters above 50 mm. Thus, a circular target with a diameter of 50 mm is manufactured and utilized in the actual experiment.
## 2 Literature Review  
### Cross-Line and Skip-Stop Operation in Rail System  
The previous studies of cross-line and skip-stop operation mainly focus on the determination of stopping planning and operation frequency. Two issues are studied separately, and scholars previously focused on only one specific issue. Regarding the skip-stop pattern, there is much research in the field of urban rail transit. Typically, the optimization models were established by building the relationships between the objective functions and stopping patterns to meet the passenger demand [2], and the goal of these models is to minimize passenger travel time and operation costs [3; 4; 5; 6]. [PERSON] [7] proposed a coordinated optimization scheme which combined passenger inflow control with train rescheduling of skip-stopping strategies for minimizing the penalty value of passengers being stranded. The researches of cross-line in urban rail transit are mainly focused on the technical requirements and applicable conditions of passenger demand. [PERSON] [8] compared the cross-line organizational patterns between Japan and European countries. [PERSON] [9] analyzed the applicable conditions for operating cross-line trains according to passenger flow demand and found that cross-line operation is necessary when passenger flow that passes by the junction station is high enough. [PERSON] [10] proposed a routing planning design model for Y-type urban rail transit aimed at minimizing the passenger travel time and train-operating distance. [PERSON] [11] proposed the train operation adjustment method of cross-line trains to solve the problem of small-scale delay in the urban rail transit network. [PERSON] [12] studied the cross-line editing train graph technology of the network operating based on the single-line train graph editing. For the combination of cross-line and skip-stop pattern, an integrated optimization with cross-line and skip-stop is proposed to optimize the frequencies, stopping patterns, and operation zones of express/local trains in reference [13]. In these studies, the passenger path choice was based on specific assumptions and uniform assignment.
# Needle Segmentation in Volumetric Optical Coherence Tomography Images for Ophthalmic Microsurgery
## 1 Introduction
To the best of our knowledge, there is no systematic empirical research addressing the needle segmentation in volumetric OCT images. Previous studies from [19; 20] focused on the visualization of volumetric OCT. [PERSON] et al. [7; 21] studied the surgical instrument segmentation in cross-sectional B-scan image. In their work, the position of the cross-section is localized by microscope image, and the pattern of needle in B-scan  
Figure 1: Description of a conventional ophthalmic microsurgery. image is simplified. Therefore, their methods cannot be directly applied to segmenting the needle in volumetric OCT images. This study focuses on developing the approaches in two popular mechanisms for needle segmentation in volumetric OCT images. We propose two methods, corresponding to mechanisms of manually feature exaction and automatically feature exaction, to tackle difficulties for the needle segmentation in OCT images: (a) with the needle shadow principle [22], a conventional method based on morphological features (MF) comes to the mind; (b) another approach is based on the recently developed fully convolution neural networks (FCN) [23], which has been applied for MRI medical image analysis. The MF method is usually straightforward which needs features with parameters manually input upon the analysis of all situations. The FCN is a complicated network which can learn all features automatically by properly training the model based on dataset. We extend the FCN method to identify the needle in OCT images. The main contributions of this paper are: (a) A specific FCN method is developed and compared with the conventional method for the needle segmentation with different pose and rotation; (b) A benchmark including 60 OCT cubes with 7680 images is set up using ex-vivo pig eyes for evaluation of both methods. The rest of the paper is organized as follows: Section 2 gives the basic configuration of OCT and typical patterns of needle in OCT B-scan images, afterwards, two methods are presented for needle segmentation in detail. We carry out the experiments and describe the results in Section 3. Section 4 gives the discussion and Section 5 concludes the presented work.
## 3 The Design of an Innovative Coil for Magnetic Stimulation
### Calculation of the Electromagnetic Fields  
The stimulation coil can be regarded as many single-turn coils. The magnetic flux density can be determined by Biot-Savart's law [11] in Equation (4).  
\[\mathbf{B}=\frac{\mu_{0}}{4\pi}\oint\limits_{C}\frac{i(t)d\mathbf{l}\times\mathbf{r}^{ \prime}}{\left|\mathbf{r}\right|^{3}} \tag{4}\]  
where \(\mu_{0}\) is the permeability, \(\mathbf{d}\mathbf{l}\) is a vector along the path \(C\) whose magnitude is the length of the differential element of the wire in the direction of current, \(l\) is a point on path \(C\), and \(\mathbf{r}^{\prime}=\mathbf{r}-\mathbf{l}\) is the full displacement vector from the wire element \(\mathbf{d}\mathbf{l}\) at point \(\mathbf{l}\) to the point at which the field is being calculated (\(\mathbf{r}\)).  
Due to the similar permeability of the air and the nerve cells, the electric field in the nerve cells can be calculated by Equation (5):  
\[\mathbf{E}=-\frac{\partial\mathbf{A}}{\partial t}-\
abla\varphi \tag{5}\]  
where \(\mathbf{A}\) is the magnetic vector potential, \(\varphi\) is the scalar potential, and \(\mathbf{E}\) is the induced electric field strength.  
The magnetic vector potential is then simplified by Equation (6):  
\[\mathbf{A}=-\int\frac{\mu_{0}Ni(t)d\mathbf{l}}{4\pi R} \tag{6}\]  
where \(N\) is the turns of the stimulation coil and \(R\) is the radius of the coil. \(i(t)\) is the current in the coil.  
Therefore, the induced electric field can be expressed by Equation (7):  
\[\mathbf{E}=-\frac{\mu_{0}N}{4\pi}\int\left(\frac{\partial i(t)}{\partial t}/R \right)\cdot d\mathbf{l} \tag{7}\]  
Figure 3: Framework of the TMS system including insulated gate bipolar translator (IGBT).  
The distribution of the electromagnetic field can be obtained based on Equations (4)-(7) by programming in MATLAB. The core code of the loop calculation is to calculate the integration. In this research, the single turn of the coil was divided into 41 parts. After loop calculation, the electric field can be calculated. To verify the calculation by MATLAB programming, the simulation model based on the finite element method (FEM) was been carried out based on Equation (8) [12].  
\[\
abla\times\
abla\times\left(\mu_{0}^{-1}\mu_{r}^{-1}\mathbf{A}\right)+\left(j \omega\sigma-\omega^{2}\varepsilon_{0}\varepsilon_{r}\right)\mathbf{A}+(\sigma+j \omega\varepsilon_{0}\varepsilon_{r})\
abla\varphi=0 \tag{8}\]  
where \(\sigma\) represents the electrical conductivity tensor, \(\varepsilon_{0}\) is the permittivity of vacuum, and \(\varepsilon_{r}\) is the permittivity of tissue. The boundary of the simulation area is defined as an open-domain, where \(\mathbf{A}=0\).  
As can be seen from Figure 4, the coil was modeled in three dimensions. The diameter of the proposed coil was set to the mean diameter of the prototype and simplified with uniform multi-turn coils. When the boundary conditions, the excitation conditions, and the material parameters were set up, the distribution of the electromagnetic field could be calculated by solving the Maxwell equations based on FEM.
# 3D Face Model Super-Resolution Based on Radial Curve Estimation
## 3 Method
### Radial Curves Database  
As radial curves are used as the main feature of face models, we establish a radial curve database for the subsequent processing. As described in Section 1, the face database we used contains 111 sets of face models. Each set consists of a low resolution 3D face model and a high resolution 3D face model. Considering \(N\) sets of low resolution and high resolution face models in face database \(D\), for low resolution face models, we extract \(K\) curves \(C=\{c_{1},c_{2},...,c_{K}\}\) from each model as described in Section 3.1 using \(\beta\) as rotation angle. Then we can get the low resolution face model radial curve database \(D_{lowC}=\{D_{lowC1},D_{lowC2},...,D_{lowCK}\}\), where \(D_{lowCi}(i\in[1,K])\) represents the set of the \(i\)-th radial curve from all the low resolution face models. Similarly, for high resolution face models, we extract corresponding radial curves using the same \(\beta\). These curves form the high resolution face model radial curve database \(D_{highC}=\{D_{highC1},D_{highC2},...,D_{highCK}\}\). Figure 3 shows radial curves and numbering when \(\beta=10^{\circ}\); i.e., \(K=18\).
# Coordinated Optimization of Feeder Flex-Route Transit Scheduling for Urban Rail Systems
## 3 Mathematical Model
### Model Assumptions  
Reducing transfer waiting time is typically achieved by increasing bus departure frequency through shorter intervals. However, excessively increasing bus departure frequency raises operational costs, and deploying larger-capacity vehicles requires consideration of road conditions for feeder flex-route transit. These approaches may not effectively address capacity shortages at specific stations during peak periods. The operation of urban rail and feeder flex-route transit is affected by multiple factors, and their surrounding environments are relatively complex. Therefore, before constructing the model, appropriate simplifications and assumptions should be applied based on real-world conditions.  
(1) The capacities of urban rail and flex-route transit are assumed to meet demand, and each passenger boards the nearest arriving vehicle, irrespective of congestion levels. This assumption is realistic for commuters during peak hours; (2) Urban rail transit operates strictly according to a predetermined schedule, while flex-route transit consistently departs on time, with any station departure delays effectively managed. This assumption serves as a reasonable approximation commonly applied in transfer optimization research [20,21]; (3) Flex-route transit vehicles are assumed to operate at a uniform speed; (4) the required transfer time is considered constant.
## 4 Discussions and Comparative Analysis
### Capability of Tracking GMPP  
Due to the fact that the solar PV system does not receive uniform sun irradiance in very close sites in a short time, there is a possibility of partial shade due to unavoidable conditions. These conditions may be responsible for the formation of multiple peaks (LMPPs) on the I-V and P-V characteristics, which affects the MPPT's tracking efficiency. The online and offline MPPT techniques are incapable of tracking the GMPP under partial shading conditions, whereas intelligent MPPT techniques are capable of tracking the GMPP under all abnormal conditions, such as uniform and nonuniform conditions.
## 4 Example Verification
### Experimental Result Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Input Size** & **P (\%)** & **R (\%)** & **mAP (\%)** & **FPS** \\ \hline Faster R-CNN & 640 \(\times\) 640 & 88.5 & 82.5 & 87.8 & 17 \\ YOLOv3 & 640 \(\times\) 640 & 85.0 & 83.1 & 86.6 & 54 \\ YOLOv4 & 640 \(\times\) 640 & 87.0 & 84.3 & 88.0 & 50 \\ YOLOv5s & 640 \(\times\) 640 & 88.9 & 83.5 & 87.1 & 56 \\ YOLOv5s-ESC (Ours) & 640 \(\times\) 640 & 87.5 & 87.1 & 89.3 & 42 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with state-of-the-art models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **EIOU** & **Detection Layer** & **CA** & **P (\%)** & **R (\%)** & **mAP (\%)** & **FPS** \\ \hline YOLOv5s & \(\times\) & \(\times\) & \(\times\) & 88.9 & 83.5 & 87.1 & 56 \\ YOLOv5s-E & \(\surd\) & \(\times\) & \(\times\) & 88.9 & 84.2 & 87.5 & 55 \\ YOLOv5s-ES & \(\surd\) & \(\surd\) & \(\times\) & 86.5 & 87.7 & 88.8 & 48 \\ YOLOv5s- & \(\surd\) & \(\surd\) & \(\surd\) & 87.5 & 87.1 & 89.3 & 42 \\ ESC(Ours) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation test results. layer introduction (ES variant) drove the most significant performance shift, boosting recall by 4.2% (83.5%\(\rightarrow\)87.7%), while incurring a 12.7% speed penalty (55\(\rightarrow\)48 FPS), translating to 3.3 recall percentage points per 10 FPS decrease. Conversely, the final CA addition (ESC) yielded marginal mAP gains (0.5%) despite a further 12.5% speed drop (48\(\rightarrow\)42 FPS), suggesting diminishing returns for resource-constrained deployments.
## 3 Results  
### Data Source  
The Gaofen-7 satellite is a high-resolution Earth observation satellite that operates in a sun-synchronous orbit at an altitude of about 506 km. The observation data used in this study are spaceborne GPS dual-frequency data with a 1 s sampling interval, and the observation arc lasts for 24 h. The reference orbit is based on post-mission precise orbit data provided by Wuhan University. This study compares and analyzes the orbit determination accuracy of the same gravity field model at different orders using on-orbit data from the Gaofen-7 satellite collected between 20 March 2022, and 28 April 2022. The Gaofen-7 satellite has a repeat cycle of about 60 days. Although only 40 days of on-orbit observation data have been acquired thus far, this timeframe adequately captures the fundamental aspects of the orbital cycle, ensuring strong representativeness. It accurately depicts the satellite's operating behavior and orbit determination accuracy throughout this stage of its orbit.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 1 Introduction
Additionally, some researchers have focused on recognizing pallets from images and extracting pose parameters using depth sensors such as laser radar and structured light. The authors of these studies were able to achieve more robust performance in semi-structured environments. In [18], a three-foot pallet template was created to match the scattering image of LiDAR data. In a color image, an edge template was used to match the distance-transform image. The two results were combined based on the maximum percentage of couple points. In [30], a pallet model was created and pose was estimated using an ICP method applied to LiDAR data. In [31], a classifier with a feature grid template and gray-scale features (normalized pair differences) was proposed, and it was shown that pallets could be identified with the classifier. The algorithms used color thresholds in [32; 33; 34]. Moreover, other researchers trained a classifier [35] to detect a wooden pallet from images using Haar-like features, and the results were verified with an adaptive structural feature and direction-weighted overlapping ratio in [36]. The aforementioned approaches used different types of sensing, but they all involved single-sensor devices, such as laser scanners and cameras (monocular or stereo). A laser scanner only provides distance information, but it has the advantages of being stable and robust against light. In contrast, a camera provides vision data that include rich information. However, an RGB-D sensor combines a camera and a distance range sensor to obtain both color and distance information. The camera obtains image data by CMOS, and each pixel of the image records red, green, and blue color data [37; 38; 39; 40] that are used for recognition. The depth sensor obtains depth information for every pixel. For tray image recognition,researchers have mostly focused on the recognition of a single ray at the expense of the spatial relationship between trays and the environment. For pallet image recognition, researchers have mostly focused on the recognition of a single pallet at the expense of the spatial relationship between pallets and the environment [41; 42; 43]. This paper proposes an algorithm to recognize pallets and locate them using an RGB-D sensor in a semi-structured environment. This algorithm was adapted to situations with multiple pallets and with differently shaped pallets. An accuracy experiment was conducted to test the algorithm. A labeled template matching algorithm was developed to accelerate the matching speed, and this algorithm served as a basis for studying object detection in unstructured environments. The simplicity of operation should enhance the flexibility of autonomous forklifts and expand the applicability of autonomous forklifts in complex environmental conditions.