esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Deriving mission requirements for ocean meso-scale applications  
### Merging multiple altimeter missions to map ocean mesoscale signals  
Mapping the ocean mesoscale variability can be performed through optimal interpolation methods which use an a priori knowledge of the space and time scales of the ocean signal. When data are mapped onto a regular space/time grid using such interpolation methods, the along-track long wavelength errors (or high-frequency ocean signals) can induce artificial cross-track gradient at smaller scales and thus spurious eddy signals [8]. The effect is particularly important in low eddy energy regions and when several altimeter data sets are merged. Along-track long wavelength errors can also lead to other serious problems. A well-known example comes from GEOSAT for which errors in the tide correction were aliased to produce spurious Rossby wave like signals in the mapped data [9]. To minimize these problems, the mapping method should either take into account an along-track long wavelength error (i.e. a correlated noise due to orbit, tidal or inverse barometer residual errors) or the long wavelength errors should be removed before mapping [8]. Merging multi-satellite altimeter data sets is generally necessary to map the mesoscale variability. To merge multi-satellite altimetric missions, it is first necessary to have homogeneous and cross-calibrated data sets. Homogeneous means that the same geopotential model and reference systems for the orbit and the same (as far as possible) instrumental and geophysical corrections should be used (e.g. same tidal models, same meteorological models, etc). Cross-calibrated means that relative biases and drifts must be corrected and also that the orbit error must be reduced. An effective methodology is to use the most precise mission such as T/P and Jason-1 as a reference for the other satellites [10-11]. This allows a reduction of orbit error for the other satellites to few cm rms even if the initial orbit errors are as large as 1 m rms [10].  
When altimetric data have been homogenized and cross-calibrated, the next step is to extract the sea-level anomaly (SLA) for the different missions. It is preferable that the SLAs from different missions be calculated with respect to the same ocean mean using a common reference surface (e.g. either a very precise mean sea surface or mean profiles consistent between the different missions). The final step is to merge the SLAs from the different missions via a mapping technique. [PERSON] et al. [12] provide a detailed analysis of T/P and ERS-1/2 merging over a five year period, in particular to quantify the contribution of merging in the description of the ocean mesoscale circulation.
## 2 Materials and Methods
### Model Improvement  
#### 2.2.1 Improved YOLOv11 Network Model Construction  
YOLOv11 is the latest YOLO version, inheriting and refining the core concepts and technical frameworks of its predecessors to enhance detection speed, accuracy, and robustness [29]. Significant improvements have been made in its backbone network and neck layers. Addressing the current challenges of large model parameters, large file size, and low detection accuracy in maize leaf disease detection in complex scenarios, this study enhances the YOLOv11s model.  
Specifically, the backbone network uses RepLKNet as the feature extraction network. RepLKNet utilizes advanced convolution operations and re-parameterization techniques to significantly reduce computational load while maintaining feature extraction capabilities, thereby enhancing the model's lightweight characteristics. In addition to its foundational architecture, RepLKNet incorporates the Partial Spatial Attention (PSA) module, which directs the model's focus toward critical feature regions. Furthermore, the neck network integrates the CBAM, an attention mechanism that enhances the model's ability to capture multi-scale features by combining both channel and spatial attention strategies. This dual approach enables a concentrated emphasis on key features related to pests and diseases. Moreover, the neck network aggregates features of different scales through the ELAN-W module, which employs the SPPCSPC module for spatial pyramid pooling, further improving the handling of multi-scale features. The detection head incorporates the DynamicHead module, which integrates various attention mechanisms to unify scale, spatial, and task awareness during object detection, significantly boosting the representational capacity of the detection head without additional computational overhead. The proposed Adaptive Spatial Feature Fusion (ASFF) module adaptively fuses shallow and deep features to reduce variance in the feature scale, accurately detecting large, medium, and small targets. The accuracy of bounding box regression is improved by optimizing the loss function through the Symmetric IoU Loss Function (WIoU). WIoU optimizes the precision of bounding box localization, thereby enhancing the model's overall detection performance.  
Figure 1: Original dataset: (**a**) healthy, (**b**) rust, (**c**) gray leaf spot, (**d**) northern leaf blight, (**e**) southern leaf blight, and (**f**) round spot disease.  
Lastly, the dynamic adjustment mechanism (DynamicATSS) dynamically selects positive and negative samples based on statistical information obtained during training, replacing the traditional fixed-ratio allocation strategy and significantly improving the model's generalization capabilities.  
Figure 2 presents improved YOLOv11s-RCDWD model. Through the optimizations presented above, our model excels in maize leaf disease and pest detection, achieving high precision and performance in locating and identifying diseases and pests while maintaining excellent lightweight characteristics suitable for deployment on agricultural mobile devices.
## 3 Results and Discussion
### Photoluminescence Measurements
However, one possible emission mechanism in the Eu\({}^{3+}\)/Yb\({}^{3+}\) doped ZnSe semiconductor is presented in Figure 10. Yb\({}^{3+}\) is often used as a co-doping ion since it has a single excited state \({}^{2}\)F\({}_{5/2}\) with an energy difference to the ground state \({}^{2}\)F\({}_{7/2}\) of 10,000 cm\({}^{-1}\) (1000 nm), which matched the phonon energy of the Eu\({}^{3+}\) ion (>10,000 cm\({}^{-1}\)) (Figure 10). In the so-called cross-relaxation process (I), Eu\({}^{3+}\) ion, after being excited by 488 nm radiation, in the \({}^{5}\)D\({}_{0}\) excited state, can transfer its energy to two Yb\({}^{3+}\) ions mediated by phonons, as indicated in the Figure 10 [24]. However, the population of Eu\({}^{3+}\) excited levels can be recovered again because of the cooperative energy transfer process (II) mediated by the pair of previously excited Yb\({}^{3+}\) ions [25] according to the following relation:  
\[2\times\ Yb^{3+}(^{2}\text{F}_{5/2})+\text{Eu}^{3+}(^{7}\text{F}_{0})=2\times \ Yb^{3+}(^{2}\text{F}_{5/2})+\text{Eu}^{3+}(^{5}\text{D}_{1}) \tag{5}\]  
The probability of this mechanism is very low; therefore, a second most probable enhancement emission mechanism could be related to the charge transferring between the Yb\({}^{3+}\) and Eu\({}^{3+}\) ions. In addition, the excitation wavelength (488 nm-2.54 eV) may also give rise to interband transitions (Eg\(\sim\)2.5 eV). Thus, energy transfer from the matrix itself should not be discarded. Collectively our results suggest that co-doping with Yb\({}^{3+}\) enables one to enhance the emission efficiency, thus representing an important advantage if we compare it with other similar compounds that only contain Eu\({}^{3+}\)[26; 27; 28]. Figure 10: Energy level diagram for Eu\({}^{3+}\)/Yb\({}^{3+}\) co-doped ZnSe semiconductors showing activity transitions and ground state field splitting.
## 2 Aerodynamic Model and Radar Model
### Radar Measurement Model  
The radar measurement model [14] was previously described in our paper [7]. In the radar measurement coordinate system, the coordinates of the HGV centroid are \(\left[\begin{array}{cc}x&y&z\end{array}\right]^{T}\), and based on coordinate transforming relations we can know the coordinates of the HGV centroid in the spherical coordinate system. According to the radar principle [14], range (R), azimuth angle (A) and elevation angle (E) can be expressed as:  
\[\left\{\begin{array}{c}A=\left\{\begin{array}{c}R_{0}=\sqrt{x^{2}+y^{2}+z^ {2}}\\ \arctan(\frac{z}{z})+\pi,x<0\\ \arctan(\frac{z}{z}),x>0,z>0\\ \arctan(\frac{z}{z})+2\pi,x>0,z<0\\ E=\arcsin(y/R_{0})\end{array}\right.\end{array}\right. \tag{4}\]  
In HGV tracking we assume the radar measurement noises are known independent white noises, so the radar measurement equations can be expressed as:  
\[\left\{\begin{array}{c}\widetilde{R}_{0}=R_{0}+\varepsilon_{R}\\ \widetilde{A}=A+\varepsilon_{A}\\ \widetilde{E}=E+\varepsilon_{E}\end{array}\right. \tag{5}\]  
where \(v_{R}=\left[\begin{array}{cc}\varepsilon_{R}&\varepsilon_{A}&\varepsilon_{ E}\end{array}\right]^{T}\) has been defined in our paper [7].  
The statistical characteristics of radar measurement noises \(v_{R}\) can be expressed as  
\[R=\left[\begin{array}{ccc}\sigma_{R}^{2}&0&0\\ 0&\sigma_{A}^{2}&0\\ 0&0&\sigma_{E}^{2}\end{array}\right] \tag{6}\]  
where \(\sigma_{R}^{2}\), \(\sigma_{A}^{2}\) and \(\sigma_{E}^{2}\) are measurement error variances of the range, azimuth angle and elevation angle respectively.
# Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data
## Materials and Methods  
### Patient Image Dataset  
Tumor operations were guided by using a neuronavigation system (SonoNavigator, Localite, Sankt Augustin, Germany), including an AplioXG ultrasound device (Toshiba Medical Systems Europe, Zoetermeer, Netherlands) with 2D ultrasound transducers. At the beginning of the intervention, preoperative 3D contrast T1 Magnetic Resonance (cT1 MR) data were registered with the patient's head, based on anatomical landmarks. The procedure was then improved using a surface-based registration technique. During the operation and in addition to the acquisition of intraoperative B-mode ultrasound volumes (noted 3D-iUS), two 3D intraoperative contrast enhanced ultrasound (3D-iCEUS) data were acquired after the injection of 4.8 milliliter of an ultrasound contrast agent (SonoVue, Bracco s.p.a., Milan, Italy) at a rate of 3.0 mL/min using a syringe pump (ACIST Vueject, Bracco s.p.a, Milano, Italy) and using the contrast harmonic imaging (CHI) method. The size and density of the micro-bubbles in the SonoVue contrast agent was 1.9 \(\pm\) 0.1 micromillimeter and 3.4 \(\pm\) 0.5 micro-bubbles/mL as recently reported in [24]. The first volume, denoted 3D-iCEUS\({}_{Start}\), was obtained transdurally immediately after the craniotomy when the tumor was still entire, and the second one, denoted 3D-iCEUS\({}_{end}\), was acquired after removing the tumor at the end of the surgery. The surgeon scanned the cerebral region of interest (ROI) with the ultrasound probe (large linear array transducer with a field of view of 38 mm, a range of frequencies of 4.8 MHz to 11.0 MHz and an average frequency of 8 MHz was selected for the acquisitions). The transducer was tracked. The neuronavigation system reconstructs a 3D volume from the 2D-iCEUS slices and overlaid it onto the cT1 MR data. The pixel size in the original 2D ultrasound images is 0.422 mm \(\times\) 0.422 mm and the voxel size of the reconstructed 3D-iCEUS data is \(1\times 1\times 1\) mm\({}^{3}\). The data involved in the surgery process are shown in Figure 1.
# Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System
## 3 The Purpose of the Investigations
### 5G Micro Base Station  
From the perspective of supply requirements, 5G micro-base stations required significantly higher power supply capacity and stability than 4G. If some 5G micro-base stations were mounted on street light poles, the separate deployment of power supply systems could be reduced, as the street light system can provide an uninterrupted power supply. From the perspective of construction cost, the total cost of ownership (TCO) of 5G micro base stations mainly comes from the construction of base stations and ancillary equipment, site rental, operation, and maintenance costs. The micro base stations are mounted on smart streetlights through proper site selection, which can help save much cost and avoid waste of land resources caused by rebuilding new poles. From the perspective of construction speed, the small coverage radius of 5G micro base stations leads to an exponential increase in demand compared to 4G base stations. The separate establishment of new poles consumes a certain amount of time. Therefore, to accelerate the construction of 5G, street light resources can meet the basic needs of operators for 5G and shorten the construction cycle. From the perspective of system synergy, the application of 5G micro base stations in multiple scenarios can be realized through the synergy between different devices. For example, 5G micro base stations can provide high transmission rate road information for autonomous vehicle driving by cooperating with various road sensors carried by smart streetlights. The synergy effect between systems can be better utilized to reflect the intelligence of smart cities.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## Data and Methods  
### Data and Study Regions  
We present hereafter glacier surface velocity maps for three different mountain ranges in different climatic regions: (1) the southern Alps of New Zealand in Oceania with a temperate maritime climate; (2) the Cordillera Blanca in the tropical Andes; and (3) the European Alps which have a wide variety of climatic influences from the Mediterranean to continental [17]. The southern Alps of New Zealand have large (up to tens of km\({}^{2}\)) and fast-flowing mountain glaciers, while the glaciers of the Cordillera Blanca rarely exceed 2 km in length and many of them have a highly crevassed surface due to steep slopes. The glaciers of the European Alps are larger than the Andean glaciers but smaller than the New Zealand glaciers, with both steep and flat slopes.  
For all these regions, we estimate glacier surface velocity fields at the regional scale using Sentinel-2 data with a resolution of 10 m for the years 2017 and 2018. Using a statistical analysis, we compare the performance of Sentinel-2 with USGS/NASA Landsat 7/8 (15 m resolution for the panchromatic band) over the three study areas. In the Alps, we compare our glacier surface speed fields with differential GPS in-situ measurements on the Argentiere, Mer de Glace and Otztal Alps glaciers. We compare the performance of Sentinel-2 with very high resolution (0.5 m) Pleiades imagery acquired as part of the Kalideos project funded by the CNES (French Space Agency). In addition, we also used 15 satellite images from the Planet Labs Doves constellation that are acquired using the PlanetScope instrument at a resolution of 3 m. In this study, we used pairs of Planet images separated by time periods ranging from 7 to 45 days. Planet Labs is a commercial company that aims at imaging Earth every day using a fleet of more than 120 miniature CubeSat satellites. Finally, we compare Sentinel-2 and Venus results. Venus is a satellite mission developed through the cooperation between the Israeli Space Agency (ISA) and CNES, which acquires optical images at a spatial resolution of 5 m in predefined regions. As part of the CNES Pointing program, Venus data have been acquired every two days and are freely available since 2018 for the Southern Alps of New Zealand (Tasman Glacier region). The data used in this study along with the corresponding technical information are summarized in Table 1.  
Over the Alps, we used the in-situ differential GPS measurements from the GLACIOCLIM observatory to validate our measurements of glacier surface velocity ([[https://glacioclim.osug.fr/](https://glacioclim.osug.fr/)]([https://glacioclim.osug.fr/](https://glacioclim.osug.fr/))). GLACIOCLIM GPS speed data were obtained by measuring the displacement of stakes during the hydrological years 2016-2017 and 2017-2018 (September 2016 to August 2018) on the Argentiere and Mer de Glace glaciers. We also used the annual ice speed estimates provided by [18], where ice speed was also measured following the position of stakes for glaciers in the Otztal Alps (Austria) in 2015, 2016 and 2017.
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 5 Cyber-Physical System
### Physical Asset: Smart PhotoVoltaic Station
#### 5.1.2 Sensors and Actuators  
The SPVS features a set of sensors and actuators used to perceive and interact with the surrounding environment. On the sensors side, the device is equipped with sensors to assess the air quality (temperature, atmospheric pressure, and relative humidity) and a DC power sensor that is used to collect data on the live power production of the PV panel, the power stored in the battery, and the power used by the system in performing its operations.  
The station is also capable of tracking a desired orientation. To do so, on the actuators side, the device's mechanical gear work is actuated through a couple of servo motors. One of them is devoted to control the pitch angle of the panel, whilst the other one controls its yaw angle (as shown in Figure 8).
## 3 Computational Aeroacoustic (CAA) Model of Human Phonation
### CAA Results
#### 3.3.1 Acoustic Sources  
Figure 8 presents the aeroacoustic sources computed by PCWE (26) from three CFD simulations. The sources, which are a function varying in space and time, were transformed from the time domain into the frequency domain by the Fourier transform, which provides a useful insight into the spatial distribution of the sources at distinct frequencies. The first row in Figure 8 shows the aeroacoustic sources at the fundamental frequency (frequency of vibration of the vocal folds) \(f_{0}=100\) Hz. The strongest sources are located inside the glottis. The laminar simulation results in slightly higher source intensities than the LES simulations. This correlates with the flow rate amplitude, which is also higher in the laminar case (see Figure 2). At the third harmonic frequency \(f_{2}=300\) Hz, aeroacoustic sources are predicted also downstream of the glottis and in the supraglottal vestibule. Especially in laminar and WALE cases, disturbances induced in the shear layer of the jet are clearly visible. The third and the last row refer to a higher harmonic frequency \(f_{9}=1000\) Hz and a non-harmonic frequency \(f=1235\) Hz. At these higher frequencies, the dominant aeroacoustic sources do not occur within glottis, but in the places, where the fast glottal jet interacts with the ventricular folds and with the slowly moving and recirculating air in the supraglottal volume.  
Figure 8: Spatial distribution of sound sources [kg m \({}^{6}\) s \({}^{2}\)] in mid–coronal plane at four frequencies (as a result of Fast Fourier Transform).
## 5 Methodologies
### Hardware and Software  
Only limited local resources were required to carry out this research due to the relatively low dimensionality of FTS data compared to other data domains (e.g., text, images, video). Further, the use of dilated kernels in QGAN and RSQGAN TCN architectures is efficient in terms of the number of parameters needed to cover a large effective receptive field size [63], enabling training to take place on a local GPU within a reasonable length of time.  
The local hardware resources used were: i7-8750H @ 2.20 GHz, 16 Gb RAM, NVIDIA GTX 1060 GPU.  
The key packages used were Python 3.7.7, Pytorch 1.4.0, Plotly 4.9.0, ggs [20], and stylefact [11].  
\begin{table}
\begin{tabular}{l l} \hline
**Stylised Fact (SF)** & **Evaluation Metric** \\ \hline
1. Linear unpredictability & \(Corr(r_{t},r_{t+k})=\frac{\mathbb{E}[(r_{t}-r_{t})(r_{t-k}-r_{t})]}{\rho_{cf}^ {\tau}}\) \\  & \(Corr(r_{t},r_{t+k})\approx 0,k\geq 1\) \\
2. Heavy-tailed distribution & \(f_{R}(r)\propto r^{-k}\) \\  & \(f_{R}(r)\) is the p.d.f of \(R\), \(\kappa\in[3,5]\) \\
3. Volatility clustering & \(Corr(|r_{t}|,|r_{t+k}|)\propto k^{-\beta}\) \\  & up to large \(k\), small \(\beta\) \\
4. Leverage effect & \(L(k)=\frac{\mathbb{E}[r_{t}|r_{t+k}|^{2}-r_{t}|r_{t}]^{2}}{\mathbb{E}[|r_{t}|^{2 }]}\) \\  & \(L(k)<0\) for \(k\in[1,10]\) \\
5. Coarse–fine vol correlation & \(\Delta\rho_{cf}^{\tau}(k)=\rho_{cf}^{\tau}(k)-\rho_{cf}^{\tau}(-k)\) \\  & \(\rho_{cf}^{\tau}(k)=Corr(|\sum_{i=1}^{\tau}r_{t+k-i}|,\sum_{i=1}^{\tau}|r_{t-i}|)\) \\  & for some \(k>0\)\(\Delta\rho_{cf}^{\tau}(k)<0\) \\
6. Gain/loss asymmetry & \(T_{wait}^{t}(\theta)=\begin{cases}\inf_{r}r_{t+t^{\prime}}\geq\theta,\theta>0 \\ \inf_{r}r_{t+t^{\prime}}\leq\theta,\theta<0\end{cases}\) \\ \hline \end{tabular}
\end{table}
Table 1: Description of stylised facts.
## 1 Introduction
The WRPM not only effectively removes foggy regions from images but also preserves the original details of the image to the greatest extent possible. The low cost of training and the high inference efficiency of the backbone network suggest that its application in practice is both feasible and beneficial. We refer to this integrated approach as Urban Green Space SAM (UGSAM). The main contributions of this work are as follows:  
1. Due to the variable climate in cities, rainy and foggy weather greatly affects the clarity of urban street scene images. Currently, there are few algorithm models for all-weatherurban vegetation positioning. We propose a high-precision urban vegetation positioning algorithm UGSAM that can perform well in bad weather. The UGSAM consists of a multimodal large model SAM, a defogging module WRPM, and a correction network SCP-Net. We find that this algorithm performs excellently in bad weather and also has certain performance improvements in normal weather. 2. To ensure that the UGSAM can maximize defogging while minimizing the damage to the original features of the image, we propose a dual-branch structure WRPM based on channel attention and pixel attention. WRPM includes the dense fog feature network FFA-Net and the light fog feature network LS-UNet, which can fully extract the diverse features of the image. Finally, the features are fused through FIM to achieve refined defogging. To further improve the positioning accuracy, we trained the correction network SCP-Net to conduct a secondary verification on the segmented image, obtaining more accurate results. 3. The experimental results show that the UGSAM has the best positioning accuracy in all weather conditions. Meanwhile, it requires less time for training and inference and has certain transferability, making it suitable for deployment in urban scenarios.