esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Temperature Test for Competitive Selection of Optimal Channel Scheme
Specifically, the outlet-wall temperatures for A1 and A2 were approximately 467 K, while B1-M and B2-M exhibited outlet temperatures of around 475 K. Additionally, the category B channels displayed a more pronounced temperature drop in the inclined segment. Finally, focusing on the critical performance assessment, as expected, A1 and B1-M, which barely passed the previous round, exceeded the temperature limit in this round, with maximum temperatures of 867.3 K and 822.2 K, respectively. In contrast, A2 and B2-M comfortably passed the test once again. Notably, B2-M exhibited a peak temperature of only 720 K, which is 80 K below the threshold, suggesting it could still meet the requirements even under further reduced flow conditions. The results of the two rounds of testing for the parallel arrangement are presented in Figures 19 and 20. Figure 19 shows the temperature distribution from the first round,  
Figure 17: The outer-wall temperature distributions for the cooling channels A1, A2, B1-M, and B2-M during the first-round tests in the series arrangement. Figure 18: The outer-wall temperature distributions for the cooling channels A1, A2, B1-M, and B2-M during the second-round tests in the series arrangement. where the flow rate was 1 g/s, and the inlet temperature was 300 K. The overall distribution pattern remains consistent with the previous observations, so it is not described further here. However, it is important to note that the temperature difference between the outlet and inlet is significantly larger compared to that of the series arrangement. Specifically, the outlet-wall temperatures for the category A channels are approximately 380 K, while for the category B channels, the outlet temperatures are around 400 K. The maximum temperatures are arranged in descending order as follows: 865.9 K (B2), 833.3 K (B1), 792.9 K (A1), 738.1 K (B1-M), 669.2 K (A2), and 633.4 K (B2-M). Once again, A1, A2, B1-M, and B2-M passed the test. Figure 20 presents the results of the second round, where the flow rate was reduced to 0.5 g/s, and the inlet temperature remained at 300 K. The difference between the inlet and outlet wall temperatures became even more pronounced, with A1 and A2 showing outlet wall temperatures of 447 K, and B1-M and B2
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 4 Discussion  
### Significant Publications Referencing EO Satellite Data  
Table 3 shows the top ten significant publications referencing EO satellite data that were obtained using CiteSpace software. The strength of the burst represents the impact of this article to the remote sensing community, and the beginning and end years indicate the time range of its influence [63].  
As shown in Table 3, as above-mentioned, MODIS and Landsat are far more concerned by scientists than other EO satellite missions. The data from these two EO satellite missions have provided the remote sensing community with more measures and inspiration to observe the Earth. The high precision of the data will significantly promote the widespread use of the dataset [83]. [PERSON], et al. [84] confirmed that MODIS had high accuracy in retrieving aerosol, which promoted the research of aerosol radiative forcing of global climate. Then, the aerosol products of MODIS were stated to be significant in developing a long-term aerosol climate data record [85; 86]. Land cover is essential to global change science and sustainable development, and some widely use global land cover datasets are derived from the imageries obtained by Landsat and MODIS [87; 88]. In addition, mapping of global forest and surface water cover using EO satellite data provide a significant support for the study of global climate and biodiversity, which affects human well-being [89; 90]. Efficient tools will greatly facilitate the application of EO satellite data with large volume.  
Figure 4: Worldwide distribution of publications referencing EO satellite data.  
RF is an outstanding ensemble classifier for remote sensing applications, and promotes significant advance of EO satellite data analysis [91].
## 4 DT Use Cases and Application in Healthcare
### Personalized Medicine  
Personalized medicine technologies aim to expedite drug development processes and reduce the reliance on test subject animals. The effectiveness of medications varies significantly among patients, with failure rates ranging from 38% to 75% for various conditions, from depression to cancer [58]. This inconsistency stems from the heterogeneous nature of patients who are administered the same or similar drugs based on treatments optimized for the average individual. Efforts are also underway to identify and minimize drug side effects early on [47].  
For example, some patients may have allergic reactions to specific drug components, which could lead to severe health problems. These reactions can be anticipated and mitigated by examining chemical properties in patients' DT before administering potentially allergenic medications. In addition, in cases where allergies are not detectable through blood tests, real-time data from the patient's DT can be used to monitor their response to drugs in a controlled manner, allowing doctors to identify and address complications early on [57]. Digital twin solutions offer a pathway to realize the global vision of personalized medicine [57].  
They also enable research into complex diseases like Multiple Sclerosis and Alzheimer's, improving treatment insights and shortening trial durations. A DT can be customized for each individual's anatomy, physiological traits, and medical history [59]. Additionally, DT technology can simulate novel treatments, allowing for data-driven decisions that enhance patient outcomes, reduce risks, and speed up the delivery of groundbreaking therapies. For instance, researchers at Oklahoma State University developed a DT model of the respiratory system to simulate aerosol drug delivery for enhanced lung cancer treatment [53]. Another study proposed using health digital twins to address cardiovascular diseases [60]. Similarly, Siemens created a heart DT using extensive imaging data to gain a deeper understanding of heart health and predict potential issues. In France, a company developed a DT of aneurysms and nearby blood vessels to help surgeons select the most suitable tools based on DT insights [53].  
Finally, one notable institution focused on personalized medicine is Linkoping University in Sweden, which has made strides by creating a digital twin that maps mouse RNA to forecast medication outcomes [59].
# Modeling and Experimental Study of the Dual Cylinder Fluid Inerter
## 3 Modeling of Mechanical Properties of a Dual-Cylinder Fluid Inerter
### Damping Force Modeling  
Part of the damping force is caused by the friction between cylinder and piston, and the other part is caused by the viscous resistance of oil flowing back-and-forth. When the relative velocities of the two terminals of the dual-cylinder fluid inerter are low, damping and inertia forces are small, and friction dominates. Therefore, in this paper, a 0.01 Hz quasi-static sinusoidal displacement signal is used as the input, and the mean value \(\overline{|F|}\) of the absolute values of the output forces at both terminals is used to approximate the friction force \(F_{f}\) that is to say:  
\[F_{f}=\overline{|F|}\text{sign}\Big{(}\hat{S}\Big{)} \tag{8}\]  
where sign is the sign function, indicating that the direction of friction force \(F_{f}\) is opposite to the relative velocity \(S\). The pressure-loss along the path when the fluid flows through pipelines 4 and 11 is the source of damping force, and the pressure-loss coefficient \(\lambda\)[19] is:  
\[\lambda=\left\{\begin{array}{c}\frac{75}{R_{e}^{2}},R_{e}\leqslant 2320\\ 0.0025R_{e}^{3},2320<R_{e}\leqslant 4000\\ \frac{0.3164}{R_{e}^{0.25}},4000<R_{e}\leqslant 10^{5}\\ 0.0032+\frac{0.221}{R_{e}^{0.25}},10^{5}<R_{e}\leqslant 10^{6}\end{array}\right. \tag{9}\]  
where \(R_{e}\) is the Reynolds number; the calculation formula is:  
\[R_{e}=\frac{\rho vd}{\mu} \tag{10}\]  
where \(\rho\) is fluid density, \(v\) is fluid velocity, \(d\) is flow-channel diameter, and \(\mu\) is fluid dynamic viscosity. The pressure-drop \(\Delta p\)[20] generated by liquid flow damping is:  
\[\Delta p=\frac{1}{2}\rho v^{2}\lambda\frac{L}{d} \tag{11}\]  
where \(L\) is the length of a straight pipe. The damping-loss \(\Delta p_{4}\) along pipeline 4 is:  
\[\Delta p_{4}=p_{2}-p_{6}=\frac{1}{2}\rho_{4}v_{4}^{2}\lambda_{4}\frac{L_{4}}{ d_{4}} \tag{12}\]  
where \(p_{2}\) is the pressure in main chamber 2 and \(p_{6}\) is the pressure in main chamber 6. Similarly, the damping-loss \(\Delta p_{11}\) along pipeline 11 is:  
\[\Delta p_{11}=p_{9}-p_{15}=\frac{1}{2}\rho_{11}v_{11}^{2}\lambda_{11}\frac{L _{11}}{d_{11}} \tag{13}\]  
where \(p_{9}\) is the pressure in annular chamber 9 and \(p_{15}\) is the pressure in annular chamber 15. The damping force \(F_{c}\) is:  
\[F_{c}=A_{2}p_{2}-A_{15}p_{15}+F_{f} \tag{14}\]  
In combination with Formulas (3), (12)-(14), the following can be obtained:  
\[F_{c}=\Delta p_{4}A_{2}+\Delta p_{11}A_{15}+F_{f} \tag{15}\]
## 3 Experiments and Results Analysis  
In this section, two real-world datasets collected from different regions were adopted in this study to verify the effectiveness of the proposed model. The two datasets are described in the following.  
The first dataset is the ISO-NE (New England) public dataset [53], which includes load data, temperatures, as well as day types from 1 March 2003 to 31 December 2014. The dataset was collected every hour, and the data from 1 March 2003 to 8 October 2007 were selected for this study. The second selected dataset is the GEFCom2012 public dataset [54], including power load and temperature. The dataset was collected every hour, and a total of 38,065 sets of data from 1 January 2004 to 29 June 2008 were selected as the study sample. The two datasets were divided into a training set, a validation set and a test set at a ratio of 8:1:1.  
Experiments on the two datasets were performed on a PC with AMD-4800H CPU and NVIDIA GeForce RTX GPU with 16 GB of memory. The software platform used was Python 3.7 based on Pytorch 1.7.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 6 Conclusions  
This study tests the effectiveness of PS in mapping iron oxides in Mirandela, Portugal. The results demonstrate that PS has the same capability to identify Fe\({}^{3+}\) iron oxides as Sentinel-2, ASTER, and Landsat 9; however, its higher spatial resolution allows for more precise identification and analysis of potential points of interest. Despite its spectral limitations, notably the absence of SWIR bands, the proposed methodology, based on spectral analyses and band ratios (BRs), proved robust for mineral discrimination. When discussing potential areas of interest, Sentinel-2 exhibited the highest agreement with PS among the compared sensors. However, the absence of anomalous values indicates that the results remain reliable and consistent across different sensors. As a practical contribution, this study reinforces the potential of PS for mineral exploration, providing a reliable method for identifying areas of interest. Future research will explore the integration of PS with sensors featuring SWIR bands (e.g., Sentinel-2) to mitigate spectral limitations, assess the methodology's applicability across diverse geological contexts, and incorporate advanced techniques such as machine learning to enhance mineral discrimination.  
Conceptualization, D.S. and A.C.T.; methodology, D.S., J.C.-F. and A.C.T.; software, D.S.; validation, D.S., J.C.-F., A.L. and A.C.T.; formal analysis, D.S.; investigation, D.S.; resources, A.C.T.; data curation, D.S.; writing--original draft preparation, D.S.; writing--review and editing, D.S. and J.C.-F.; visualization, D.S.; supervision, A.C.T., A.L. and J.C.-F.; project administration, A.C.T.; funding acquisition, A.C.T. All authors have read and agreed to the published version of the manuscript.  
The work is funded by national funds through FCT--Fundacao para a Ciencia e Tecnologia, I.P., in the framework of the UID/04683: Instituto de Ciencias da Terra (ICT). [PERSON] was financially supported by Portuguese national funds through FCT (Grant: UI/BD/154412/2023).  
Figure 13: Comparison between drone-based high-resolution imagery and fuzzification results. (**a**,**c**) High-resolution aerial images acquired via drone reconnaissance. (**b**,**d**) Fuzzification output highlighting regions with maximum values that indicate sampling locations for soil and rock collection.
# Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
## 2 Related Work
### Sparse Coding Retinex (SCR) Model  
In the SCR model [17], sparse coding is used to search for a suitable basis in the dictionary for the reflectance function and capture more detailed structures or features of the reflectance function. Sparse coding can be used to learn the dictionary, and each image patch can be represented sparsely using a linear combination of the atoms from a specially chosen dictionary. Specifically, the signal \(Y\) is sparse in the sense that \(Y\approx DX\) with the sparse coefficient matrix \(X\), which is represented by the dictionary \(D\). The SCR model expresses the reflectance component as a dictionary of sparse coding to remove illumination.  
\[\begin{split}\min_{r\geq 0l\geq s,D,\{\Gamma_{ij}\}}E^{*}(r,l,D,\{ \Gamma_{ij}\}_{(i,j)\in P})\\ :=\frac{1}{2}||\
abla||_{2}^{2}+\frac{\beta}{2}||l-r-s||_{2}^{2}+ \frac{\tau}{2}\sum_{(i,j)\in P}||R_{ij}r-D\gamma_{ij}||_{2}^{2}+\delta\sum_{( i,j)\in P}||\gamma_{ij}||_{0}\end{split} \tag{3}\]  
here, \(\
abla\) is the gradient operator. \(D\) is a learned dictionary of size \(n^{2}\)-by-\(k\) attached to the restored image, with \(k\) atoms in the dictionary; \(R_{ij}\) is the sampling matrix of size \(n^{2}\)-by-\(N^{2}\) to construct a patch for a part of \(r\); \(\gamma_{ij}\) is a vector of size \(k\)-by-1, containing the encoding coefficients for the patch of \(r\) represented in the dictionary; \(P=\{1,2,\ldots,N-n+1\}^{2}\) denotes the index set for different patches of \(r\); \(||\cdot||_{2}\) denotes the Euclidean norm of a vector; and \(||\cdot||_{0}\) denotes the number of nonzero elements.  
A drawback of dictionary-based sparse coding approaches is that important spatial structures of the signal of interest can be lost because of its subdivision into mutually-independent patches. Further, patches (atoms) of the dictionaries learned with this approach are often redundant and contain shifted versions of the same features. This can be seen in Figure 1b, which shows the sample atoms of a dictionary learned from reflectance images. Moreover, as we show in the red box of Figure 2, owing to the nature of the mathematical formulation (a linear combination of learned patches), these patch-based approaches can fail to adequately represent high-frequency, high-contrast image features, which are particularly important in reflectance images.  
Figure 1: Single image dictionary: (**a**) Training image; (**b**) Sparse coding dictionary in single image [17]; (**c**) CSC dictionary in single image.  
Figure 2: The result of applying the Retinex algorithm from the single image dictionary in Figure 1: (**a**) SCR [17] illumination; (**b**) SCR reflectance; (**c**) Image enhancement through SCR; (**d**) CSC Retinex [18] illumination; (**e**) CSC Retinex reflectance; (**f**) Image enhancement through CSC Retinex.
## 3 Materials and Methods
### Dense Residual Fused Group (DRFG)  
Many studies have demonstrated promising performance in SISR tasks by leveraging dense residual connections. This provides us with a valuable approach. Our approach repeatedly reuses intermediate feature maps across network stages while applying adaptive feature weighting to suppress channel redundancy. This strategy not only expands the effective receptive field but also enhances feature discriminability through hierarchical refinement.  
Each DRFG integrates four MLLA modules and one GAB. The MLLA modules dynamically refine the model's focus through adaptive feature selection, enabling multi-level spatial information extraction via iterative feature reuse. This process progressively expands and enhances the effective receptive field while capturing rich contextual dependencies. Subsequently, the GAB aggregates these multi-scale features while preserving critical high-frequency details. These two modules will be described in detail in subsequent sections. By integrating MLLA, GAB, and dense residual connections, our framework dynamically adapts its focus regions based on global contextual inputs. This enables cross-scale information integration, long-range dependency capture, and the adaptive fusion of global-local features, achieving comprehensive feature extraction essential for high-fidelity super-resolution. For an input feature map \(M\), the computation process of each DRFG is as follows:  
\[M_{j}=F_{MLLA}\big{(}F_{FWB}\big{(}\big{[}F_{MLLA}(M),\ldots,M_{j-1}\big{]} \big{)}\big{)},j=1,2,3,4, \tag{6}\]  
\[F_{output}=F_{GAB}(M_{4})+M, \tag{7}\]  
where \([\cdot]\) denotes the concatenation of multi-level features from preceding layers. \(F_{MLLA}(\cdot)\) and \(F_{GAB}(\cdot)\) denotes each MLLA layer and \(F_{GAB}(\cdot)\) GAB layer, respectively. Here, \(M\) denotes the input feature map to the DRFG block, which is passed via residual connection to later stages (e.g., GAB) to enhance feature continuity and gradient propagation. It is not a tunable parameter, but a reused intermediate representation. \(F_{FWB}(\cdot)\) represents a Feature Weighted Block designed to suppress redundant information that accumulates during dense concatenation. It consists of two parallel \(1\times 1\) convolutional layers, where one branch is followed by a sigmoid activation. Their outputs are fused via element-wise multiplication to produce channel-wise adaptive weights. This effectively learns a relevance score for each channel based on its contribution to the global context. Channels with lower activation are likely to carry redundant or low-utility information and are therefore attenuated by the learned gating weights. This process allows the network to maintain compact and discriminative feature representations throughout the DRFG module.
## 2 Theoretical Background of the Uncertainty Theory
### Uncertainty Theory: Measure, Variable, and Distribution
#### 2.1.3 Uncertainty Distribution  
The uncertainty distribution is used to model the likelihood of various outcomes in SCM, such as demand rates or delivery times. This classification supports risk assessment and decision-making processes, enabling better preparedness for different scenarios. Recent developments include the use of uncertainty classification in predictive analytics to develop demand forecasts and risk management strategies to improve supply chain resilience [18; 19]. Defining the 'uncertain variable' \(\xi\), which has an uncertainty distribution \(\Phi(x)\) given by \(\Phi(x)=\mathcal{M}(\xi\leq x)\), the distribution measures the event where \(\xi\) is less than or equal to a value \(x\). For regular uncertainty distributions, the values mainly range between 0 and 1 and are continuous and strictly increasing. Bias is absent in the linear asymmetry of \(\xi\), and it can be expressed as follows:  
\[\Phi(x)=\begin{cases}0,&\text{if }x<a\\ \frac{x-a}{b-a},&\text{if }a\leq x\leq b\\ 1,&\text{if }x>b\end{cases}\]  
where \(a\) and \(b\) are constants with \(a<b\).
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 6 Polarization Splitting Grating Couplers (PSGC)
The common focal points of this confocal family of ellipses lie along the co-ordinates defining the corresponding waveguide entrances. In this configuration, combined power coupled into the two waveguide arms is independent of the input polarization. An important performance metric of PSGCs is the polarization dependent loss (PDL) [55]. Perfectly vertical coupling ensures total decoupling of input orthogonal polarizations, although at the cost of enhanced second order back reflection. However, PDL is always expected for near vertical coupling due to orthogonal polarizations not experiencing the same grating. PDL can be reduced by inducing \(\pi\) phase shift in one arm of the polarization splitter [56]. This would bring in-phase modes that are excited in the two orthogonal arms of the 2D splitter, which were initially out of phase due to the fiber tilt of one of the polarization states. Another way to reduce PDL as suggested in [54] is by using more complex diamond shaped scatterers for gratings. Table 3 summarizes the various kinds of SOI based experimentally demonstrated PSGCs.
# Charging Schedule for Load Peak Minimization on Large-Scale Electric Bus Depots
## Bus Depot Alsterdorf
### Route Assigining  
Bus depot Alsterdorf currently does not have any assigned routes as it is still in the construction phase. For the simulation purposes, the data from a bus depot nearby (currently running with diesel buses) were gathered and used. This results in a scenario with 127 buses and an average of 230 daily trips/routes. Buses are assigned to routes based on the first-in-first-out scheduling concept. While assigning the buses additional conditions have to be fulfilled:  
1. Bus cannot take a route if the expected state-of-charge (SoC) upon its return to the depot will be smaller than 20%.
2. Bus needs to match the bus type necessary for the trip (standard, articulated, and double articulated).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bus Type** & \(-15\,^{\circ}\)**C** & \(+20\,^{\circ}\)**C** & \(+28\,^{\circ}\)**C** & **Unit** \\ \hline Standard & 2.11 & 1.40 & 1.60 & kWh/km \\ Articulated & 3.25 & 2.05 & 2.40 & kWh/km \\ Double articulated & 4.50 & 2.68 & 3.17 & kWh/km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy consumption of electric buses in dependence on the ambient temperature.  
Figure 1: Electrical infrastructure on the Bus Depot Alsterdorf with the detailed representation of the Carport No. 6.  
A driving schedule was assembled based on the timetable of the neighboring diesel depot and the conditions stated above. Table 2 shows an example of such schedule for three random buses. The expected SoC values before and after the trip are calculated for orientation purposes.