esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
## 6 Positioning Experiments
### Pedestrian Localization
#### 6.2.2 Experimental Results  
Figure 13a,b shows the results of pedestrian localization in the case of using the initial values of correct position and orientation and the case of incorrect values, respectively. As can be seen in the figures, the performance of the dead reckoning is lower than the case of the robot experiment; because of this, the path drifts quickly toward the bottom of the figures. In contrast, the path of the Kalman filtering remains near the experimental area by utilizing the IMES radio information. However, in both cases of Kalman filtering and trilateration, the positioning accuracy was far lower than that of the robot experiment.  
Figures 14 and 15 respectively show the time series data of the position estimation errors for both the trilateration and the EKF and their histograms in the case of using incorrect initial values. As can be seen in Figure 14, the estimation error of the EKF follows the trend of the trilateration, although the EKF's variance is smaller. Figure 15 also shows that there is not a large difference between the average estimation error of the EKF and that of trilateration, compared to the case of the robot experiment,  
Figure 13: Experimental result of pedestrian position estimation. (**a**) Correct initial position and orientation; (**b**) Incorrect initial position and orientation.  
As is the case of the experiment with the robot, Figure 15(a) shows the sequence of innovation of transmitter #175, and Figure 15(b) shows the time series of the error covariance and the actual position estimation error. Similar to the experimental results with the robot, the innovation sequence is not a white noise, and the estimation errors are far larger than the covariance values (rather, the estimation error is diverging).
# Efficient Method for Enhancing Reverse-Time Migration Images Using Vertical Seismic Profiling Data
## 4 Results
### Modified Marmousi Model  
This section employs the Marmousi model to demonstrate the imaging performance of CRRTM in complex scenarios, along with its distinctions from the traditional W-VSP RTM.  
Figure 17 illustrates the model utilized, which was composed of \(1240\times 2040\) grid points with vertical and horizontal intervals of 2.5 m between grids. The model was surrounded by PML layers comprising 60 grid points on each side. In addition, to mitigate unnecessary strong reflections, minor smoothing of the model was necessary. In this study, a Gaussian filter with sigma = 0.5 was used to smooth the model.  
The VSP observation system utilized is described as follows: 480 seismic sources were employed, evenly spaced at 10 m intervals on the surface. The observation well was positioned at x = 2500 m, with 280 numerical receivers distributed along it, spaced at 10 m intervals. VSP seismic data were generated using a Ricker wavelet with a frequency of 20 Hz, with a maximum recording time of 3 s, and a time step interval of 0.25 ms between each time step.  
Figure 18 shows the common-receiver point-gathers used in the experiment.  
Figure 19 illustrates the final imaging results obtained using CRRTM. This serves as evidence of the applicability of this method to complex models.  
Figure 17: True P-wave velocity of the modified Marmousi model.  
Figure 18: Three receiver gathers of various receivers for the Marmousi model, with receivers located at depths of (**a**) 150 m, (**b**) 800 m, and (**c**) 1500 m. Only the separated upgoing wave has been retained.
## 3 Results and Discussion
### Bacterial Community  
The composition of the anaerobic bacterial community over time was not significantly affected by the SCO\({}_{2}\) pre-treatment. However, the abundance of methane-producing Archaea was indeed affected by the highest OLRs used in V3 and V4. With OLR at 2 to 3 gVS/dm\({}^{3}\cdot\)day, the share of Archaea exceeded 24% in both stages (Figure 8). When the OLR range was 4 to 5 gVS/dm\({}^{3}\cdot\)day, the proportion fell to 19-23% in S1 and 19-21% in S2 (Figure 8). Similar trends were found for _Methanosarcinaceae_ (MSMX860). On the other hand, no relationship was found between the variant used and the abundance of Bacteria (EUB338) and _Methanoseta_ (MX825). The share of Bacteria in the community ranged from  
Figure 7: Correlations between pH and the yields of biogas (**a**) and methane (**b**); as well as between FOS/TAC and the yields of biogas (**c**) and methane (**d**).  
Similarly, feedstock pre-treatment prior to AD was found to have no effect on the anaerobic bacterial community in an experiment by [PERSON] et al. (2015) [58]. As in the present study, the relative abundance of Archaea decreased as the OLR increased, regardless of whether raw or sonicated MSS was used. The application of Archaea-specific FISH probes showed the presence of long filamentous rods of the _Methanosaeta_ spp. The latter was found in all digestion phases, highlighting the occurrence of acetonitrile methanogenesis, as expected in a mesophilic anaerobic system [58]. The present study results corroborate earlier findings reported by [PERSON] et al. [59], who noted that an OLR increase from 1.0 to 6.0 kgCOD/m\({}^{3}\).d during AD led to decreased abundance of Archaea [59]. Yet, another study [60] similarly reported that the anaerobic bacteria community was directly affected by rising OLRs, reduced HKT, and the resultant changes in reactor conditions.
## 3 Experimental Study
### Sensitivity to Different Looks of Speckle Noise  
The major difficulty of SAR image segmentation is the large amounts of unpredictable and uncertain speckle noise in this kind of image. To evaluate the robustness of the proposed algorithm under the effect of noise, the synthetic SAR image is contaminated by different number of looks of simulated speckle noise. In general, the larger number of looks is and the less amount of speckle noise is. The segmenting results of the synthetic SAR image with 1-look to 10-look results by five algorithms are shown in Figure 5. The curves show that the mean segmentation accuracies of five algorithms ascend in a steady style with different number of looks. Besides, the performances of KGC and MKCIS are nearly the same when the number of look is more than eight. Besides, it is obvious that all the mean values of partitioning accuracy of MKCIS are greater than 90%.
## 7 Summary  
Articular cartilage is a specialised connective tissue that covers the articular surfaces of bones in synovial joints. It is composed mainly of chondrocytes located in the ECM, type II collagen, proteoglycans, and water. Due to the lack of vascularisation, innervation, and lymphatic vessels, the process of chondrocyte nutrition occurs through fusion with synovial fluid, which significantly limits the ability for self-repair [376].  
The function of AC is to absorb and distribute mechanical loads and, above all, to ensure joint movement with minimised friction. The organisation of collagen fibres and the content of proteoglycans in the ECM affect the biomechanical properties of AC, more specifically its elasticity and shear resistance [377].  
Degeneration of articular cartilage results in a loss of both structural and functional integrity. Degradation of ECM leads to a loss of proteoglycans and type II collagen. As a result, there is a decrease in AC thickness, crack formation, and exposure of subchondral bone [378]. In the course of OA, the AC structure degrades, which significantly changes its mechanical properties, such as a decrease in the modulus of elasticity, an increase in the coefficient of friction, or a loss of structural integrity. As a result, the cushioning and protective functions of the joint cartilage deteriorate.  
Traditional diagnostic methods, such as radiological imaging and magnetic resonance imaging, are used to assess advanced structural changes in cartilage. However, the early stages of degeneration are often not visible using these techniques. Therefore, alternative methods are gaining increasing importance, especially the analysis of vibroacoustic signals generated during joint movement. Changes in the mechanical properties of cartilage can affect the characteristics of acoustic signals, due to which this method can be used for the early detection of degeneration.  
Therapies for damaged AC include symptomatic, reparative, and regenerative treatment. Symptomatic treatment is focused on relieving pain and anti-inflammatory medications, as well as intra-articular injections [379]. The aim of repair methods is to stimulate AC regeneration through microfractures or osteochondral grafts [380,381].  
The mechanical properties of AC and their changes are important in the pathogenesis and diagnosis of joint diseases. The combination of classical and alternative diagnostic methods may result in earlier diagnosis of pathological changes and more precise monitoring of the effectiveness of the applied therapies. However, further research is needed in this area to enable the development of effective diagnostic and therapeutic strategies in the treatment of degenerative joint diseases.
# A Cross-Modal Hash Retrieval Method with Fused Triples
## 2 Related Work  
The early hash algorithms mainly performed semantic similarity retrieval on single-modal data. With the explosion of multimodal data in recent years, semantic similarity retrieval between multimodal data has received much attention. Researchers in this field have proposed many multimodal hashing methods to satisfy semantic similarity retrieval in large-scale data. The existing multimodal methods include multi-source hashing [6] and cross-modal hashing [1; 7; 8]. Multi-source hashing methods are severely limited in their application due to the difficulty of focusing on cross-modal global information. In contrast, cross-modal hashing methods generally require information from only one modality to retrieve semantically similar information from other modalities, so cross-modal hashing methods are more flexible and of greater interest than multi-source hashing methods. Two representative cross-modal hashing methods are unsupervised and supervised cross-modal hashing methods [2; 15; 16; 17].  
The unsupervised cross-modal hashing method involves direct feature extraction without labeling information while learning the binary hash code, intending to discover correlations between different modalities. Standard unsupervised cross-modal hashing methods include the CCA-ITQ approach [18] and the CMFH [19]. CCA-ITQ is a multivariate statistical method for measuring the similarity between two sets of variables. CMFH is a hash function for learning across views using collective matrix factorization. Unsupervised cross-modal hashing methods usually do not perform optimally due to their lack of label information and the effect of manual annotation.  
Supervised cross-modal hashing methods are of greater interest as they include manual annotations. The hash function can be learned in conjunction with semantic tagging information during feature extraction, thereby reducing semantic differences between modalities to improve the performance of cross-modal retrieval [20]. Typical supervised cross-modal hashing methods include the discrete latent factor hashing (DLFH) cross-modal method [2], the semantic correlation maximization (SCM) method [7], the deep cross-modal hashing (DCMH) method [15], the generalized semantic preserving hashing for n-label cross-modal retrieval [21], the multimodal latent binary embedding (MLBE) method [22], the semantic preservation hashing (SePH) method [23], the cross-view hashing (CVH) method [24], the deep multi-semantic fusion-based cross-modal hashing (DMSFH) method [25], the deep visual-semantic hashing (DVSH) method [26] and the triplet-based deep hashing (TDH) method [27].
## 2 Theory
### Modulation of Surfactant Concentration Due to Internal Waves  
The surfactant concentration \(\widetilde{\Gamma}\equiv\Gamma(x,t)\) in the presence of a varying surface current \(U(x,t)\) can be described using the following balance equation:  
\[\frac{\partial\widetilde{\Gamma}}{\partial t}+\frac{\partial}{\partial x} \Big{[}U(x,t)\widetilde{\Gamma}\Big{]}=R \tag{1}\]  
The right-hand side, \(R\), of Equation (1) describes processes of surfactant diffusion, weathering, vertical mixing due to wave breaking and turbulence, etc., but here we neglect these processes. Assuming that \(R=0\), and \(U(x,t)=U(x-Ct)+V\), a steady-state solution to Equation (1) can be written as follows:  
\[\widetilde{\Gamma}=\frac{\Gamma_{0}}{1-\frac{U(\xi)}{C-V}} \tag{2}\]  
where \(V\) is a uniform current on the water surface, \(U(\xi)=U(x-Ct)\) denotes a non-uniform current, e.g., a surface component of the IW orbital velocity, propagating with a phase velocity \(C\) in the positive direction of the x-axis, and \(\Gamma_{0}\) is an undisturbed surfactant concentration, where \(U(\xi)=0\). If \(V<<C\), the concentration \(\widetilde{\Gamma}\approx\Gamma_{0}\Big{[}1+\frac{U(\xi)}{C-V}\Big{]}\), so that the concentration achieves its maximum values over the maxima of \(U(\xi)\) (see Figure 1), which is over the troughs of pycnocline elevations for the IW basic mode. If \(V\) is close to \(C\) (a resonance condition), the concentration is small everywhere except for the vicinity of convergent points \(\xi*\), where \(U(\xi*)=|C-V|\) and \(dU(\xi*)/dx<0\)). A steady-state solution to Equation (1) at resonance does not exist, and \(\widetilde{\Gamma}\) grows constantly with time at the convergent points and can be limited to take into account relaxation/diffusion processes, for example. In the case of a resonance between a moving film and a sinusoidal IW \(U(x-Ct)=U_{0}\sin(\kappa_{\xi}^{c})\), where \(U_{0}\) and \(\kappa\) are the IW amplitude and wave number, respectively, the surfactants will be strongly accumulated over IW rear slopes.  
Figure 1: Surfactant concentration over a sinusoidal IW profile (black curve). Numbers near curves denote the values of \(U_{0}/(C-V)\). The arrow indicates the IW propagation direction.
# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 2 Materials and Methods  
In this work, three PV power plants were selected that met the image requirements of no light distortions, non-complex geometry, and grouped panels. These plants are in different parts of the world. The first PV plant has an area of \(35,975\) square meters, located in Brazil (\(-22.119948323621525\), \(-51.44666423682603\)), known as Usina Solar Unioeste 1 [75]. The second PV plant has an area of \(25,056\) square meters, located in Iran (\(34.0504329771808\), \(49.796635265177294\)), known as Arak power plant [37]. The third plant is in the United States (\(38.55989816199527\), \(-121.42374978032115\)); the plant has \(1344\) square meters of area, and it is part of a PV plant located on the roof of the California State University Sacramento Library (CSUSL) [76].  
These plants were subjected to a series of processing stages, as shown in Figure 1. The RoIs were obtained from Google Maps satellite images with a predefined altitude, and limits, from which the image (input image) of the desired PV system was obtained as the first stage, shown in Figure 1. In the second stage, the image was entered into a DL server that was developed in this work, taking into account the previous work [65]. The DL server was launched with TensorFlow [77] and Flask [78]. This stage obtains a mask (image segmentation) of the PV plant, as shown in Figure 1. In the third stage it was necessary to apply a series of OpenCV functions (post-processing), as referenced in Algorithm 1 in the Appendix A, to adjust the mask (output mask) to the PV plant area, as seen in Figure 1. Then, the output mask or RoI was introduced in the GUI interface, where the CPP method was selected to internally execute. Later, the path GPS positions (CPP computed) were sent to the UAV through MAVlink commands [79; 80]. The UAV executed these commands in the Gazebo platform (CPP simulation) and, at the same time, the simulation data was fed back the GUI interface. Simultaneously all GPS points reached by the UAV were drawn in the GCS platform (CPP). In this stage, the trajectory was validated. Each stage is described in greater detail in the following sections.
## 5 Security Analysis
### Security of the Voting Scheme  
For this algorithm, publishers cheating and collusion attacks are two key security issues. Firstly, we solve publisher cheating by setting public supervision of the successful revocation results. The results information will be announced in a publish-subscribe mode to ensure that publishers will not falsify the counting results. Secondly, we also analyze and limit the threshold \(th_{cs}\) to avoid collusion attack threat in Section 4.4. Meanwhile, the private key signature ensures the unforgeability of user identity. Therefore, the robustness of the voting scheme is proved.
# Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems
###### Abstract  
Digitalization and concepts such as digital twins (DT) are expected to have huge potential to improve efficiency in industry, in particular, in the energy sector. Although the number and maturity of DT concepts is increasing, there is still no standardized framework available for the implementation of DTs for industrial energy systems (IES). On the one hand, most proposals focus on the conceptual side of components and leave most implementation details unaddressed. Specific implementations, on the other hand, rarely follow recognized reference architectures and standards. Furthermore, most related work on DTs is done in manufacturing, which differs from DTs in energy systems in various aspects, regarding, for example, multiple time-scales, strong nonlinearities and uncertainties. In the present work, we identify the most important requirements for DTs of IES. We propose a DT platform based on the five-dimensional DT modeling concept with a low level of abstraction that is tailored to the identified requirements. We address current technical implementation barriers and provide practical solutions for them. Our work should pave the way to standardized DT platforms and the efficient encapsulation of DT service engineering by domain experts. Thus, DTs could be easy to implement in various IES-related use cases, host any desired models and services, and help get the most out of the individual applications. This ultimately helps bridge the interdisciplinary gap between the latest research on DTs in the domain of computer science and industrial automation and the actual implementation and value creation in the traditional energy sector.  
industrial energy systems; integrated energy systems; digital twin platform; digital twin requirements; service engineering; service-oriented architecture +
Footnote †: journal: Applied Sciences  
_Article_  
Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems  
[PERSON]  
1 Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/BA/E302,  
A-1060 Vienna, Austria, [EMAIL_ADDRESS] (F.B.); [EMAIL_ADDRESS] (P.S.); [EMAIL_ADDRESS] (P.S.); [EMAIL_ADDRESS] (P.R.H.)
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 4 Implementation Details
### Quantitative Experimental Results
#### 4.3.2 Quantitative Results Obtained on the NWPU-RESISC45 Dataset  
To further demonstrate the efficiency and adaptability of the proposed LSANet in remote sensing image super resolution, extensive experiments were conducted on the NWPU-RESISC45 dataset. As shown in Table 2, LSANet consistently achieved the highest PSNR values across all upscaling factors while maintaining a relatively lightweight model size and low computational complexity. Notably, under the \(\times\)2 upscaling factor, LSANet only requires 22.1G Multi-Adds and 602K parameters, yet outperforms all baseline methods, including IMDN, RFDN, and CTN, in terms of both PSNR and SSIM. Figure 5 presents visual comparisons for three representative scenes. Compared with other networks, LSANet generates images with finer textures, sharper edge structures, and clearer boundaries of  
Figure 4: SR reconstruction validation results produced using different methods on the UC Merced Land Use dataset.  
roads and objects. This advantage is particularly evident in challenging regions such as basketball court lines and bridge markings. These results highlight LSANet's ability to recover detailed spatial structures with minimal artifacts.
## 3 Results
### Effect of TTK on Migration of HUVECs  
Effect of TTK on endothelial cell migration was determined. In the control, the number of migrated cells was 1 \(\pm\) 0.82 cells/field, and TTK induced cell migration in a dose-dependent manner (Figure 2A). TTK at 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL elevated cell migration to 106.33 \(\pm\) 4.19 cells/field, which was 106-fold higher compared with that of the control (Figure 2B). In the positive control, 50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of VEGF and 1 \(\mathrm{nM}\) 17\(\mathrm{\SIUnitSymbolMicro g}\)-estradiol induced cell migration to 122.33 \(\pm\) 4.92 and 105.33 \(\pm\) 4.11 cells/field, respectively (Figure 2B). In the case of simvastatin treatment for peripheral vascular disease, HUVEC migration showed a 58-fold increase compared with the control, whereas TTK treatment induced 1.8-fold higher HUVEC migration compared to simvastatin (Figure 2B).  
Figure 1: Effect of TTK on proliferation of HUVECs. Cells were plated to the 96-well plate and treated at the marked TTK concentration for 24 \(\mathrm{\SIUnitSymbolMicro h}\). Cell growth rates were evaluated using WST assay. The experiments were repeated four times. The results are presented as the means \(\pm\) SEM. a \(p\)\(<\) 0.001 vs. 0 (untreated cells).
# Optical Flow Estimation with Occlusion Detection
## 3 Occlusion Detection Strategy
### Matching-Based Strategy  
Optical flow estimation can be considered as a matching problem. Comparing two frames of images, occlusion can be determined if a pixel does not appear in the second frame. We can use either color features or more complex features for matching. In order to balance computation cost and computational accuracy, we do not match point-by-point, but use grid point sampling algorithm. To be specific, we first generate grids with a fixed size of 5 in both frames. For each sampling point, we build two descriptors \(H\) and \(C\) and use these descriptors for matching. \(H\) consists of 9 orientation histograms with 12 bins and \(C\) comprises the mean RGB color of 9 subparts. Correspondences between points are obtained by approximate \(K\)-nearest neighbor matching, which \(K\) is 2 in our strategy. We compute the Euclidean distances of both descriptors separately and normalize them by the sum over all distances.  
\[d^{2}(H_{m},H_{n})=\frac{\left\|H_{m}-H_{n}\right\|_{2}^{2}}{\frac{1}{5}\sum_ {i,j}\left\|H_{i}-H_{j}\right\|_{2}^{2}} \tag{1}\]  
\[d^{2}(C_{m},C_{n})=\frac{\left\|C_{m}-C_{n}\right\|_{2}^{2}}{\frac{1}{5}\sum_ {i,j}\left\|C_{i}-C_{j}\right\|_{2}^{2}} \tag{2}\]  
\[d^{2}(m,n)=\frac{1}{2}(d^{2}(H_{m},H_{n})+d^{2}(C_{m},C_{n})) \tag{3}\]  
where \(m\) is the grid points in the first frame and \(n\) is the matched point in the second frame, \(S\) is the total number of combinations \(m\), \(n\). Due to the normalization, \(H\) and \(C\) share the same weight in the final distance. The next step is consistency check, which is helpful for removing outliers. Since the matching results are sparse, we need sparse-to-dense interpolation to obtain the initial occlusion detection \(M_{m}\), and the overall flow of our matching-based strategy is shown in Figure 2.  
We also experimented with the KD-tree-based matching algorithm proposed by [PERSON] et al. [34] and the Siamese neural network based matching methods [35]. The former algorithm achieves similar results of the above one, but its computational complexity is greater. Perhaps due to the lack of sufficient training data, the CNN-based algorithm has not achieved satisfactory results.