esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Algorithm and Validation
#### 3.1.2 Standardised Coefficients  
Results of the multiple regression model show a strong correlation between the quantities of GHG emissions and the independent variables. The determination coefficient (R\({}^{2}\)) reaches a value of 0.98; only a very small percentage of the GHG variation (2%) cannot be explained by the combination of the three variables, GDP, population and RES. On the other hand, the obtained \(p\)-values for the estimation of the GHG emissions generation are less than 0.05. This confirms that, with a confidence level of 95%, the variation explained by the model is not due to chance. Ultimately, these values are sufficiently high to consider the influence of the variables in the estimation of GHG emissions generation proven.  
The standardised coefficients allow us to establish the relative significance of the input variables (independent variables) on the output variable (dependent variable). The standardised coefficients are as follows: \(\beta_{1}\) = 0.250 (GDP); \(\beta_{2}\) = 0.738 (population) and \(\beta_{3}\) = -0.001 (RES). The variables \"GDP\" and \"Population\" present greater influence (greater standardised coefficients) than the other independent variable (RES). However, the estimation model shows a greater degree of accuracy when the \"RES\" variable is included instead of including only GDP and population.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & & & \multicolumn{3}{c}{**Independent Factors**} \\ \cline{4-5}  & **Member State (\(i\))** & & \(WV_{a_{i}}\) & \(WV_{b_{i}}\) & \(WV_{c_{i}}\) \\ \hline
1 & AU & Austria & 0.028 & 0.019 & 0.045 \\
2 & BE & Belgium & 0.034 & 0.025 & 0.011 \\
3 & BU & Bulgaria & 0.004 & 0.016 & 0.041 \\
4 & CR & Croatia & 0.004 & 0.009 & 0.050 \\
5 & CY & Cyprus & 0.002 & 0.002 & 0.036 \\
6 & CZ & Czechia & 0.015 & 0.024 & 0.026 \\
7 & DE & Denmark & 0.022 & 0.013 & 0.054 \\
8 & ES & Estonia & 0.002 & 0.003 & 0.067 \\
9 & FI & Finland & 0.017 & 0.012 & 0.071 \\
10 & FR & France & 0.179 & 0.149 & 0.026 \\
11 & GE & Germany & 0.246 & 0.184 & 0.018 \\
12 & GR & Greece & 0.015 & 0.025 & 0.036 \\
13 & HU & Hungary & 0.009 & 0.022 & 0.028 \\
14 & IR & Ireland & 0.020 & 0.011 & 0.008 \\
15 & IT & Italy & 0.136 & 0.135 & 0.025 \\
16 & LA & Latvia & 0.002 & 0.004 & 0.070 \\
17 & LI & Lithuania & 0.003 & 0.007 & 0.057 \\
18 & LU & Luxembourg & 0.004 & 0.001 & 0.010 \\
19 & MA & Malta & 0.001 & 0.001 & 0.023 \\
20 & NE & Netherlands & 0.057 & 0.038 & 0.007 \\
21 & PL & Poland & 0.035 & 0.086 & 0.053 \\
22 & PO & Portugal & 0.015 & 0.023 & 0.036 \\
23 & RO & Romania & 0.014 & 0.045 & 0.035 \\
24 & SK & Slovakia & 0.007 & 0.012 & 0.015 \\
25 & SN & Slovenia & 0.003 & 0.005 & 0.046 \\
26 & SP & Spain & 0.089 & 0.105 & 0.021 \\
27 & SW & Sweden & 0.036 & 0.022 & 0.085 \\ \hline \end{tabular} \(a\): GDP; \(k\): population; \(c\): RES; and \(i\): member state.  
\end{table}
Table 4: Weighting values of each member state.
# Task-Driven Real-World Super-Resolution of Document Scans
## 3 The Proposed Framework for Task-Driven Training
### Network Architecture and Loss Function  
To address the challenge of super-resolving real-world document scans for OCR-related tasks, we propose to enrich the training process of a baseline SR technique with auxiliary components for semantic supervision. In our study, we have selected the well-established SRResNet network [9]. Its simple and well-understood architecture allows for fast training, offering a good compromise between the model size and reconstruction accuracy, as confirmed in many studies [40,41]. Although more complex models like EDSR [20] or SwinIR [23] retrieve higher reconstruction quality scores, they are also more likely to hallucinate image details, which is not beneficial in task-oriented applications. Importantly, in our earlier study [35], we considered several different architectures for OCR-oriented SISR--even though it was limited to simulated datasets, it showed that SRResNet is an optimal choice.  
In our work, we focus on the ability to reconstruct task-relevant semantics essential for OCR, in addition to the pixel-wise similarity between the super-resolved image and the HR reference. A key element of our framework is the integration of pretrained CTPN, CRNN, and Key.Net models into the loss function exploited for training the SR network.  
Importantly, the weights of these pretrained models are not updated during training; hence, the models preserve their original behavior. The models for text detection (CTPN) and recognition (CRNN) were integrated into a single pipeline (the CTPN and CRNN models are available at [[https://github.com/courao/ocr.pytorch](https://github.com/courao/ocr.pytorch)]([https://github.com/courao/ocr.pytorch](https://github.com/courao/ocr.pytorch)) (accessed on 1 June 2025)) that allows for solving the OCR task in an end-to-end manner [28]. During training, these networks serve as proxy-supervisors: rather than requiring manually annotated labels, we extract high-level feature activations from their deepest layers and compare them between the super-resolved image \(\hat{I}\) and the ground-truth HR image \(I_{\text{HR}}\) using the L1 distance. In this way, each previously-trained network implicitly generates its own \"labels\" based on learned representations, yielding a semi-self-supervised training regime that enforces semantic consistency without explicit annotations.  
Furthermore, we incorporate a pretrained Key.Net model (independent from OCR) to extract image keypoints, because text regions in a super-resolved image and the HR reference should exhibit similar keypoint patterns. Although Key.Net was originally designed for general-purpose keypoint detection, we test its ability to complement OCR-driven losses by encouraging the similarity in terms of the structural alignment. These auxiliary loss functions guide the SR model to preserve both textual and structural cues.
## 4 Precise Point Positioning Results
### Academic Comparison  
To the authors' knowledge, four independent academic partial validations of the phase biases generated by the CNES PPP-WIZARD demonstrator have been performed.  
In the reference [45], the authors made an exhaustive analysis of the CNES phase biases, both at the satellite level and the PPP level. This study showed that the use of all the constellations significantly improved positioning accuracy and convergence time. Unfortunately, the BDS-3 biases were not fully available at that time. The static position root mean square (RMS) values are in accordance with the ones presented in this paper, but the kinematic position RMS values are less precise.  
The second one was conducted by NRCan using the following frequency combinations: L1/L2/L5 for GPS, E1/E5a/E6 for Galileo, and L2/L6/L7 for Beidou 2 and 3. Figure 15 shows the PPP-AR positioning errors of (68 th percentile) of multiple 5 min runs in various configurations. The improvement brought by Galileo and Beidou phase biases is clearly visible.  
The authors of the reference [46] analyze the benefit of using the real-time phase biases computed by the CNES PPP-WIZARD demonstrator and conclude, as presented in this proposed paper, that using the phase biases improves the positioning accuracy from the PPP-float mode to the PPP-AR mode. In addition to this, they introduced an interpolation method to overcome situations in which phase biases are missing and predict them in such situations.  
In the same way, the reference [40] concludes that the CNES phase biases can be used to improve the positioning accuracy in terms of the dispersion of the results around the mean value. On top of that, the mean position errors are smaller, which emphasizes the fact that the errors are more centered around zero.
# Extracellular Vesicles for Regenerative Medicine Applications
## 3 Pre-Clinical Applications of EVs in Tissue Engineering and Regenerative Medicine
### Bone and Cartilage Regeneration  
The regeneration of bone and cartilage are active research interests in TERM and the utility of EVs in enhancing bone and cartilage regeneration has been shown as promising in pre-clinical results. EV-based therapies derived from MSC sources have been shown to promote cartilage regeneration in vitro and in vivo through increasing type II collagen production and inhibiting cartilage degradation [112; 113; 114; 115]. Furthermore, MSC-derived exosomes have been shown to promote cartilage regeneration in vivo when printed into hydrogel scaffolds [116]. In bone regeneration, osteoblast-produced EVs and periodontal-ligament stem cell-derived EVs, and specifically exosomes, have been shown to promote bone regeneration and facilitate osteoclast activation [117; 118; 119; 120; 121]. Biomineralization vesicles (matrix vesicles) have also shown promise in the engineering of bone tissue through promoting bone formation and the calcification of bone matrix [60; 62; 122; 123].  
Moreover, there has been extensive research focused on the therapeutic efficacy of EVs in the axial skeleton, with particular emphasis on treating intervertebral disc degeneration. Specifically, exosomes derived from MSC sources, such as bone marrow [124; 125; 126; 127; 128; 129], human urine [130; 131] adipose [132], human placenta [133], and human umbilical cord [134], have all shown promise in reducing various aspects of inflammation and damage in intervertebral disc degeneration. In addition to MSC-derived exosomes, exosomes isolated from nucleus pulposus cells [135; 136; 137], endplate chondrocytes [138; 139; 140], annulus fibrosus cells [141], platelet-rich plasma [142], and notochondral cells [143] have shown similar early promise in the treatment of intervertebral disc degeneration.  
In addition to regeneration of bone and cartilage, EVs have also been explored as a therapy for pathophysiological conditions that affect the joint such as rheumatoid arthritis (RA); for example, bone marrow-derived dendritic cells can be genetically modified to produce exosomes enriched with anti-inflammatory molecules, such as IL-10 [144; 145] and IL-4, [146] or pro-apoptotic molecules, such as FasL [147], or immune checkpoint inhibitors, such as CTLA4-Ig. [148] EV have also been isolatedfrom the plasma of antigen-immunized mice [149]. All these approaches have shown pre-clinical efficacy in RA through the immunomodulation of innate and adaptive immune cell populations [144; 145; 146; 147; 148; 149]. In addition, MBVs have been shown in vivo to mitigate RA through the immunomodulation of macrophage phenotypes locally and systemically in a rat model [150]. MBVs, a distinct population of extracellular vesicles, represent an alternative to these fluid-phase exosome therapies, with minimal processing and manipulation in comparison [70; 74].
## 1 Introduction
### Limitations of the Present Models  
The existing models can be classified into three groups, depending on the possibility to calculate from them simple analytical expressions of the different electrical quantities (current, internal voltage, dissipated energy, etc.), for the four modes of operation described above. The first group would consist of the constant capacitance RC series model. This is the only model for which valid analytical expressions exist to study the four modes of operation. This is the reason why it is the most widely used, even if the results are less accurate than those obtained with more sophisticated approaches. In the second group would be the series RC model, with capacitance linearly varying with voltage and a single branch. For this model, only analytical studies of its operation at a constant current can be found. In fact, the methods for the estimation of its parameters are based on the analytical expressions for DLSC operation when discharged at a constant current. For this model, there are no published analytical expressions when operating at a constant power, nor is it possible to find studies when charging or discharging the cell through a voltage source or discharging at constant resistance. The third group would be formed by the rest of the models (models with several parallel RC branches, distributed parameter models, etc.), which are more accurate but much more complex, making it very difficult, if not impossible, to obtain simple analytical expressions that allow the calculation of the main electrical variables when operating in any of the four operational modes. Therefore, their use is usually restricted to numerical calculation or simulation.  
Although numerical methods and the use of simulators are useful and, in certain cases, they are the only way to analyze the behavior of DLSCs, having analytical expressions is a great advantage, since they allow for a simple, fast and accurate analysis. Moreover, conclusions can be drawn much faster from them than with other methods, which imply multiple simulations and laboratory tests.
## 3 Results and Discussion
### Effect of Ionic Strength  
As can be observed from Figure 5, the co-existence of sodium cations and dye molecules in the solution decreases the adsorption yield. Furthermore, the increase in the ionic strength is unfavorable to the adsorption efficiency. Because the ionic strength of the solution controls either electrostatic or non-electrostatic interactions between the adsorbate and the adsorbent surface [42], the electrostatic attraction mechanism for our cationic dyes can be suppressed with the addition of Na\({}^{+}\) ions at a high concentration, due to the competition for the active sites on the adsorbent surface, leading to electrostatic repulsion [43].  
Figure 4: Effect of adsorbent dosage on the percentage of removal and amount of dyes adsorbed on 4 AZW: (**a**) MB; (**b**) MG (C0(MB) = 8 mg/L, C0(MG) = 41 mg/L, contact time 70 min, T = 25 \(\pm\) 1 \({}^{\circ}\)C and unadjusted pH).
## 2 Data and Theory Description
### Theory and Method of Retrieving PWV
#### 2.2.2 PWV Derived from Radiosonde  
The RS data derived from IGRA includes a variety of meteorological parameters, such as P, T and specific humidity, which are widely used in various studies, including model establishment [41], atmospheric process and climate research [42]. However, the PWV value cannot be detected from the RS data directly and the corresponding calculation is required to obtain PWV. Previous study has given a specific procedure to calculate the PWV [43] and the main process can be divided into three steps as follows:  
(1) According to the layered water vapor pressure and temperature provided by RS, ZWD is calculated by layered superposition. In the present study, PWV was calculated from the height layered superposition of the surface pressure to the top layer pressure of 300 hPa [43; 44]:  
\[{\rm{N}_{w}}=\frac{\sum\limits_{1}^{n}\frac{3.73\times 10^{5}\times{\rm{p}_{ \rm{V}}}}{{\rm{T}}^{2}}\times\frac{dh}{1000}} \tag{6}\] \[{\rm{ZWD}}=\int\limits_{{\rm{P}_{0}}}^{{\rm{P}}}{\rm{N}_{w}}{\rm{ dp}}\]  
where p\({}_{\rm{v}}\) is the water vapor pressure (unit: hPa); N\({}_{w}\) is the wet refraction and dh is the layer height difference (unit: m); T is the temperature (unit: k); p\({}_{0}\) is the surface pressure, p is the top layer pressure.  
(2) According to the water vapor pressure and temperature provided by RS, T\({}_{\rm{m}}\) was calculated by layered superposition [19]:  
\[{\rm{T}_{m}}=\frac{\sum\limits_{1}^{n}\frac{{\rm{p}_{\rm{v}}\times{dh}}}{{ \rm{T}}^{2}}}{\sum\limits_{1}^{n}\frac{{\rm{p}_{\rm{v}}\times{dh}}}{{\rm{T}}^ {2}}} \tag{7}\]  
(3) PWV was finally retrieved by multiplying the conversion factor as presented in Equation (3).
###### Abstract  
In recent years, the low-Earth-orbit (LEO) satellite network has achieved considerable development. Moreover, it is necessary to introduce edge computing into LEO networks, which can provide high-quality services, such as worldwide seamless low-delay computation offloading for unmanned aerial vehicles (UAVs) or user terminals and nearby remote-sensing data processing for UAVs or satellites. However, because the computation resource of the satellite is relatively scarce compared to the ground server, it is hard for a single satellite to complete massive deep neural network (DNN) inference tasks in a short time. Consequently, in this paper, we focus on the multi-satellite collaborative inference problem and propose a novel COllaborative INference algorithm for LEO edge computing called COIN-LEO. COIN-LEO manages to split the complete DNN model into several submodels consisting of some consecutive layers and deploy these submodels to several satellites for inference. We innovatively leverage deep reinforcement learning (DRL) to efficiently split the model and use a neural network (NN) to predict the time required for inference tasks of a specific submodel on a specific satellite. By implementing COIN-LEO and evaluating its performance in a highly realistic satellite-network-emulation platform, we find that our COIN-LEO outperforms baseline algorithms in terms of inference throughput, time consumed and network traffic overhead.  
Keywords:edge computing; satellite network; collaborative inference +
Footnote †: journal: Advances in Neural Information Processing Systems
# Sustainable Urban Water Management in China: A Case Study from Guangzhou and Kunming
## 6 Water Sensitive Solutions for Guangzhou and Kunming
### Solutions for Kunming
#### 6.2.1 Approaches in Asset Management  
In terms of asset management, drainage systems deteriorate as a result of aging, high demand, overuse, and inadequate maintenance, and rehabilitation is typically neglected until a catastrophic failure occurs [58]. As a result, recovery is both difficult and costly. Thus, a faulty drainage system will impair the operation of other critical infrastructures, including the water supply system, healthcare system, and environment. Indeed, Kunming's waterlogging problem is serious enough that it has a detrimental effect on traffic, living standards, and even economic services, to the point that economic losses may exceed the building and maintenance costs. Thus, drainage and sewerage system assets are allocated considering the whole decision-making life cycle, which includes engineering, planning, construction, operation, and maintenance [59].  
Kunming is also embroiled in a hindering situation of municipal planning and land availability for commercial activities. Because some unmanaged natural wetlands have gradually become commercial land, the cities' hydrological functions will suffer as a result of a lack of green space. Even the land that was originally allocated for the sewage treatment plant to be built on is now constrained. Therefore, during the feasibility analysis of Sponge City construction, reasonable urban planning should also be considered [60].
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 3 Analytical Method
### Response Spectrum  
Response spectrum is a plot of the peak response of a series of SDOF systems with varying natural frequencies that provides practical information for the seismic design of structures [22; 23]. Based on the response spectrum, ground response measurements and predictions by the analysis program and proposed CNN models are compared.
## 4 Included Application Cases
The results indicate that the differences between the estimated vegetation-covered areas from Sentinel and the results based on high-resolution images from Google Earth are low. The use of GIS in urban areas to identify the best location to build a new hospital was presented by [PERSON] et al. in [24]. The authors focused their work on the Gaza Strip. They proposed using a model to evaluate the hospital site's suitability area based on 14 conditioning factors and the location of current hospitals. Most of the conditioning factors were geoinformation variables such as slope, topographic wetness index, or road distance. For the models, the authors included support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models to evaluate the suitability of the area. The results indicate that twelve conditioning factors (including slope degree, plan curvature, distance from the residential area, and population density, among others) were suitable for use. Their results were characterized by R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models. A final potential use of remote sensing or GIS systems is to exploit the UAVs used for image gathering for additional use, such as by providing a connection to land-based devices as a mobile gateway. This possibility was shown by [PERSON] et al. in [25], in which a drone used for crop monitoring was able to collect the data of land-based nodes. The authors analyzed that if given a flying height and flying velocity, selected according to the crop being monitored, the drone can be used as a mobile gateway for a different density of land-based nodes. The authors included in their proposal different WiFi configurations and different antennas. The drone operation algorithms and the nodes and message exchange were proposed. The results indicate that this solution might be used by adjusting the antenna coverage for flying heights that are less than 100 m for different flying velocities (from 1 to 20 m/s) for different tested node densities. Nonetheless, this solution can only be applied to dense scenarios (1 node every 60 m\({}^{2}\)) if the drone's flying velocity is 1 m/s and the flying height is less than 24 m.
## 5 Data Collection Station and Hardware Setup  
The camera parameters and specifications are shown in Table 5. Compared to Basler acA1920-48 gc, Basler a2A1920-51 gcPRO can compress frames and downscale frame resolution at the camera.  
The virtual machine (VM) runs on an industrial server at the edge of the network to process the live camera feed. The VM consists of 16 virtual processors on Intel(r) Xeon(r) Silver 4210R CPU. It has an NVIDIA GRID T4-16Q GPU with 16 GB of dedicated memory, 64 GB of RAM, and SSD storage.  
A Lambda Workstation located at ETS university is utilized to train the model and analyze the recorded videos. It has an AMD Ryzen Threadripper 3960X CPU, two NVIDIA GEFORCE RTX 3090 as GPUs with 24 GB dedicated memory each, 128 GB of RAM, and SSD storage. Additionally, there are two more servers involved which the framework utilizes to store and visualize data. An SQL server is responsible for storing the prediction data while a server running Ignition(r), a web-based industrial automation software, visualizes the results on the user interface.