esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Challenges and Motivation  
Much research has been carried out in dynamic scene recognition, as the semantic information among the different objects in a scene is preserved by the different frames of the video, and the orientation information is not lost. Classification of the still image scene is a challenging task due to the lack of spatial information about multiple objects in the still image. Also, the lack of a sufficient still image dataset is another challenge in this field. Many of the state-of-the-art approaches use techniques like data augmentation and image annotation to improve their performance, which results in a reduced diversity of data and is time-consuming. Although the majority of research works focus on the recognition of outdoor scenes, recent research has focused on indoor scene recognition. Many state-of-the-art approaches that have high performance in outdoor scenes do not have the same performance in indoor scenes. Unlike the outdoor scene, the indoor scene suffers from many challenges, including variation in illumination, size, spatial layout, several occlusions, varying spatial locations, etc.  
Indoor scene images consist of several discrete objects with diverse appearances, sizes, shapes, poses, and scales that can be placed at various spatial locations in a multitude of possible layouts. The major challenges in indoor scene recognition are the large-scale variations in spatial scaling and spatial layout of the different objects in the scene. For the above-mentioned challenges, firstly, in different scenes of the same class, the constituent objects have different spatial scales. Secondly, these constituent objects may be located in varying spatial locations and in numerous possible layouts. For example, a fridge can be available in a kitchen at different spatial locations with significant differences in appearance and scale. Some indoor scenes can be characterised by global appearance information, whereas others require local spatial information. For example, a corridor scene can be characterised by a single object (e.g., a large wall), but a kitchen scene can include multiple objects (e.g., a fridge, an oven, a cooking range). Therefore, in order to accommodate all the diverse scene types, both local and global spatial information needs to be considered. This is also a critical condition for indoor scene recognition. Indoor scene environments consist of rich and decorative features in disordered patterns that vary in size, orientation, and layout. The above-mentioned challenges inspired us to work on indoor scene recognition in order to achieve invariance in the scaling and spatial layout of the objects in the indoor scene images
# Directional Support Vector Machines
## 6 Asymmetric Directional SVM  
In Figure 4, we portray a toy dataset together with the model that optimizes Equation (7) using \(g_{t}\) in the model \(f(x)\). As observed, the margin is determined by the \"worst case\" transition between positive and negative examples. It is reasonable to assume that a model placing the second threshold centered in the gap between positive and negative examples would generalize better. [PERSON] and [PERSON] [PERSON] and [PERSON] (2016) faced a problem with similar characteristics when addressing ordinal data classification in \(\mathbb{R}^{D}\). Similar to them, we propose to maximize the sum of the margins around the two threshold points. Towards that goal, we only need to generalize the model to allow independent slopes in the two parts of the triangle wave, setting \(f(x|w_{0},w_{1},\boldsymbol{\theta},\zeta)=w_{0}+w_{1}g_{asy}(x+\boldsymbol{ \theta},\zeta)\), where:  
\[g_{asy}(x,\zeta)=\begin{cases}1-\frac{x-2k\pi}{\pi(1-\zeta)},\ 2k\pi\leq x\leq 2\pi(1- \zeta)+2k\pi\\ \\ 1+\frac{x-2k\pi}{\pi\zeta},\ -2\pi\zeta+2k\pi\leq x\leq 2k\pi\\ \\ \zeta\in(0,1),\,k\in\mathbb{Z}.\end{cases}, \tag{14}\]  
Here, \(\zeta\) controls the asymmetry of the wave: if \(\zeta\to 0\), the wave has infinite ascending slope; if \(\zeta\to 1\), the wave has infinite descending slope, and for \(\zeta=0.5\), it coincides with the symmetric case, \(g_{t}\). The wave is depicted in Figure 5 for some values of \(\zeta\).  
It should be clear that the model instanced with \(g_{asy}\) retains the same expressiveness as before, being able to express two thresholds (and not more than two) in any position in \([0,2\pi)\).  
As before with \(g_{t}\), it is not possible to solve the optimization problem as a conventional setting, and we again directly optimize the goal function using sub-gradient methods.  
Figure 4: Toy example where the fitted symmetric model does not provide an appealing solution.
# Instantaneous Networking Service Availability for Disaster Recovery
## 5 Restoration of Networking Service Availability for Users in Multiple Areas  
In what follows, we analyze the the temporal and spatial features of restoration process in detail regarding the networking service availability for users. We take the time consumption in launching each LI-Cloud and the global network into account. The list of symbols is shown in Table 1.  
The basic scenarios studied in the evaluation involve three areas, as shown in Figure 1. There can be various scenarios in a disaster situation. It may also be difficult to adopt a few scenarios to represent all others. Hence, we try to employ the scenarios that reflect the basic functionalities and fundamental operations in network restoration, involving (1) local connections and their restoration and (2) global connections and their restoration.
###### Abstract  
In this paper, we consider the emerging context of ALOHA-based multi-static backscattering communication systems. By assuming an architecture consisting of a set of passive backscattering nodes, an illuminator, and a set of spatially dislocated receivers, we firstly propose a cross-layer framework for performance analysis. The model jointly accounts for the shared wireless channel, including fading and capture effect, and channel contention strategy, which is regulated by a Framed Slotted ALOHA protocol. Furthermore, based on the inherent macroscopic diversity offered by the multi-static settings, we introduce the concept of capture diversity, which is shown to enable multiple packet detection in slots with multiple transmissions. In order to characterize the multiple access interference and approximate the capture probabilities, we enforce a log-normal approximation of the inverse Signal-to-Interference Ratio that relies on moment matching. Numerical results show the impact of deployment scenarios and the relative positions of illuminator, backscattering nodes, and receivers on the system normalized throughput. We show how the number of detection points impacts the system performance under various channel conditions. Moreover, the accuracy of the proposed approximation rationale is validated via Monte Carlo simulations. Finally, we analyze the optimal frame length in the presence of capture diversity.  
backscattering communications; random channel access; cross-layer analysis 2021215070[[https://doi.org/10.3390/s21155070](https://doi.org/10.3390/s21155070)]([https://doi.org/10.3390/s21155070](https://doi.org/10.3390/s21155070))
## 3 Predictive Equations for Shear Spring Parameters
### Comparison with Existing Models  
To evaluate the predictive accuracy of the proposed model and existing models, the ratio of analytical results to experimental data was determined for three key parameters: maximum force (\(R_{F}\)), initial stiffness (\(R_{\text{K0}}\)), and cumulative energy dissipation capacity (\(R_{\Sigma E}\)). The performance of the proposed model was compared against those of [PERSON] and [PERSON] (2018) [16] and [PERSON] et al. (2016) [21] using data from 15 column specimens as shown in Table 2 and Figure 11.  
For maximum force prediction, all three models exhibit strong agreement with experimental data, with a median value of 0.95. In terms of standard deviation, the proposed approaches including those from [PERSON] achieve the lowest value at 0.11, indicating a high degree of prediction precision. In terms of initial stiffness prediction, the proposed model demonstrates the most accurate results with a median value of 0.94 and a relatively low standard deviation of 0.25. Regarding cumulative energy dissipation, the [PERSON] approach exhibited the closest agreement to experimental data in terms of the median value. However, its higher standard deviation compared to the other approaches indicates a lower level of prediction consistency. Therefore, based on the experimental data considered in this study, it could be concluded that the proposed model offers better predictive performance for maximum force, initial stiffness, and cumulative energy dissipation compared to existing models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Maximum Force**} & \multicolumn{3}{c}{**Initial Stiffness**} & \multicolumn{3}{c}{**Cumulated Energy**} \\  & \multicolumn{3}{c}{**(\(R_{\text{K}}\))**} & & \multicolumn{3}{c}{**Dissipation Capacity (\(R_{\Sigma E}\))**} \\ \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Proposed**} & **[PERSON] and [PERSON] (2018)[16]** & **[PERSON] et al. (2016)[21]** & **Proposed** & **[PERSON] and [PERSON] (2018)[16]** & **[PERSON] et al. (2016)[21]** & **Proposed** & **[PERSON] and [PERSON] (2018)[16]** & **[PERSON] et al. (2016)[21]** \\ \hline Median & 0.96 & 0.97 & 0.95 & 0.94 & 0.76 & 0.83 & 0.82 & 0.74 & 0.94 \\ \hline Standard & \multirow{2}{*}{0.11} & \multirow{2}{*}{0.11} & \multirow{2}{*}{0.24} & \multirow{2}{*}{0.25} & \multirow{2}{*}{0.22} & \multirow{2}{*}{0.35} & \multirow{2}{*}{0.13} & \multirow{2}{*}{0.19} & \multirow{2}{*}{0.26} \\ Deviation & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Median and standard deviation of comparative analysis between proposed and existing models.  
Figure 11: Statistical comparison with existing models [16; 21]. Note: red cross means outlier.
## 3 Impact of external errors
### Impact of roll angle knowledge
#### Sensors 2006
H can be easily expressed in the local frame and (Ni) are related to all the noises defined in Equation (18). Covariance matrices of the estimated parameters can then be computed as followed, leading to the knowledge of the quality of the process:  
\[C_{\theta}=\left(H^{\,T}R^{-1}H\right)^{-1} \tag{20}\]  
Figure 15: Geometry of crossovers measurements and definition of the appropriate local frame. The quality of the estimation is defined by the standard deviation of the roll angle, related to the residual error in its knowledge (in arcsec). The histogram in Figure 17 (a) shows that the shorter the repeat cycle, the better the estimation. The main reason is the ocean decorrelation which becomes very important above 5 days (see [25]). The histogram in Figure 17 (b) shows that even a very small number of nadir/swath measurements help decrease the error in the a posteriori knowledge of the roll angle. Finally, the last histogram in Figure 18 shows that the minimum onboard incoherent processing (going to 3 km pixels almost removes errors greater than 0.05 arc second!). Indeed, it would be better to do the estimation with high  
Figure 16: (a) Latitudinal Dependence of the interval between successive crossovers (b) WSOA Jason-2 crossovers characteristics: blue refers to ascending tracks first and red to descending tracks first; each rectangle corresponds to one crossover, its width defining how long it is over flown. Figure 17: (a) Influence of the orbit characteristics on the quality of the processing (b) Influence of the presence of nadir altimeter on the quality of the processing. resolution data, even if the precision is lower, and then add a ground average processing procedure to get the final pixel resolution more compliant with the required height precision. Processing crossovers may also enable us to correct the linear part of the ionospheric and tropospheric gradients errors in the swath, which would then be assimilated as an additional roll angle.
## 3 Methodology
### Forgery Score Computation Based on Frequency Analysis  
In this study, we utilized a trained generator model based on \"U-Net\" architecture [38] to obtain a 3D representation of the original artist's brushstroke style, denoted as \(G\) (\(qx_{m,p}\), \(z\)), where \(z\) represents the random noise vector in the generator model of \(G\). Given a query natural-light painting image \(qx_{m,p}\in Up\), our objective was to quantify the dissimilarity between the generated RTI painting image \(G\) (\(qx_{m,p}\), \(z\)), and its corresponding GT RTI brushstroke image \(qy_{m,p}\in U\_RTIp\). While the theoretical formulation includes a noise vector \(z\), in practice, introducing noise did not significantly improve the results and was omitted in the implementation of cGAN [13].  
The degree of dissimilarity of the query image \(Qp\) was computed using 2D FFT frequency analysis. The Formula (1) defines the discrete Fourier Transform \(F\) of a c-by-c brushstroke image of unseen images \(V\) (i.e., \(qy_{m,p}\), \(G\) (\(qx_{m,p}\), \(z\))) \(\in Qp\). In this formula, \(Wc\) represents complex roots of unity, where \(Wc=e^{-2\Pi(i/c\Pi)}\), and \(i\) is the imaginary unit. The indices \(s\) and \(j\) run from 0 to \(c\)-1, and the indices \(t\) and \(k\) also run from 0 to \(c\)-1.  
\[F_{s+1,\ t+1}=\sum_{j=0}^{c-1}\sum_{k=0}^{c-1}W_{c}^{j\beta}W_{c}^{t}(V)_{j+1,k +1} \tag{1}\]Using Fourier transform \(F\), we extracted frequency features from each image \(V\) and compared these features using Euclidean norm. We obtained forgery score (_F-Score_) by calculating the Euclidean norm between \(F_{s+1,t+1\in\mathbb{G}}\) and \(F_{s+1,t+1}\), which were, respectively, derived from each brushstroke image patches \(G\) (\(qx_{m,p}\), _z_) and the corresponding GT RTI image patch \(qy_{m,p}\), where \(\left\{qx_{m,p},\ qy_{m,p}\right\}_{p=1,\ m=1}^{P,M}\in Qp\).  
\[||F_{Score}||=\sqrt{\sum_{k=1}^{\leq c}\left|F_{s+1,t+1}-F_{s+1,t+1\in\mathbb{G }}\right|^{2}} \tag{2}\]  
Since the forgery scores varied for each brushstroke image patch due to spatial information, the authentication assessment of the entire query painting image \(Qp\) considered the mean of the F-Score for all the \(m\) patches, denoted by \(m\) = 1, 2,..., \(M\).
## 3 Developments of CIM
### Problems Encountered in CIM Development
#### 3.2.2 CIM Data Specification Issues  
In the development of CIM, data standardization serves as a critical foundation for ensuring data consistency, reliability, and availability. As a novel and transformative production factor, data have rapidly permeated diverse domains, including production, distribution, circulation, consumption, and social service management, fundamentally reshaping production methodologies, lifestyle paradigms, and social governance frameworks. Nevertheless, in the practical implementation of CIM, the formulation and enforcement of data standards present significant challenges.  
Data standardization encompasses the precise definition of the format, structure, and semantics of urban data, ensuring their accurate interpretation, processing, and exchange across heterogeneous systems. This foundational requirement is indispensable for achieving interoperability and facilitating the data sharing within CIM. Nevertheless, the implementation of standardized data practices encounters significant challenges due to the disparities in the policy frameworks, urban requirements, and technological ecosystems across nations, despite the ongoing efforts by international organizations and industry consortia to establish comprehensive data standards [52]. First, the quality of urban data remains inconsistent, primarily due to the variations in data collection methodologies and standards across different cities. These discrepancies often result in inaccurate or incomplete datasets, complicating their utilization and integration into CIM systems. Second, the absence of unified standards creates substantial barriers to effective data exchange and system interoperability. Many countries and regions have yet to adopt a common data standard for CIM, impeding the seamless communication between systems and hindering broader integration initiatives. To address these challenges, it is imperative to foster international collaboration aimed at harmonizing data structures and semantic specifications. This can be achieved by leveraging the established industry standards, such as those developed by the International Organization for Standardization (ISO) and the World Wide Web Consortium (W3C), and by establishing a global alliance for urban data standardization. Such an alliance would facilitate cross-border cooperation in the development and alignment of technical standards, ultimately advancing the evolution of CIM and promoting global-scale data sharing.
## 3 Mathematical Model
### Model Formulation  
#### 3.3.1 Objective Function  
At the th period of the pre-sale period, the actual sale amount in section (i,j) depends on the minimum value of the passenger demand \(\rm d_{ij}^{t}\) and the ticket allocation \(\rm x_{ij}^{t}\), i.e.,\(\min\left\{\mathrm{d}_{ij}^{\mathrm{t}},x_{\mathrm{ij}}^{\mathrm{t}}\right\}\). Then, in this period within the pre-sale period, the ticket revenue of section (i,j) can be expressed as Equation (7).  
\[\mathrm{R}_{\mathrm{ij}}^{\mathrm{t}}=\mathrm{p}_{\mathrm{ij}}^{\mathrm{t}}. \min\left\{\mathrm{d}_{ij}^{\mathrm{t}},x_{\mathrm{ij}}^{\mathrm{t}}\right\} \tag{7}\]  
If \(\mathrm{d}_{\mathrm{ij}}^{\mathrm{t}}\leq x_{\mathrm{ij}}^{\mathrm{t}}\), all passengers' ticket purchase demands are met. Otherwise, all passengers exceeding the number of tickets allocated \(x_{\mathrm{ij}}^{\mathrm{t}}\) will enter the queue of standby seats at a proportion of \(\mathrm{\theta}_{\mathrm{ij}}\), and then wait to buy the standby tickets at the final period \(\mathrm{T}\) of the pre-sale period. The objective function of maximizing the train revenue is established as shown in Equation (8).  
\[\max\mathrm{R}=\sum\limits_{\mathrm{t=0}}^{\mathrm{T}-1}\sum\limits_{\mathrm{i= i+1}}^{\mathrm{M}-1}\mathrm{p}_{\mathrm{i}}^{\mathrm{t}}.\min\left\{\mathrm{d}_{ij}^{ \mathrm{t}},x_{\mathrm{ij}}^{\mathrm{t}}\right\}+\sum\limits_{\mathrm{t=T-1} }^{\mathrm{T}}\sum\limits_{\mathrm{i=1}}^{\mathrm{M}-1}\mathrm{p}_{\mathrm{ i}}^{\mathrm{t}}.\left(\min\left\{\mathrm{d}_{ij}^{\mathrm{t}},x_{\mathrm{ij}}^{ \mathrm{t}}\right\}+\mathrm{\theta}_{\mathrm{ij}}.\mathrm{\delta}_{\mathrm{ ij}}.\mathrm{M}_{\mathrm{ij}}^{\mathrm{T}-1}\right) \tag{8}\]
###### Abstract  
This study aimed to investigate the reliability, validity, and sensitivity of spatiotemporal parameters, during sprint skating, of bandy players. Thirty-two well-trained male bandy players (age: 17.8 \(\pm\) 1.2 years; height: 1.80 \(\pm\) 0.06 m; body mass: 75.7 \(\pm\) 1.2 kg) participated in this study. They performed two 80 m linear skating sprints. To calculate the velocities and obtain glide-by-glide spatiotemporal variables, nine timing gates and two skate-mounted inertial measurement units (IMUs) were synchronized and used. The spatiotemporal variables at each step included the glide time, glide length, double support time, double support length, step length, and step frequency. All the spatiotemporal variables were analyzed separately: averaged over 80 m, during the acceleration, and the maximal steady-state phases. The relative and absolute reliability of the spatiotemporal parameters were good (ICC \(>\) 0.70; CV \(<\) 10%), except for the step frequency during the steady-state phase. The spatiotemporal parameters showed \"good\" to \"satisfactory\" sensitivity during the acceleration phase and whole sprint, and \"marginal\" sensitivity during the steady-state phase. Content validity was confirmed by a low percentage of the shared variance (17.9-34.3%) between the spatiotemporal parameters obtained during the acceleration and steady-state phases. A \"stepwise\" regression significantly predicted the steady-state skating velocity from the spatiotemporal metrics obtained during the acceleration [F\({}_{(5,26)}\) = 8.34, _p_\(<\) 0.001, adj. R\({}^{2}\) = 0.62] and steady-state phases [F\({}_{(5,26)}\) = 13.6, _p_\(<\) 0.01, R\({}^{2}\) = 0.67]. Only the step frequency obtained in the acceleration phase significantly predicted the maximal skating velocity (_p_\(<\) 0.01), while the glide length and step frequency derived during the steady-state phase significantly added to the prediction (_p_\(<\) 0.01). In conclusion, the spatiotemporal parameters, obtained by two skate-mounted IMUs, were shown to be reliable and sensitive measures of sprint skating, and they could be used to provide independent information for the different skating phases. The maximal skating velocity could be predicted from the spatiotemporal parameters, with longer gliding and more frequent steps as the most significant determinants.  
kinematics; acceleration phase; steady-state phase; maximal velocity; glide time and length; double support time and length; step frequency; wearables; accelerometers +
Footnote †: journal: Applied Sciences  
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Footnote †: journal: Applied Sciences  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))