esa-sceva/satcom-chunk-collection huggingface dataset:
# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
## 2 Related Work
### Methods Based on Geometry  
VO methods based on geometry rely on extracting geometric constraints from images to estimate motion. These methods can be further categorized into two types: feature-based methods and direct methods. In feature-based methods such as MonoSLAM [20], which is a typical filter-based approach [20; 21], the state vector size can increase substantially with a rapid increase in feature points, making real-time calculations challenging. PTAM [22] improved based on MonoSLAM by splitting tracking and mapping into different threads, resulting in real-time camera motion tracking. ORB-SLAM [3] inherited the framework of PTAM while achieving real-time performance on CPU. Later, ORB-SLAM2 extended ORB-SLAM to support stereo and RGB-D cameras, while ORB-SLAM3 [23] added support for fisheye cameras and visual-inertial SLAM.  
In contrast to feature-based methods, direct methods do not rely on artificially designed sparse features but instead build an optimization problem that estimates camera motion directly from pixel information, typically photometric errors. DTAM [24] calculates a dense depth map for each key-frame by minimizing global spatially regular energy functionals. The camera's position is determined by aligning the entire image with the depth map directly. This method is computationally intensive. To reduce the amount of calculation involved, SVO [25] only extracts features when a key-frame is selected to initialize new 3D points. DSO [26] combines photometric and geometric errors and jointly optimizes all model parameters, and the method demonstrates robust performance in certain featureless environments.  
However, all these geometry-based methods are complex, and fine adjustment is required for each module to achieve good performance. Additionally, current frameworks are basically fixed, and algorithms are approaching their limits.
## 2 Materials and Methods
### Governing Equations for the Simulation  
A finite element analysis method was employed in the modelling and analysis of the TEG in Comsol-Multiphysics. The governing equations are as follows [46]:  
Heat transfer in solids: Energy balance  
\[\rho\text{C}_{p}\mathbf{u}\cdot\
abla\text{T}+\
abla\cdot q=Q+Q_{tel} \tag{10}\]  
Figure 2: Simplified segmented unicouple thermoelectric generator (TEG) for V\({}_{\text{oc}}\) measurement.  
[PERSON]'s law  
\[\mathbf{q}=-k\
abla T\] (11) Thermal insulation \[-\mathbf{n}\cdot\mathbf{q}=0\] (12) Electric current: Current conservation \[\
abla\cdot\mathbf{J}=Q_{j,\rho}\] (13) [PERSON]'s law \[\mathbf{J}=\sigma\mathbf{E}+\mathbf{J_{e}} \tag{14}\] \[\mathbf{E}=-\
abla\ V\] (15) Joule heating (irreversible process) \[Q=J\cdot E\] (16) Electric insulation \[n\cdot J=0\] (17) Thermoelectric effects: \[\mathbf{q}=P\mathbf{J}\] (18) \[P=ST\] (19) \[\mathbf{J_{e}}=-\sigma S\
abla T\] (20) Electromagnetic heat source: \[\rho C_{p}\mathbf{n}\cdot\
abla T=\
abla\cdot(k\,\
abla T)+Q_{e}\] (21) \[Q_{e}=\mathbf{J}\cdot\mathbf{E} \tag{22}\]  
\(\rho=\) Density, \(C_{p}=\) Specific heat, \(Q=\) Heat source, \(J=\) Induced electric current, \(Q_{total}=\) Thermoelectric effect, \(E=\) Electric field, \(V=\) Electric potential, \(S=\) Seebeck coefficient, \(P=\) Peltier coefficient, \(\sigma=\) Electrical conductivity, \(Q_{j}=\) Current source, \(q=\) Heat flux in conduction, \(k=\) Thermal conductivity, \(T=\) Temperature, and \(J_{e}=\) External current source.
# Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures
###### Abstract  
Asphalt recycling technologies have advanced considerably over the last few decades with the utilization of reclaimed asphalt pavements (RAP) and recycled asphalt shingles (RAS). Characterizing aged and heterogeneous binders in these mixtures is challenging, particularly with limited extracted binders. This study suggests a data-driven framework that considers the rheological, chemical, and thermal characteristics to predict the binders' performance. Ninety-seven mixtures with 0-35% of the asphalt binder replaced with RAP/RAS binders were included as cores from the field, plant-produced mixtures, and laboratory-fabricated mixtures. The binders were chemically quantified using aging, aromatic, and aliphatic indices. Thermal analyses of the binders involved the percentage of the thermal residue. The framework predicted the rheological resistance of the binders to putting and cracking using linear and nonlinear machine learning models. The nonlinear models outperformed the linear models for the three rheological parameters. The nonlinear models achieved a 69% reduction in the root mean square error (RMSE) for rutting, a 37% reduction in the RMSE for fatigue cracking, and a 21% reduction in the RMSE for thermal cracking. However, the nonlinear models overfitted for block cracking and had an RMSE 41% higher than the linear models, despite a perfect correlation (R = 1.00). The feature importance demonstrated the strong effects of the chemical and thermal parameters on rheological prediction. The data-driven framework can successfully support efforts to better manage asphalt recycling by predicting the binder performance.  
RAP; RAS; machine learning; FTIR spectroscopy; TGA; thermal residue; asphaltene; aging indices; aromatic; aliphatic 2025
# Rapid Restoration Techniques for Software-Defined Networks
###### Abstract  
There is increasing demand in modern day business applications for communication networks to be robust and reliable due to the complexity and critical nature of such applications. As such, data delivery is expected to be reliable and secure even in the harshest of environments. Software-Defined Networking (SDN) is gaining traction as a promising approach for designing network architectures which are robust and flexible. One reason for this is that separating the data plane from the control plane, increases the controller's ability to configure the network rapidly. When network failure events occur, the network manager may trade-off the optimality of the achieved network reconfiguration with the responsiveness of the reconfiguration process. Responsiveness may be favoured when the network resources are under stress and the failure rate is high. We contribute SDN recovery methods that leverage information about the structure of the network to expedite network restoration when a link failure occurs. They operate by detecting community-like structures in the network topology and then they find alternative paths which have low operation and installation costs using this information. Extensive simulations are conducted to evaluate the proposed SDN recovery methods using open-source simulation tools. They provide evidence that the proposed approaches lead to performance gains when an alternative path is required among a set of candidate paths.  
software-defined networking; openflow; failure restoration; failure recovery; fault management; link failure Article
# Mapping with Pleiades--End-to-End Workflow
## 3 Test Sites and Data Sets
### Test Site Trento (Italy)  
This test site covers rural as well as mountainous terrain, the ellipsoidal heights ranging from 175 m to 1550 m and spans over an area of 220 km\({}^{2}\). A Pleiades-1A triplet was acquired in September 2012, which however is far from optimal for 3D reconstruction, since the first two images have a very small intersection angle of only 5.5\({}^{\circ}\), while the second stereo pair has a huge intersection angle of 27.3\({}^{\circ}\). The test site is shown in Figure 4. Also for Trento an airborne LiDAR DSM is available as reference data set, collected in 2007 at 1.3 points/m\({}^{2}\), available as raster data in UTM 32 North map projection and WGS 84 datum at 1 m GSD with ellipsoidal heights. Again there is a huge temporal gap between Pleiades and LiDAR acquisition to be taken into consideration. 21 GCPs were measured from LiDAR and serve as reference points.
# Enhancing Imbalanced Sentiment Analysis: A GPT-3-Based Sentence-by-Sentence Generation Approach
## 3 Proposed Approach  
Our research paper proposes an innovative approach to addressing the class imbalance in sentiment analysis by generating synthetic data using the GPT-3 model, implemented through a sentence-by-sentence generation technique. This method aims to balance the underrepresented classes, specifically the negative and neutral sentiments, to match the level of the majority class, which is the positive sentiment. This balancing process is exclusively applied to the training data for sentiment classification.  
As illustrated in Figure 1, our approach begins with the original dataset, specifically a Coursera review dataset, which comprises positive, negative, and neutral sentiments. This dataset is highly unbalanced. Our focus is primarily on the negative and neutral data, which are prepared as inputs for synthetic data generation. To this end, we first perform preprocessing on these specific sentiment classes.  
Following preprocessing, we initiate the generation process using the GPT-3 model, employing a sentence-by-sentence generation technique. This implies that each original data input included in the prompt results in one or more output generated sentences.  
Once the synthetic data are generated, they undergo an evaluation phase to ensure their quality. Only the synthetic data deemed to be of good quality are selected for the next step. These good-quality synthetic data are then utilized to balance the minor classes in the original dataset, namely, the negative and neutral classes. In other words, we integrate these good-quality synthetic data into the original dataset, preparing them as the training data for the subsequent process of sentiment classification.  
Our proposed approach, therefore, not only addresses the imbalance in sentiment classes but also ensures that the quality of the synthetic data is suitable for effective training, aiming to improve the overall performance of sentiment classification models.  
Figure 1: Detailed schematic of the proposed approach for sentiment classification.
## Case Study
### Discussion on Practical Implementations  
#### 5.3.1 The Number of Populations and the Maximum Number of Iterations in PSO  
Set the population size of the PSO to 10, 20, 30, 40, and 60, respectively. For each population size, the simulation is carried out 100 times. The average values of the fitness results under different iterations are given in Table 5.  
Table 5 shows that the larger population size and the higher maximum number of iterations can minimize the fitness at the maximum number of iterations, that is, the minimum total control cost. However, increasing the population size and the number of iterations will increase the optimization solution time. In engineering practice, the population size and the maximum number of iterations should not be too small or too large. The population size can be set to 40 or more, and the maximum number of iterations can be set to 20 or more.
# A Novel Ring Spring Vibration Isolator for Metro Superstructure
## 2 Theoretical Derivation and Numerical Simulation of Ring Spring
### Numerical Calculation Model  
The finite element software ABAQUS was used to simulate the ring spring. It is more efficient to calculate the result by establishing only a quarter of the whole structure since the geometry, the boundary constraints, and the load of the ring spring are all axisymmetric structures [17; 18; 19]. The three-dimensional model is shown in Figure 3. As we can see, the spring and upper and lower connecting plates are all established. The spring compression is achieved by slowly controlling the displacement of the upper connecting plate. The ring spring includes six outer rings, five inner rings, and two half inner rings. The specific dimensions are shown in Figure 4.  
The material of the ring spring is 60 SiMnA spring steel, of which the elastic modulus is \(2.06\times 10^{5}\) N/mm\({}^{2}\). The Poisson's ratio is 0.3, and the density is 7.85 g/cm\({}^{3}\). The ideal elastic-plastic model is adopted for the material relationship of the ring spring, and the yield stress is 1375 Mpa [20]. The ring spring is composed of C3D8I units, which introduces an additional degree of freedom to enhance the deformation gradient of the unit in the first-order unit. The additional degree of freedom enables the first-order element to produce a linear change in the element domain to overcome the problem of shear self-locking, and it only requires a few elements in the thickness direction, therefore saving on cost. When the element distortion is small, the results of displacement and stress are accurate. In summary, the C3D8I unit can effectively simulate the deformation and stress characteristics of the ring spring. The design of the connecting plate should ensure sufficient rigidity and strength to guarantee the correct transmission of the load. Therefore, the deformation of the upper and lower plates is ignored in the finite element simulation. The elastic modulus of the upper and lower plates is set to be much greater than the spring material during the analysis, so the rigid material is used to analyze the rigid surface for simulation.  
When we define the contact property of the inner rings and the outer rings, the normal direction is hard contact while the penalty function method is selected for the tangential direction. We apply plane-plane contact with consideration of friction to each contact surface and establish contact pairs between the inner rings and the outer rings. The friction coefficient is 0.10 [21]. We apply symmetrical constraints on the symmetrical surface of the ring spring, and set \(\text{UX}=\text{UR}_{2}=\text{UR}_{3}=0\) on the \(\text{X}\) surface and \(\text{U}_{3}=\text{UR}_{1}=\text{UR}_{2}=0\) on the \(\text{Z}\) surface.
# Study on Seismic Performance of a Mold Transformer through Shaking Table Tests
###### Abstract  
This study presents an experimental seismic investigation of a 1000 kVA cast resin-type hybrid mold transformer through tri-axial shaking table tests. The input acceleration time histories were generated in accordance with the specifications recommended by the International Code Council Evaluation Services Acceptance Criteria ICC-ES AC156 code, with scaling factors in the range of 25-300%. The damage and the dynamic characteristics of the mold transformer were evaluated in terms of the fundamental frequency, damping ratio, acceleration time history responses, dynamic amplification factors, and relative displacement. The shaking table test results showed that the damage of the mold transformer was mainly governed by the severe slippage of the spacers and the loosening of the linked bolts between the bottom beam and the bed beam. In addition, the maximum relative displacement at the top beam in Y and Z-directions exceeded the boundary limit recommended by the Korean National Radio Research Agency. Moreover, the operational test of the specimen was performed based on the IEC 60076-11 Standard before and after the shaking table test series to ensure the operational capacity of the transformer.  
earthquake/seismic forces; seismic damage; mold transformer; shaking table test; non-structural elements; dynamic characteristics +
Footnote †: journal: Applied Sciences
# Accounting for Training Data Error in Machine Learning Applied to Earth Observations
## Guidelines and Recommendations
### Step 2: Minimize Design-Related Errors
#### 4.2.1 Sample Design
This problem can best be understood within the context of cross validation, which is appropriate for ML parameter tuning, e.g., [31]. However, when the numberof folds exceed one (as in our yield estimation case study; Figure 11B) then the portions excluded from training lose statistical independence and can no longer serve as the map reference [77]. Map reference data independence may also be undermined when training sites are selected iteratively, in order to increase their representativeness and improve ML performance e.g., [55; 149]. If the gain due to new training sites is assessed against the map reference, then it will also lose independence after the first iteration. Moreover, any error in the map reference sample will be integrated into the final map. [PERSON] et al. [55] avoided this problem by visually assessing whether their classifier improved map quality after having new TD points added to the initial sample. A more quantitative approach is to divide an initial sample into three splits: one for training, the second for validating algorithm improvements, including those related to the addition of new training sites, and the third as the map reference, used only for final accuracy assessment. This partitioning approach can be implemented in the mapping platform used in the cropland mapping case study, Figure 11A [199].
# SIS Epidemic Propagation on Scale-Free Hypernetwork
## 5 Conclusions and Discussion  
To characterize the spread of infectious diseases in real social contact networks, based on the dynamic evolution model of the scale-free hypernetwork and the SIS propagation model, this paper uses analytical and simulation methods to study the dynamic process of the global spread of diseases under the hypernetwork structure. The simulation results show that the theoretical results are in complete agreement with the simulation experiments. Through analysis of hypernetworks, infectious diseases can spread more widely and more rapidly in a population. The size of the hypernetwork has little effect on the global spread of the disease, while the selection of the initial propagation node has a greater impact on the rapid spread of the infectious disease in the early stage, and has less impact on the steady-state behavior of the system. Furthermore, by changing the structural parameters of the hypernetwork for simulation, it is found that the larger the number of old nodes selected each time and the number of hyperedges formed each time, the faster the spread of infectious diseases and the wider the spread. The number of new nodes has less impact on the spread of infectious diseases. The contagion rate not only affects the initial spread of infectious diseases, but also affects the steady-state behavior. The recovery rate mainly affects the spread of the disease in the later stable stage. The smaller the recovery rate, the larger the proportion of infected people in the network in the steady state.  
This paper mainly focuses on the dynamic process of the spread of infectious diseases in the uniform scale-free contact hypernetwork, revealing the evolution law of the network dynamics of infectious diseases from a macro perspective, which contributes to the discussion regarding the use of hypernetworks in the study of epidemics. In addition, it provides some theoretical references for relevant departments to prevent and control infectious diseases. In practice, the spread of infectious diseases in social networks is often affected by many complex factors. From the point of view of network dynamics, more sophisticated methods than before are needed to capture important aspects of the phenomenon of real social contagion. Whether non-uniform hypernetworks and dynamic hypernetworks have unique propagation characteristics remains to be further explored. Modeling on real network data, and the integration of environmental heterogeneity during transmission as well as individual heterogeneity into the hypernetwork model will also be a key research direction in the future.  
Investigation, K.W.; methodology, K.W. and Y.G.; resources, F.H.; software, K.W. and Y.G.; validation, Y.G. and F.H.; formal analysis, K.W.; writing--original draft preparation, K.W.; writing--review and editing, K.W. and F.H.; supervision, F.H. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Natural Science Foundation of China (Grant No. 61663041).  
Not applicable.  
The authors declare no conflict of interest.
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 2 Low-Resource Language Speech Recognition
### Methods of Low-Resource Language Speech Recognition
#### 2.2.1 Data Augmentation  
Data augmentation generates diverse data by designing various transformation functions [39; 40]. For speech recognition, popular data augmentation methods include adding noise [39] and reverberation [41], speed perturbation [42], using vocal tract length perturbation to generate warped spectral variants [43], speech synthesis [44; 45; 46; 47], SpecAugment [48], MixSpeech [49], sub-sequence sampling [50], etc. Data augmentation is one of the most-effective methods for low-resource language speech recognition and has been widely used in practice [40; 51; 52; 53; 54].
# Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers
## 1 Introduction
### Challenges in Achieving Cyber Resilience for Space Infrastructure
#### 1.2.1 Isolation and Limited Physical Access  
Space systems operate in an environment that is physically remote and largely inaccessible once launched [14]. This contrasts with terrestrial systems, which can undergo regular maintenance, upgrades, and security enhancements. Once a satellite or other space asset is deployed, it must function without direct physical intervention for its entire operational lifespan [1]. This places a greater emphasis on designing for resilience from the outset, as there are fewer opportunities to address vulnerabilities post-deployment. Unlike terrestrial systems, where maintenance can mitigate vulnerabilities as they emerge, space systems must pre-emptively account for a wide range of potential threats and environmental factors during the design and development phases [7; 26].
# A Study of OWA Operators Learned in Convolutional Neural Networks
## 4 Methods
### Layer Structure  
We propose the integration of OWA operators in a general CNN via the use of a special OWA layer [13; 38]. This layer will be placed inside the convolutional region of the network and will modify the input data, a 3D matrix of activations, by adding newly generated feature maps by means of OWA operators.  
This layer structure responds to our intention of only augmenting the information on the network while using the OWA operators to aggregate the channels based on channel-wise information, usually unavailable to the convolution operation. This way, the layer introduces some of the global scale information codified through channel-wise metrics into the computation of the early layers of the network, which usually exploit only local information. The general structure of the layer is depicted in Figure 1.  
This layer takes a 3-dimensional matrix of \(I\) rows by \(J\) columns by \(C_{in}\) channels (feature maps) as input, and appends \(C_{f}\) new channels of the same size to the matrix. Each of the new \(C_{f}\) channels are obtained by aggregating the original \(C_{in}\) channels by means of a specific OWA operator. These OWA operators are defined by a weighting matrix of  
Figure 1: Proposed OWA layer structure.  
\(C_{in}\times C_{f}\) elements, which are treated as any other parameter in the network and learned through backpropagation.  
To apply these OWA operators, two stages are needed. In the first one, we sort the original \(C_{in}\) channels is a decreasing order based on a channel-wise metric. Recall that, since we are ordering with respect to a global feature map metric and aggregating channel-wise, we are considering induced OWAs. In the second stage, we perform \(C_{f}\) weighted linear aggregations on the sorted channels. These two steps combined give us \(C_{f}\) independent OWA operators that share the same ordering metric. Finally, these new \(C_{f}\) are concatenated to the original matrix, giving an output with a total of \(C_{out}=C_{in}+C_{f}\) channels.  
Note that, in our aggregation proposal, we consider the channel as a whole, rather than aggregating each position \((i,j)\) independently. Thus, we have to carefully define the order metric used for the sorting stage, and the way the aggregation itself is performed, which are not trivial. We elaborate on these issues in the next sections.