esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems
## 3 Joint Optimization
### Height and Elevation Optimization Subproblem  
Given the power variables \(P_{\text{G}}\) and \(P_{\text{U}}\), and the path loss variable \(PL\), the Equation (13) can be expressed as follows:  
\[\min_{\mathbf{H},\Theta}\ P_{e}, \tag{14}\]  
\[s.t.\ r\ =\ h/\tan(\theta)\leq r_{\max}|_{PL_{\max}}, \tag{14a}\] \[\theta_{\min}\leq\theta\leq\theta_{\max},\ H_{\min}\leq H\leq H_{\max} \tag{14b}\]  
This is a convex optimization problem, which can be solved by using standard convex optimization techniques. In order to achieve the local minimum BER, the descent direction is \(-\
abla P_{e}\), and the step size of the gradient descent process is set to \(v\) until the stop condition is met. A minimum BER threshold is also set \(\delta\), \(\delta\to 0^{-}\). When \(-\
abla P_{e}^{t}\geq\delta\), the minimum BER is considered to have been reached. There are optimal height \(h^{*}\) and elevation \(\theta^{*}\), and the corresponding BER is minimized, which can be obtained by searching \([h_{\min},h_{\max}]\) and \([\theta_{\min},\theta_{\max}]\) and satisfying \(\frac{\partial P_{e}}{\partial h}=0\) and \(\frac{\partial P_{e}}{\partial\theta}=0\).
## 3 Methodology
### Model Construction
#### 3.2.1 Teacher Model  
The teacher model employed in our system consists of two high-performing image classifiers: Swin Transformer and ResNet152V2. The Swin Transformer was developed as an advancement to the Vision Transformer (ViT). The major issue faced in vision transformers involves the quadratic time complexity associated with computing self-attention since each patch needs to look into every other patch to compute self-attention. Swin Transformer introduced a solution to this problem by integrating shifted windows and hierarchical transformer architecture. In the shifted window mechanism, images are divided into fixed-size windows. To compute self-attention, the patches in a particular window only focus on the other patches of that window, which dramatically reduces resource consumption. The shifted window mechanism also allows the architecture to gain a higher contextual understanding beyond a mere window-centric focus. The hierarchical block architecture, on the other hand, combines features across different scales. This mechanism significantly improves the classification performance, making Swin Transformer one of the most powerful image classifiers available [53].  
ResNet152V2 is a CNN architecture that is based on ResNet [54]. The backbone of this model is the residual connection. The skip connections used in residual blocks  
Figure 4: Change in retinal images after a series of preprocessing.  
allow the network to retain information extracted from previous blocks and propagate throughout the network. This also erases the vanishing gradient problem, which allows large model construction.  
As presented in Figure 5, the proposed teacher model is a fusion model made of Swin Transformer and ResNet152V2. The features extracted from both networks follow a batch normalization for smoother convergence. Since ResNet152V2 returns two-dimensional features, global average pooling (GAP) is employed for converting the 2D feature maps to the 1D feature vector. GAP was found to be a more efficient approach for this task than flattening. Finally, features from both networks are fused and passed to a dropout layer, where 50% of features are randomly dropped to regularize the model. A 50% dropout rate in the teacher model ensures that the model does not overfit in the highly imbalanced dataset. This learning is then transferred to the student model, eliminating the need for additional regularization. The dropout layer then follows a dense layer, which is composed of 256 neurons, and is activated with the SeLU activation function. SeLU is a simple yet effective activation function developed to address the dying ReLu problem [55]. The final layer consists of two and five neurons, with softmax activation for binary and multi-class classifications, respectively. The optimizer chosen for this task is Adamax, which provides smoother and faster convergence [56].
## 3 Framework
### Architecture  
Globally, the combination of TDMA/CA and SERENA works as follows:  
* SERENA (SchEduc RoutEr Node Activity) is a generic node coloring algorithm able to adapt to various application requirements and different environment/MAC layer constraints (e.g., unicast with/without immediate acknowledgement, broadcast). SERENA assigns colors to nodes in such a way that the number of colors used is small and two nodes having the same color can transmit simultaneously without interfering. More precisely, SERENA builds for any node \(N\) the set \(\mathcal{N}(N)\) of nodes that cannot have the same color as \(N\). Such a node would cause a collision making impossible for an intended destination to correctly receive its frame.
* The synchronization module receives a list of colors from SERENA. This synchronization module allocates time slots according to node colors, by means of beacons [24].
* TDMA/CA schedules the medium access according to the colors assigned by SERENA. It assigns one time slot per color. All nodes having the color corresponding to the current time slot are allowed to transmit. Any other node that has a one-hop neighbor of this color must be awake to receive its message. All the other nodes save energy by sleeping. In data gathering applications, we distinguish two types of cycles: those favoring upstream traffic, where colors are scheduled in decreasing order and those favoring downstream traffic, where colors are scheduled in the reverse order. The frequency of each cycle type is determined by the application constraints.  
Before detailing the functioning of SERENA and TDMA/CA, we present the requirements that must be met by their combination as well as the assumptions done by SERENA.
# Evaluation of AEP Predictions for Commercial Wind Farms in Sweden
## 2 Wind Turbine Production Data  
The data in this study come from the database Vindstat, which has been gathering production data, including downtime, from Swedish wind turbines since 1988 [14]. Vindstat data have previously been used for evaluating the wind turbine performance decline in Sweden [15]. Until 2016, most Swedish wind turbines were included but when the Swedish energy agency withdrew its financial support in 2017 and Vindstat introduced a member fee, several of the large wind turbine owners stopped reporting, limiting the included wind turbines to around half for the subsequent periods. Besides production data, Vindstat includes some general information about each wind turbine--for example, the predicted AEP as reported by the owner. This AEP was assumed to correspond to a normal wind year and comes from the wind energy calculation from the planning phase (i.e., the P50 value).  
For this study, all Vindstat reporting wind turbines installed after 2005 with a rated power of at least 1.8 MW were available, totaling 1162 turbines. The geographical spread of these turbines is illustrated in Figure 1. After excluding turbines according to the criteria described in Section 3.3, the number of wind turbines used in the results was decreased to that listed in Table 1. The locationsof the wind turbines have been divided using satellite imagery into three terrain types, open terrain, forest areas, and offshore, with the number of turbines in each category also presented in Table 1.
# VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
## 4 Experimental Results
### The Olivetti Face Database  
The 400 pictures in the Olivetti face database were collected in different places, with different light intensities and different emotional states of the same person (eyes open or closed, smiling or not smiling). Our experiment uses the first 20 people's pictures.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Algorithms** & **Clusters** & \multicolumn{8}{c}{**Ranking Results of the Detected 13 Features in Each Cluster**} \\ \hline \multirow{3}{*}{VSFCM} & Cluster1 & 12 & 6 & 9 & 11 & 5 & 4 & 2 & 8 & 7 & 3 & 1 & 10 & 13 \\  & Cluster2 & 4 & 13 & 6 & 5 & 10 & 8 & 2 & 12 & 11 & 1 & 9 & 7 & 3 \\  & Cluster3 & 10 & 12 & 5 & 7 & 6 & 8 & 1 & 13 & 9 & 11 & 4 & 3 & 2 \\ \hline \multirow{3}{*}{ESSC} & FRPCM & 10 & 2 & 13 & 12 & 11 & 7 & 3 & 6 & 9 & 5 & 8 & 4 & 1 \\ \cline{2-11}  & Cluster1 & 12 & 6 & 2 & 9 & 5 & 8 & 3 & 10 & 7 & 4 & 1 & 11 & 13 \\ \cline{1-1}  & Cluster2 & 9 & 13 & 2 & 4 & 3 & 7 & 5 & 12 & 6 & 11 & 8 & 10 & 1 \\ \cline{1-1}  & Cluster3 & 10 & 11 & 4 & 5 & 3 & 9 & 2 & 13 & 6 & 1 & 7 & 12 & 8 \\ \hline \multirow{3}{*}{SCAD1} & Cluster1 & 12 & 6 & 2 & 8 & 5 & 9 & 4 & 10 & 7 & 3 & 1 & 11 & 13 \\ \cline{1-1}  & Cluster2 & 9 & 13 & 2 & 4 & 3 & 7 & 5 & 12 & 6 & 11 & 8 & 10 & 1 \\ \cline{1-1}  & Cluster3 & 10 & 11 & 4 & 5 & 2 & 9 & 3 & 13 & 6 & 1 & 7 & 12 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Wine’s ranking results of the detected 13 features in each cluster.  
Moreover, Figure 8 shows the histogram of its running results. Moreover, from Table 7, we can see the clustering results of different relevant algorithms on this database.  
In Table 7, the CR value of our VSFCM algorithm on this data set can even reach 0.9850, while that of FRPCM, ESSC, SCAD1, DVPFCM, and V-FCM are significantly lower, which are 0.8300, 0.9450, 0.7500, 0.8350, and 0.6950, respectively. As can be seen from the histogram in Figure 8, the indicator values of our algorithm are obviously better than other algorithms. When processing Olivetti's face database, the indicators CR, NMI, and EARI of our algorithm are all close to 1. It reveals that the weight distribution of our algorithm plays an important role in the process of high-dimensional data (200 \(\times\) 1024), which includes reducing the weight of some less important dimensions while increasing the weight of relatively important dimensions to improve the accuracy and efficiency of the algorithm. Moreover, the proposed algorithm introduces the separation term between clusters to increase the distance between clusters, and the initial cluster centers obtained by using the cluster center initialization method CDCI are closer to the real ones, which prevents the VSFCM algorithm from falling into the local optimal value or iterative divergence.
# Reflectance Transformation Imaging as a Tool for Computer-Aided Visual Inspection
###### Abstract
Indeed, in visual inspection, the operator usually varies the configuration of the inspection scene, (i.e., the geometric configuration between the observer, the lighting, and the inspected object) in order to highlight possible appearance defects. It is important to highlight here that the fact that the RTI imaging technique transcribes, in a certain way, the visual inspection practices known by the operators in charge of these tasks, allowing a better and faster appropriation of the technique during its deployment in the industrial context. The acquired data obtained with the RTI technique enable building a local experimental model of the angular reflectance at each point of the inspected surface, which characterizes the appearance [25]. It is then possible to estimate the maps of local characteristics of surfaces, which are linked to the distribution of measured luminances or to the local micro-geometry [26; 27], and to reconstruct the visual rendering of the surfaces under a virtual lighting. The core objective of this paper is to evaluate the use of these relightings in an industrial context of inspection and assessment of the visual quality of surfaces, as a tool for computer-aided visual inspection, (i.e., to assist the experts and operators of sensory control). Studies in this sense have already been conducted, especially in the field of heritage [25; 28], to quantify the performance of the existing RTI reconstruction models in particular [29; 30]. This type of analysis evaluates the performance from a quantitative, numerical point of view but does not take into account the aspects related to human perception. The presented methodology aims to integrate these aspects and bring elements of an answer concerning the relevance of this approach in this context. Moreover, in addition to what has been presented in [29], the proposed methodology also integrates the dynamic aspects (whichare essential in the human perception mechanisms) not taken into account when only static RTI relighting is assessed. Thus, this paper aims to show how the RTI approach can be used and implemented for surface quality inspection tasks and how the parameters associated with the RTI process, both from the point of view of acquisition and modeling or reconstruction, can influence the surface appearance assessment and therefore the analyses and decisions concerning, for example, the acceptability of a surface. A brief overview concerning the Reflectance Transformation Imaging technique and the existing methods for the perceptual assessment of a sequence of images is presented in Section 2. The proposed methodology is detailed in Section 3. The major findings are presented and discussed in Section 4.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 6 Discussion  
The results obtained from Section 5 for the evaluation of the proposed OPMSS and benchmarked with the existing Kite solution proved that mobility support in the future Internet is feasible. The OPMSS solution is proposed to bridge the gap and support the smooth integration of NDN with 5G, WSN, IoT, and any other recent technology that requires mobility support upon integration with NDN. Our solution provides an avenue to rapidly update the local FIB tables when the content producer moves to a new location, thus ensuring the reachability of the mobile producer. The solution does not change the architecture of NDN but rather introduces a little functionality at the application level of the producer, the domain control router, and the expansion of FIB tables. The _MI_ packet and broadcasting strategy are implemented at the application and forwarding level of the NDN producer. Therefore, the routing plane of NDN is still maintained. Once the FIB tables are updated, the network uses the routing plane of NDN to create an optimal path to reach the new location of the mobile producer after handoff. The solution minimizes handoff latency, signaling costs, and unnecessary interest packet loss by providing support for seamless producer mobility.  
The differences between Kite and OPMSS solution are obvious, as Kite has as much as three additional data packets, such as trace data (_TD_), trace interest (_TI_) and consumer interest, while the OPMSS has mobility interest (_MI_) and consumer interest. The exchange of three different packets may result in high signaling in the network. In addition, the Kite has a rendezvous server (RS) or immobile anchor router for interest forwarding when the mobile producer changes to a new location. The OPMSS used broadcasting strategy to update the intermediate routers with the new routing prefix to prevent the incidence of single point of failure as a result of using RS or immobile anchor. Moreover, with the help of NDN data plane, broadcasting strategy and _MI_ packet, the OPMSS provides an optimal data path after handoff, which is not possible in Kite, as some data exchange passes through the RS or immobile anchor that causes path stretching, as shown in Table 3.  
Figure 10: (**a**) Throughput vs. simulation time in the grid scenario, (**b**) Throughput vs. simulation time in the Abilene scenario.
## 3 Methodology
### Coastal GNSS-Reflectometry Waveform Modeling  
The theoretical scattering model based on the bistatic equation and Kirchhoff approximation was developed by [PERSON] and [PERSON] and is known as the Z-V model which describes the averaged power waveform of the reflected signal consist of delay \(\Delta\tau\) and Doppler frequency offset \(\Delta f\) with respect to the nominal specular point [35]. It is widely used to simulate the delayed waveform of the reflected signal from the satellite, which can be expressed by:  
\[|Y_{0}(\tau)|^{2}=\frac{T_{i}^{2}P_{t}G_{t}\lambda^{2}}{(4\pi)^{3}}\int\frac{G_ {r}(\vec{r})\Lambda^{2}(\tau(r)-\Delta\tau)S^{2}(T_{i}(f(r)-\Delta f))}{R_{G}^ {2}(r)R_{R}^{2}(r)}\sigma^{0}(r)dr, \tag{7}\]  
where \(\vec{r}\) is the spatial coordinate on the sea surface, and \(T_{i}\) is the coherent integration time. \(P_{t}\) is the transmitted power from the satellite, and \(\lambda\) is the signal wavelength. \(G_{t}\) and \(G_{r}\) are the gains of the transmit and receiver antenna, respectively. \(R_{G}\) and \(R_{R}\) are the ranges between the ocean surface point and the satellite and receiver, respectively, \(\Lambda(\cdot)\) is the autocorrelation function of signal from the satellites. \(S(\cdot)\) is the frequency modulation function. \(\sigma^{0}(\cdot)\) is a bistatic radar cross section (BRCS) of the rough surface which depends upon the surface slope probability density function (\((P(\cdot))\)) and is expressed as:  
\[\sigma^{0}(r)=\pi|\Re|^{2}\frac{q^{4}(r)}{q_{z}^{4}(r)}P\left(-\frac{q_{\perp} }{q_{z}}\right), \tag{8}\]where \(\Re\) is the Fresnel reflection coefficient. The Mean Square Slopes (MSS) of the up and cross winds (\(\sigma_{u}\) and \(\sigma_{c}\), respectively) are approximated based on the calibrated sea surface roughness model developed by [PERSON] (1966) and are calculated as follows:  
\[\sigma_{u}=0.45\cdot[0.00+0.00316\cdot f(U)], \tag{9}\] \[\sigma_{c}=0.45\cdot[0.003+0.00192\cdot f(U)],\]  
where \(f(U)\) is related to the in situ wind speed measurement (\(U\)) and can be calculated as follows:  
\[f(U)=\begin{cases}U,&0<U\leq 3.49,\\ 6\ln(U)-4.0,&3.49<U\leq 46,\\ 0.411\,U,&46<U.\end{cases} \tag{10}\]  
This work focuses on the wind speed at the height of 10 m above the sea surface which is the default value for the anemometer height. The relationship of the wind speed measured at different heights ([PERSON], 1966) can be expressed as:  
\[U_{2}=U_{1}(z_{2}/z_{1})^{P}, \tag{11}\]  
where \(U_{2}\) is the wind speed at the desired reference height \(z_{2}\), and \(U_{1}\) is the wind speed measured at height \(z_{1}\). A value for the exponent \(P\) is \(0.11\) was determined to be applicable most of the time over the ocean.
# Fusion of Telecommunications and IT Services Boosted by Application Programming Interfaces
## 5 Conclusions  
Telecommunication networks have gone through a drastic evolution whereby the business logic and the service logic have been separated, as we can see through the Nokia TAS API service examples. The originally developed INAP and CAMEL services are reaching their end of life and are being replaced with native services implemented as an IMS AS or with API-based services. Moreover, the IN replacement, API-based services, also enable faster time to market thanks to a bigger developer base, and the non-Telco-specific service environment unlocks the possibility of easily integrating the Telco and IT domains. However, future trends are moving to device- and access-agnostic directions, where services are orchestrated as shown in Figure 9. Future services may be connected to IT/IoT services and are getting more and more interactive, as we illustrated with IMS DC-related examples.  
**Author Contributions:** Conceptualization, M.A.T. and Z.S.; methodology, M.A.T., Z.S. and A.H.; writing--original draft preparation, M.A.T., Z.S. and A.H.; visualization, Z.S. and A.H.; supervision, A.H. and G.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** The authors acknowledge the valuable discussions with [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] (at Nokia CNS), and with [PERSON], [PERSON] and [PERSON] (at Nokia Bell Labs), and their useful comments.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Numerical Methodology and Procedure
### Computational Domain  
Figure 5 illustrates the model subdivided into several key regions according to different basin properties as follows: the upper cavity, lower cavity, inlet flow channel, outlet flow channel, and the fluid within the piston's inner flow channel. In order to increase the calculation accuracy and reduce the calculation time, the grid of each basin is divided into the hexahedral grid under a fine design, and the total number is 1.795 million. A circular cylindrical, penetrating layer with a thickness of 0.01 mm is introduced between the channels to ensure the convergence of the fluid continuity equation during computation.  
While the piston moves, the fluid within the upper and lower cavities experiences either compression or tension. The lower end face of the upper cavity and the upper end face of the lower cavity serve as the load-bearing surfaces. The fluid in the piston's flow channel is associated with a moving grid, while the upper and lower cavities are represented by deformation grids. The fluid within each domain synchronizes with the piston's movement. The surface grids within each region are designated as the stress surface, deformation surface, wall, interface, inlet, and outlet. Ideal gas is used to simulate the internal fluid of the piston, whose parameters are shown in Table 2.  
In the process of grid independence verification, the fluid domain was divided according to different mesh sizes, and the following four different mesh numbers were obtained: 553,000, 806,000, 1.201 million, and 1.795 million. Then, the variation trend of the final impact velocity of the plunger piston under different mesh numbers was analyzed. As shown in Figure 6, different mesh numbers have certain effects on the final impact velocity and impact frequency of the hammer. When the mesh number is close to 1.795 million, the final impact velocity of the piston and the time required to reach its velocity are very close, and the calculated results tend to be stable. When the number of grids reaches about 1.8 million, the effect of the number of grids on the calculation results is almost negligible.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Density/** & **Cp/** & **\(\lambda\)/** & **Viscosity/** \\
**kg/L** & **J/(kg K)** & **W/(m K)** & **kg/(m s)** \\ \hline \(1.0\times 10^{-3}\) & 1006.43 & 0.0242 & \(1.7894\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 2: Fluid physical parameters.  
Figure 5: Grid system of the model and associated profile.