esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of the Elevation Angle on CYGNSS GNSS-R
## 6 Results on the Surface Roughness and the Soil Moisture Effect
### Global Analysis
It was found \(\text{Y}_{\text{r,peak,LHCP}}\) decreased \(\approx 20\%\) when \(\theta_{\text{e}}\) reduced from \(\theta_{\text{e}}=90^{\circ}\) to \(\theta_{\text{e}}=50^{\circ}\). \(\Gamma_{\text{LHCP,cyGNSS}}\) increased in the second range (from \(\theta_{\text{e}}\approx 55^{\circ}\) to \(\theta_{\text{e}}\approx 25^{\circ}\)), which could be justified because the reflectivity became mostly coherent \(\Gamma_{\text{LHCP,coh}}\) in regions without rough topography. Figure 5: One-month (from 20 September to 20 October 2017) averaged CyGNSS bistatic reflectivity \(\Gamma_{\text{LHCP,cyGNSS}}\) as a function of the elevation angle \(\theta_{\text{e}}\) for different values of effective surface roughness parameter h: **(a)** [0.10,0.11], **(b)** [0.11,0.12], **(c)** [0.12,0.13], **(d)** [0.13,0.14], **(e)** [0.14,0.15], and **(f)** [0.15,0.16]. For each plot (**a–f**), different levels of SMC were used in this analysis at steps of 0.1 m\({}^{3}\)/m\({}^{3}\), from 0 m\({}^{3}\)/m\({}^{3}\) to 0.5 m\({}^{3}\)/m\({}^{3}\). The averaging was performed over the complete land-surface coverage enabled by CyGNSS. \(\sigma_{\rm LHCP,coh}^{0}\) was high over smooth or slightly-to-moderate rough surfaces, because it increased with a decrease in surface roughness. As such, \(\Gamma_{\rm LHCP,CyGNSS}\) increased with a decrease in \(\theta_{\rm e}\) in the angular range \(\theta_{\rm e}\) < 55\({}^{\circ}\) because the signal coherence increased with a decrease in \(\theta_{\rm e}\)[51], and thus \(\sigma_{\rm LHCP,coh}^{0}\) became dominant for \(\theta_{\rm e}\) < 55\({}^{\circ}\). The scattering phenomenon became more complicated in regions with a rough topography. As such, this result should be understood as the average response over the Earth's surface. For higher \(\theta_{\rm e}\), even in a Nadir-looking configuration, there was still a non-negligible coherent component so as to be tracked [32; 53; 54]. The dynamic range of \(\Gamma_{\rm LHCP,CyGNSS}\) was higher for lower roughness levels, which seemed to indicate GNSS-R loses sensitivity to SMC over rougher surfaces in a general scenario. This reduction of the dynamic range appeared to be dependent on \(\theta_{\rm e}\) along the second angular range, where \(\sigma_{\rm LHCP,coh}^{0}\) was dominant. This increment was larger for decreasing SMC levels, which meant that the effect of a higher signal coherence was more relevant over areas with low SMC. Additionally, the effect of a larger signal attenuation through the canopy layer L\({}^{\rm canopy}\) over highly vegetated areas was larger for a lower \(\theta_{\rm e}\), which could contribute to this loss of sensitivity. Overall, \(\Gamma_{\rm LHCP,CyGNSS}\) increased as SMC increased for a given \(\theta_{\rm e}\) and h (Figure 5).
# Using the Hall Effect for Monitoring the Starter Condition in Motor Vehicles
## 2 Analysis of the Magnetic Circuit and Distribution
### Magnetic Field from Excitation Winding  
Induction in gap \(B_{\delta fx}\) from excitation winding is proportional to field strength \(H_{\delta fx}\).  
\[B_{\delta fx}=\mu_{s}\times H_{\delta fx} \tag{1}\]  
where air magnetic permeability \(\mu_{s}\) = 4\(\pi\times 10^{-7}\) H/m.  
The strength of magnetic field \(H_{\delta fx}\) can be expressed by the following formula when magnetic tension drops in the iron core are neglected:  
\[H_{\delta fx}=\frac{\mu_{\delta fx}}{l_{\delta f}} \tag{2}\]  
When magnetic tension drops in the air gap,  
\[U_{\delta fx}=F_{f}=0.5\;\Theta_{f} \tag{3}\]  
where \(F_{f}\) is the magnetic tension of magnets, and \(\Theta_{f}\) is the excitation flow of one pair of poles.  
\[\Theta_{f}=\frac{z_{f}}{p}I_{f} \tag{4}\]  
where \(z_{f}\) is the number of coils on all poles, \(p\) is the number of pole pairs, and \(I_{f}\) is the excitation current.  
The gap length is calculated as follows:  
\[l_{\delta x}=k_{c}\times\delta(x). \tag{5}\]  
The Carter coefficient \(k_{c}\) denotes a calculative increase of the air gap as a result of the existence of grooves, usually \(k_{c}\) = 1.05 \(\div\) 1.2. Eventually, the distribution of induction from excitation winding can be approximately determined from the following formula:  
\[B_{\delta fx}=\frac{\mu z_{f}}{2 pk_{c}\delta(x)}. \tag{6}\]  
Function \(\delta(x)\), present in the formula, is determined on the basis of the set shape of the pole shoe. The interpolar gap and the edges of the pole shoe are the places where equivalent gap length equal to the length of magnetic field lines between the armature and the pole can be determined. Field distribution is determined separately for each pole, assuming that there are no adjacent poles. Then, by summing field distributions of two consecutive poles, the resultant distribution can be obtained from the formula:  
\[B_{\delta\delta x}=B_{N}(x)+B_{s}(x). \tag{7}\]  
An example of induction distribution for a machine with a concentric gap is presented in Figure 2, in which the pole pitch  
\[\tau=\frac{\pi D}{2p} \tag{8}\]  
where \(D\) is the armature diameter, and \(p\) is the number of pole pairs.
## 2 Related Work  
### Network Structure  
The development process of SAGIN can not avoid the challenges of network structure. In order to achieve the convergence of the heterogeneous devices, researchers have carried out some studies on structure design.  
Early studies of SAGIN structure did not consider the change in satellites from merely providing forwarding functions to supporting information storage and processing, nor did they completely consider the application of SDN and NFV technologies in converged networks. For example, [PERSON] et al. proposed the concept and definition of an early satellite terrestrial converged network containing only satellite and terrestrial communication networks [9]. However, they proved the possibility of satellite and terrestrial networks communicating with each other only at the physical layer, failing to solve the resource management and allocation problems in the heterogeneous networks. [PERSON] et al. introduced MEC into satellite networks and proposed a bilateral computation offloading of satellite terrestrial networks in the structure [10], which gives full play to the advantage of comprehensive satellite coverage and solves the problem of limited ground network services. The structure does not have a specific resource management model, although it proposes to unify the resources of satellite ground networks for management.  
The SDN, NFV, and MEC technologies have recently received increasing attention from SAGIN researchers. For example, [PERSON] et al. proposed a SAGIN network structure consisting of LEO satellites and civil aircraft. However, the structure ignores the management role of Medium Earth Orbit (MEO) satellites in SAGIN. GIAMBENE et al. proposed a satellite access terrestrial network structure designed for eMBB scenarios [11], where the satellite backhaul network is connected to the 5G core network through a terrestrial gateway to realize satellite and 5G convergence, and user terminals can communicate with satellites either directly or via terrestrial satellite relay (satellite terminals only), but the auxiliary functions of the aircraft network have not been considered. [PERSON] et al. proposed the SAGIN structure in the Internet of Vehicles scenario, but the centralized management of the whole network needs to be considered [12]. Although researchers have extensively studied SAGIN through the latest technological means and achieved specific results, they have yet to consider the complex structure of the converged network and the large number of heterogeneous devices comprehensively. In response to the above problems, this paper proposes a SAGIN network structure that uses SDN controllers for hierarchical management. This structure can efficiently manage heterogeneous devices in converged networks and achieve efficient and flexible collaborative work among heterogeneous networks.
## 3 System Model
### Air-to-Ground Path Loss Model
Thus, the signal-to-noise ratio between MD \(i\) and UAV-MEC server \(j\) in time slot \(t\) is  
Figure 1:1  
where \(P_{i}\) is the transmit power of MD \(i\), and \(\sigma^{2}\) is the noise power. The ground-to-air (G2A) data transmission rate between MD \(i\) and UAV-MEC server \(j\) in time slot \(t\) is  
Figure 1:2  
The average data transmission rate for MD \(i\) throughout cycle period T is then defined as  
Figure 1:3  
Assume that all task requests coming from MD \(i\), which is connected to UAV-MEC server \(j\) based on maximum throughput, are offloaded through the G2A channel. Thus, the G2A data transmission latency, \(T_{i,j}^{Trans}\), between MD \(i\) and UAV-MEC server \(j\) is determined by the task's data size, \(\mathbf{i}_{i}\), and the rate at which the data are transmitted, \(R_{i,j}\). Then, \(T_{i,j}^{Trans}\) can be stated as  
Figure 1:4  
Therefore, the total data transmission latency experienced by the MDs connected to UAV-MEC server \(j\) for offloading task requests per second can be obtained as follows:  
Figure 1:5  
where \(\lambda_{i}\) is the task offload request rate of MD \(i\). The energy consumption for task transmission between MD \(i\) and UAV-MEC server \(j\) can be obtained as follows:  
Figure 1:6  
The total transmission energy consumption of all MDs connected to UAV-MEC server \(j\) for offloading task requests per second is  
Figure 1:7
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 4 Pipe Routing Scheme
### Route Selection  
During the route discovery process, if the target UAV (source node) does not already have a route to the destination (in our setting, the BS), it broadcasts an RREQ message towards the BS. Unlike [17], which floods the RREQ messages in the entire network, our scheme takes advantage of the UAV's knowledge of the BS's GPS location and broadcasts the RREQ messages only in the direction of the BS. In response, the BS sends the RREP messages back to the target UAV over the reverse routes, identified during the RREQ forwarding phase. To find stable routes, with a low control packet overhead, RREQ forwarding is limited to intermediate nodes that meet both the stability condition of the link \(l\), \(LLT_{l}>TTL\), and the energy threshold condition of node \(i\), \(En_{i}>En_{th}\).  
The target UAV receives RREPs containing the HC and IL values of the respective routes. The target UAV then computes the best route \(R^{*}\) (route with the least cost \(C_{R}\), defined in (7)) by selecting among all routes that satisfy a lifetime condition (5) and an energy condition (6). The selected route is long-lasting, less congested, and energy-aware. Here, the lifetime condition is,  
\[RLT_{R}>TTL, \tag{5}\]  
where \(TTL\) is the time-to-live value of the data packets. The energy condition is,  
\[En_{R}>En_{th}+\delta_{e}, \tag{6}\]  
For each satisfying route \(R\), we compute the cost,  
\[C_{R}=w_{1}(\frac{HC_{R}}{HC_{min}})+w_{2}(\frac{IL_{R}}{IL_{min}+\alpha\,IL_{ R}}), \tag{7}\]  
where \((\cdot)_{min}=\min_{R\in X}(\cdot)_{R}\) and \(X\) is the set of all routes known by the target UAV that satisfy the constraints (5) and (6). The weights \(w_{1}\) and \(w_{2}\) determine the balance between the route length (\(HC_{R}\)) and the interference along the route (\(IL_{R}\)), with \(w_{1}+w_{2}=1\). Increasing \(w_{1}\) favors shorter routes, while increasing \(w_{2}\) prioritizes routes with lower interference; we set \(w_{1}=w_{2}=0.5\). To reduce the route computation delay, we only consider routes with \(HC_{R}\leq HC_{min}+3\). We use \(\alpha=0.3\) as the scaling factor to convert the IL cost values to a similar range as the HC cost values. Since \(HC_{min}\geq 2\), the range of \((\frac{HC_{R}}{HC_{min}})\) is (1, 2.5). Similarly, since \(IL_{min}\geq 2\), \((\frac{IL_{R}}{IL_{min}+\alpha\,IL_{R}})\) can vary from 0.77 to 3.33. However, in our simulation settings, the range of this ratio is [1, 2.5) because \(IL_{R}\) is generally greater than \(IL_{min}\).
# Experimental Testing of Reinforced Concrete Deep Beams Designed by Strut-And-Tie Method
## 5 Results and Discussion
#### Optimal Strut-And-Tie Model of Reinforced Concrete Members  
Since different Strut-and-Tie models can be used for the design of the member, it has to be considered which of them presents the optimal model. The selection of the optimal Strut-and-Tie model certainly depends on the engineering estimation as well as on the adopted criteria. The criteria could be classified as: the quantity of reinforcement (models with a smaller amount of reinforcement have priority), the \"complexity\" of the reinforcement scheme (the models in which the reinforcement is placed only in orthogonal directions, parallel to the edges of the element, have the advantage), the size and arrangement of the cracks (preferred models are those with smaller crack widths), and maximum mid-span deflection. In this research, all considered criteria are covered with the same importance. The criteria for all three specimens are given in Table 8. The plus sign (+) indicates that the specimen is optimal, according to one of the criteria listed above.  
Member specimen W3 has the smallest crack widths and mid-span deflection at the maximum load intensity, but also the largest amount of reinforcement. However, it must be admitted that in the same specimen, the reinforcement layout is the most complex one. On the other hand, member W2 has the smallest amount of reinforcement and the simplest reinforcement layout, but has the greatest crack widths and mid-span deflection at the maximum load intensity. Although specimens W2 and W3 have the same number of plus (+) marks, the authors of this paper nevertheless suggest, regardless of the criteria taken with the equal importance, that the optimal solution is the one applied in specimen W3 since in its case the smallest crack widths occur.  
The current situation in the field of research is such that the solutions for the shapes of Strut-and-Tie models are not unambiguous. There is no single view on how to obtain an optimal shape. For example,  
Figure 28: “U” profile at the point of support of the tested specimens on the steel frame.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Members**} & \multicolumn{4}{c}{**Criteria by**} \\ \cline{2-5}  & **Reinforcement** & \multicolumn{2}{c}{**Simplicity of**} & \\  & **Amount** & **Reinforcement Layout** & **Crack Width** & **Max Mid-Span Deflection** \\ \hline W1 & – & – & – & – \\ W2 & + & + & – & – \\ W3 & – & – & + & + \\ \hline \hline \end{tabular}
\end{table}
Table 8: The selecting of optimal solution.  
in order to obtain a minimum amount of reinforcement or minimal displacement or the simplest reinforcement layout, different shapes of the Strut-and-Tie models can be determined. The same conclusion is reached by analyzing the research in references [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. It is also concluded that the used shapes of the Strut-and-Tie models, regardless of the way they were adopted, have been experimentally verified.
# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
###### Abstract  
Accurate teeth segmentation and orientation are fundamental in modern oral healthcare, enabling precise diagnosis, treatment planning, and dental implant design. In this study, we present a comprehensive approach to teeth segmentation and orientation from panoramic X-ray images, leveraging deep-learning techniques. We built an end-to-end instance segmentation network that uses an encoder-decoder architecture reinforced with grid-aware attention gates along the skip connections. We introduce oriented bounding box (OBB) generation through principal component analysis (PCA) for precise tooth orientation estimation. Evaluating our approach on the publicly available DNS dataset, comprising 543 panoramic X-ray images, we achieve the highest Intersection-over-Union (IoU) score of 82.43% and a Dice Similarity Coefficient (DSC) score of 90.37% among compared models in teeth instance segmentation. In OBB analysis, we obtain the Rotated IoU (RIoU) score of 82.82%. We also conduct detailed analyses of individual tooth labels and categorical performance, shedding light on strengths and weaknesses. The proposed model's accuracy and versatility offer promising prospects for improving dental diagnoses, treatment planning, and personalized healthcare in the oral domain.  
instance teeth segmentation; panoramic X-ray images; oriented bounding boxes (OBB); FUSegNet; PCA 2020 IEEE  
2
###### Abstract  
The variation in key parameters of the solar-terrestrial space during two consecutive auroral disturbances (the magnetic storm index, Dst index \(=-422\) nT) that occurred during the 18-23 November 2003 period was analyzed in this paper, as well as the spatiotemporal characteristics of NO 5.3 \(\upmu\)m radiation with an altitude around the location of 55\({}^{\circ}\)N 160\({}^{\circ}\)W. The altitude was divided into four regions (50-100 km, 100-150 km, 150-200 km, and 200-250 km), and it was found that the greatest amplification occurs at the altitude of 200-250 km. However, the radiance reached a maximum of 3.38 \(\times\) 10\({}^{-3}\) W/m\({}^{2}\)/sr at the altitude of 123 km during the aurora event, which was approximately 10 times higher than the usual value during \"quiet periods\". Based on these findings, the spatiotemporal variations in NO 5.3 \(\upmu\)m radiance within the range of latitude 51\({}^{\circ}\)S-83\({}^{\circ}\)N and longitude of 60\({}^{\circ}\)W-160\({}^{\circ}\)W were analyzed at 120 km, revealing an asymmetry between the northern and southern hemispheres during the recovery period. Additionally, the recovery was also influenced by the superposition of a second auroral event. The data used in this study were obtained from the OMNI database and the SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) infrared radiometer onboard the TIMED (Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics) satellite. Finally, the correlation of NO 5.3 \(\upmu\)m radiance at 120 km with temperature, solar wind speed, auroral electrojet index (AE index), and Dst index were analyzed. It was found that only the Dst index had a good correlation with the radiance value. Furthermore, the correlation between the Dst index and radiance at different altitudes was also analyzed, and the highest correlation was found at 170 km.  
infrared radiation; aurora; geomagnetic storm +
Footnote †: journal: remote sensing
## 8 Concluding Remarks  
This work addresses the problem of packet-switched Xhaul network planning in a network slicing scenario, where two principle 5G services were supported, eMBB and URLLC. We have shown that applying advanced optimization techniques, such as the column-generation method, can facilitate solving challenging optimization problems, such as the considered network slicing-planning problem in latency-aware packet Xhaul networks. The proposed method PBA is much more scalable than when solving the complete MILP model, and its performance does not depend significantly on the number of candidate paths, whereas MILP can perform poorly for larger sets of paths. Consequently, the PBA can provide good-quality results in much shorter times, particularly in large network scenarios where MILP has difficulties even finding a feasible solution. The relative difference in the objective function values reaches up to several tens of a percent in favor of the PBA, and the computation times are reduced between several up to some tens of times.  
The network analysis brings several conclusions. The number of required PPs is correlated with the volume of traffic associated with the latency-sensitive (URLLC) network slice. The PP requirements are lower for higher 5G radio numerologies applied. When PP capacity increases, the number of active PPs is also somewhat reduced (up to 10-20% for higher numerologies). Assigning higher priorities to the data bursts of the low-latency FH flows related to URLLC network slices than to the bursts of the FH flows belonging to eMBB slices is more beneficial than using the same priorities. In this case, the reduction of the number of required PPs was between 10% and 20%. Eventually, the overall transport network latencies can be also reduced by applying higher numerologies.  
In future works, we will focus on optimizing packet-switched Xhaul networks in dynamic network slicing scenarios with varying traffic loads. Also, we plan to study multi-layer packet-optical Xhaul transport network scenarios.  
This research was supported by National Science Centre, Poland, under grant number 2018/31/B/ST7/03456.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The author declares no conflicts of interest.
# Tensor Completion Based on Triple Tubal Nuclear Norm
## 2 Notations and Preliminaries
### Tensor Singular Value Decomposition
: _A tensor \(\mathbf{\mathcal{Q}}\in\mathbb{R}^{n_{1}\times n_{1}\times n_{3}}\) is orthogonal if it satisfies the following relationship:_  
\[\mathbf{\mathcal{Q}}^{\top}\ast\mathbf{\mathcal{Q}}=\mathbf{\mathcal{Q}}\ast\mathbf{\mathcal{ Q}}^{\top}=\mathbf{\mathcal{I}}.\]  
Based on the concepts defined above, the tensor singular value decomposition (t-SVD) and the tensor tubal rank are established as follows. **Definition 6** (Tensor singular value decomposition and tensor tubal-rank [31]). : _Any tensor \(\mathbf{\mathcal{T}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\) can be decomposed as:_  
\[\mathbf{\mathcal{T}}=\mathbf{\mathcal{U}}\ast\mathbf{\mathcal{S}}\ast\mathbf{\mathcal{V}}^{ \top}, \tag{4}\]  
_where \(\mathbf{\mathcal{U}}\in\mathbb{R}^{n_{1}\times n_{1}\times n_{3}}\) and \(\mathbf{\mathcal{V}}\in\mathbb{R}^{n_{2}\times n_{2}\times n_{3}}\) are orthogonal tensors and \(\mathbf{\mathcal{S}}\) is a rectangular f-diagonal tensor of size \(n_{1}\times n_{2}\times n_{3}\)._  
_The tensor tubal rank of \(\mathbf{\mathcal{T}}\) is defined to be the number of non-zero tubes of \(\mathbf{\mathcal{S}}\) in Equation (4), i.e.,_  
\[r_{tubal}(\mathbf{\mathcal{T}}):=\sum_{i}\mathbf{1}(\mathbf{\mathcal{S}}(i,i;\cdot)\
eq \mathbf{0}), \tag{5}\]  
_where \(\mathbf{1}(\cdot)\) is an indicator function whose value is one if the input condition is satisfied, and zero otherwise._  
The t-SVD is illustrated in Figure 1. It can be computed efficiently by FFT and IFFT in the Fourier domain according to Equation (3). For more details, see [1].