esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
With the continuous development of spatial augmented reality technology, various fields have begun to be affected by tremendous changes in technology. In the field of stage performance, the narrative form of augmented reality technology is an effective means of enhancing the expressive power of stage narrative. For example, real-time rendering of stage images by computer to achieve stage augmented reality [1], and projection-based augmented reality technology is also applied in actual performances [2]. The Disney(tm) theme park enhances the sense of space in the real world and the sense of reality of 3D objects, by projecting images onto the surface of real space, or actual objects, through projection-based augmented reality technology, and even achieves effective interaction between visitors and devices during the narrative process [3]. In order to achieve a better result of virtual-real fusion, the positioning of virtual elements on the stage is also an important point. In the existing augmented reality stage system, it is common to use the Microsoft(tm) Kinect sensor ([PERSON], WA, USA), Xsens (ENSCHEDE, The Netherlands), etc. to capture the actions of the actors on the stage, and transmit their position information [4; 5; 6]. Kinect's detecting range is within 4.5 meters however, so the scope of the actor's activity may exceed the capture range of Kinect in large-scale performances, which results in the loss of location information. At the same time Kinect placement may also interrupt the view of the audience.  
In the previous work of the authors, a positioning system based on infrared light emitting diode (LED) tracking was designed [7; 8]. However, as the module is usually hidden in the stage props, there is a limitation for performances with fewer props. In this paper, UWB positioning technology was utilized to resolve this issue, in which the Ultra-Wideband (UWB) tag could be easily hidden without props. The signal base station is arranged in the scene, and the position information is obtained through the signal sending between the antenna tag (with the STM32 single chip as the main control chip), and the signal base station. Next, the data is converted into 2D coordinates by a certain algorithm, which corresponds to the projection position of the antenna tag in the screen. The actors can trigger virtual effects dynamically and autonomously through a trigger module hidden in the costume. The system can present more precise positioning functions of the special effects in the stage performance. The higher applicability can also make the live performance more flexible and vivid. It could bring a more immersive visual stage experience to the audience.
# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 3 Methodology  
This section presents the details of the SG framework designed for IDSF, as illustrated in Figure 1. The framework incorporates various components and strategies, including a feature extractor, a label attention extractor, feature fusion, dynamic gates, and two separate classifiers for IDSF.
## 2 Basis and Fundamentals
### Some Evaluation Metrics  
To measure the qualities of image processing results, some metrics are often applied to calculate the evaluation values. The general evaluation metrics include but are not limited to mean square error (MSE), peak signal-to-noise ratio (PSNR), entropy (ET) and structural similarity (SSIM).  
The dissimilarity between the referenced image \(J(x,y)\) and the result image \(I(x,y)\) can be measured by MSE, which means the loss of image quality. Assume that the image size is \(n_{x}\times n_{y}\), MSE can be calculated as  
\[\text{MSE}(I,J)=\frac{1}{n_{x}n_{y}}\sum_{i=0}^{n_{x}}\sum_{j=0}^{n_{x}}[I(i,j) -J(i,j)]^{2}. \tag{8}\]  
The PSNR can be defined as  
\[\text{PSNR}(I,J)=10\log_{10}[\frac{L^{2}}{MSE(I,J)}]. \tag{9}\]  
Here, \(L\) means the gray level. The lower the MSE, the lesser the error, and MSE is inversely proportional to PSNR.  
The entropy is a measure widely applied in many fields such as data mining and image processing. A lesser entropy value implies lesser disorder. The common Shannon entropy (10) can be used to evaluate the disorder degree or variance of a result image:  
\[\text{ET}(I)=\sum_{i=0}^{L-1}H_{i}\ln H_{i}. \tag{10}\]  
Here, \(H_{i}=n_{i}/(n_{x}n_{y})\) means the gray ratio on the \(i\)th bin of the normalized image histogram.  
SSIM measures the image quality by computing the similarity between the result image and reference image. The SSIM value is limited in \([0,1]\) and a larger value indicates that the result image is more similar to the original one.  
\[\text{SSIM}(I,u)=\frac{\left(\mu_{I}\mu_{u}+k_{1}^{2}L^{2}\right)\left[2 cov(I,u )+k_{2}^{2}L^{2}\right]}{\left(\mu_{I}^{2}+\mu_{u}^{2}+k_{1}^{2}L^{2}\right) \left(\sigma_{I}^{2}+\sigma_{u}^{2}+k_{2}^{2}L^{2}\right)} \tag{11}\]  
where \(\mu_{I}\) and \(\mu_{u}\) represent the average pixels of the two images, \(\sigma_{I}\) and \(\sigma_{u}\) represent the standard variance of \(I\) and \(u\), \(cov(I,u)\) is the covariance, and constant \(k_{1}=0.01,k_{2}=0.03\).
## 2 SGX-Based TEE
### SGX Implementation  
There are currently two common ways to deploy SGX applications, namely SGX SDK-based and LibOS-based. The SGX SDK-based approach as shown in Figure 1a requires porting or re-building the program in C or C++ based on SGX SDK [32]. In addition, the legacy code needs to be partitioned into secure and insecure portions, which requires a lot of effort. SGX SDK-based implementation effectively reduces the trusted computing base (TCB) size and allows for selective placement of sensitive data in the EPC, thereby optimizing the use of EPC space. However, this approach also has some drawbacks. First, the cost of code porting is high, leading to poor usability. Second, since system calls are not supported within the enclave, frequent context switching is required, resultingin significant performance overhead. Finally, there is insufficient support for high-level languages, making programming in languages such as Python more difficult.  
To overcome these drawbacks, LibOS-based approaches are proposed and has attracted considerable attention. As shown in Figure 1b, by providing library operating system with support within the enclave, interactions between the enclave and the operating system are reduced, enabling the rapid deployment of unmodified applications in the enclave. Although the TCB size increases, the usability is significantly enhanced through a LibOS shim layer. Many solutions have been proposed based on LibOS. For example, Haven [33] pulls most of the application-supporting OS code into the enclave to reduce dependence on the underlying OS, andSCONE [34] integrates SGX with Docker containers, enabling applications to run securely within SGX enclaves without requiring modifications. Among these solutions, Gramine [35] (formerly known as Graphene) and Occlum [36] are widely used LibOS-based SGX implementations that perform well in terms of both performance and security. Gramine is an open-source LibOS that provides a flexible and lightweight environment for running unmodified applications securely within enclaves. Occlum is written in Rust at its core, providing more reliable security guarantees. This paper will focus on these two LibOS-based implementations, analyzing their performance and security.
# Bulk and Rayleigh Waves Propagation in Three-Phase Soil with Flow-Independent Viscosity
## 4 Numerical Examples and Parametric Analysis
Figure 10: Rayleigh wave propagation for different frequencies and relaxation times. (**a**) Wave speed. (**b**) Attenuation coefficient. Figure 9: S wave propagation for different frequencies and relaxation times. (**a**) Wave speed. (**b**) Attenuation coefficient. The increase in the relaxation time will influence the interaction between the microscopic particles of the material, and thus will change the wave speed and attenuation coefficient of the bulk and Rayleigh waves. The effect of saturation on the propagation behavior of bulk and Rayleigh waves can be attributed to the increase of saturation. The volume of liquid in soil pores increases, and thus the flow-dependent viscosity increases. It leads to the reduction of the wave velocity of elastic waves and the acceleration of attenuation. However, when the soil tends to be fully saturated, the mutual coupling of the liquid and gas phase disappears, resulting in the disappearance of the capillary effect and matric suction of the soil, so that the wave speeds of P\({}_{1}\) and Rayleigh waves suddenly increase and the attenuation coefficients suddenly decrease. The soil intrinsic permeability represents a quantitative property of porous material, depending solely on the pore structure of porous material. The larger the intrinsic permeability, the smaller the effect of flow viscosity on the propagation of bulk and Rayleigh waves, which will have a greater effect on its wave speed and attenuation coefficient. Figure 4:0 P\({}_{1}\) wave propagation for different intrinsic permeabilities and relaxation times. (**a**) Wave speed. (**b**) Attenuation coefficient. Figure 4:1 S wave propagation for different intrinsic permeabilities and relaxation times. (**a**) Wave speed. (**b**) Attenuation coefficient.
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
## 2 Single-Element Antenna
### Antenna Modeling Using Transmission Line
Substituting these impedances in the above equations results in the load impedance \(Z_{L}=8.82+24.24j\) and the ground impedance \(Z_{g}=-28.59j\). The obtained intermediate impedance \(Z_{\rm IT}\)is \(74.58-8.16j\) and the total input impedance \(Z_{\rm T}\)is \(55.91-21.63j\). The impedance graph from the numerical method is compared with the results of the FEM and circuit simulator shown in Figure 7b. The numerically estimated input impedance is close to \(50\ \Omega\); however, it shows slight capacitive impedance, and this is due to the shunt capacitance effect. Nonetheless, the modeled antenna reflects the \(50\ \Omega\) characteristics at \(30.4\ \rm GHz\). Figure 5:5 (**a**) Comparison of the reflection coefficient \(|\)S11\(|\) obtained from the electromagnetic solver and the circuit simulator. (**b**) The impedance comparison of the numerical method, electromagnetic solver, and circuit simulator.
## 3 Example Applications and Results  
To verify its performance, SMPO is applied in the optimization of the thermal design parameters of an atmospheric density measurement space telescope (called the ADST), which was designed and manufactured in China, and the optimized parameters are substituted into high-precision finite element software for verification. A comparison is made with the results of the current conventional method of relying on an engineer's experience to optimize the design using the solving of partial differential equations, and the superiority of the optimization framework is thus verified.
# Effect of Pole Head Profile on Torque Performance of a Disc-Type Multipole Magnetorheological Brake
## 1 Introduction
studied the multipole and dual-gap magnetorheological brake to find the impact of individual input currents on the active energizing surface areas on the rotor disk where the concentrated magnetic flux is generated [14; 15]. Axial and radial magnetic flux can influence braking output torque by increasing either the axial length or radial dimension of the rotating disc. [PERSON] et al. explained that by varying pole head radial distance, the flow of magnetic flux increased effectively and improved brake performance [16]. Structured models of brakes such as single-disk, dual disk, drum and hybrid types by maximizing the braking torque with the torque ratio higher than desired and by minimizing the mass of the brake were carried out by [PERSON] et al. [17]. With the compactness of the magnetorheological brake facilitating maximum braking torque, [PERSON] et al. proposed a design with a small radial dimension that results in large torque transmission capacity [18]. Magnets with varying shapes, coil winding gaps and shank dimensions can influence the magnetic flux captured by the stator [19]. In addition to the compact design, [PERSON] et al. studied the impact of shaft dimensions that intensify more efficient areas of magnetic flux across the MR fluid [20]. [PERSON] et al. discussed the new concept of axial flux in permanent magnets, which are mechanically activated to determine the magnetic field distribution at the midpoint of the shank, and [PERSON] et al. proposed a modification with built-in permanent magnets that increase the internal magnetic field intensity [21]. [PERSON] et al. studied the effect of the shape of the brake envelope such as rectangular, pentagonal and spline shapes to understand the flux flow [22]. [PERSON] et al. investigated special geometric shapes to the pole heads to gain the maximum torque and also investigated the effect of these pole shapes on the motor [23]. [PERSON] et al. explained that different geometric shapes of the pole heads have an impact on motor performance [24]. Until now, none of the studies have focused on the effect of magnetic pole head geometry on an MR brake's performance. Therefore, this work focuses on investigating the influence of pole geometries such as the shape, area, radial position of the pole head and corresponding shank dimensions on the MR brake's torque. Conventional pole heads such as circle or square have to be changed to trapezoid, pentagon and hexagonal pole head profiles to increase the working area of the brake. The ellipse pole head profile covers the maximum radial distance on the surface of the brake with a larger pole head area. These shapes also influence the flow of magnetic flux, which simultaneously affects braking torque.
## 2 Formulation of the Planning Problem
### Objective Function  
The objective function in (1) to be minimized is the total cost sustained for the installation of DESSs and for the operation of the LV network during the planning period:  
\[f_{obj}(\mathbf{x},\mathbf{u})=C_{op}(\mathbf{x},\mathbf{u})+C_{inst}( \mathbf{x},\mathbf{u}), \tag{5}\]  
where \(C_{op}(\mathbf{x},\mathbf{u})\) is the cost for the purchase of energy from the upstream grid over the planning period and \(C_{inst}(\mathbf{x},\mathbf{u})\) includes the cost for the installation and replacement of the DESS. Other cost items such as maintenance cost and replacement cost can be easily included within these two terms [42].  
The operation cost can be formulated as follows  
\[C_{op}(\mathbf{x},\mathbf{u})=\sum_{y=0}^{ny-1}\big{(}\frac{1+\alpha_{L}}{1+a }\big{)}\sum_{d=1}^{nd_{y}}N_{y,d}\Bigg{[}\sum_{k=1}^{nt}\big{(}\sum_{p=1}^{3} P^{p}_{1_{(y,d)}}\big{)}\Delta\ t\ Pr_{k_{(y,d)}}\Bigg{]}. \tag{6}\]  
This cost item depends on the price of energy and on the strategy adopted by the grid operator for the management of the connected DESSs including the way they are operated. Furthermore, this cost item refers to the case of the LV network that is not allowed to sell energy to the upstream grid. In the case the LV network can sell energy to the upstream grid, specific feed-in tariffs should be considered.  
The second term of the objective function is expressed as  
\[C_{inst}(\mathbf{x},\mathbf{u})=\sum_{i=1}^{ng}\Big{[}\big{(}IC_{DESS}+r_{i} RC_{DESS}\big{)}E^{size}_{DESS_{i}}\Big{]}, \tag{7}\]  
which depends on the size of the installed DESSs and on the need of battery replacement during the planning period. The latter is considered in the above equation by means of \(r_{i}\) which assumes value 0 or 1 depending on the requirement or not to replace the battery. It has to be noted that number of charging/discharging cycles and depth of discharge are factors influencing the need or not to replace the battery, as it will be shown later.
# Network Tower Sharing Analysis in Greece: A Structure-Conduct-Performance Approach
###### Abstract  
The paper intends to contribute to readers' comprehension of the Greek telecommunications market, focusing on the strategic decisions associated with network tower-sharing analysis in Greece. The Greek telecommunications industry is described for the first time following the Structure-Conduct-Performance (SCP) paradigm of Industrial Organisation (IO), as a methodological tool of analysis. In that respect, an SCP model in its extended form is constructed, aiming to examine how structure, conduct, and performance interrelate to each other. More precisely, the SCP model explains how strategic decisions regarding tower infrastructure sharing between 2013-2022 were developed, as a result of a series of interactions and feedback effects, amongst market structure, operators' conducts, and performances, resulting in strengthening competition and reshaping market structure with the entrance of a new player in the Greek mobile market, an independent TowerCo (Athens, Greece) in Greece. International tendencies and competition issues influencing domestic growth potentialities and alternative operators' concentration will be addressed, too. The paper concludes with presenting a basically qualitative, explanatory interpretive analysis of the perspectives of network tower-sharing analysis in the Greek telecommunication industry, including policy recommendations for the near future and thoughts on future research, as well.  
Structure-Conduct-Performance; network tower-sharing; Greek telecommunication industry; international competition; strategic decisions Article
# Reconstruction of Ancient Lake after Peat Excavation--A Case Study about Water Quality
## 2 Materials and Methods
### Peat Characteristics  
The drilling works included 33 exploration holes in near-surface areas (depths from 1.0 to 4.0 m) made using an INSTORF auger drill, documenting the quantity and quality of the deposit. During drilling of the holes, peat samples were taken (at every 1 m) and tested in a credited laboratory of Aquanet Ltd. in Poznan.  
Peat moisture in a large part of the Nienawiszcz deposit ranged from 79 to 84%, peat thickness varied from 0.4 to 3.4 m, water capacity was 420% on average, porosity was 8%, volumetric weight was 150 g/L, pH of 6.1-7.4, degree of decomposition was above 40%, and the ash content was significantly diverse, ranging from 11 to 39% (Table 1).  
Peat was mined using the opencast method and extracted from anaerobic layers, free of pathogens and nematodes, then it was used directly for the production of casing soil for growing mushrooms. The turf, useful for plant rooting, was composted and then became a component of horticultural soils used for the cultivation of flowers and rolled grass lawn.  
Below the Nienawiszcz peat layer, a calcareous gyttija was found, formed as a result of deposits of sediments in lake reservoirs. The gyttija consisted of three basic elements: organic mass of plant residues and plankton processed by benthic fauna, calcium carbonate precipitated from water or deposited biologically by plants and crustaceans and mineral deposit as a result of sedimentation. In the studied area, the level of CaO in calcareous gyttija was between 38 and 43%. Due to obtained environmental and mining permissions, gyttija was not removed after the excavation of peat.
# Modelling a Spatial-Motion Deep Learning Framework to Classify Dynamic Patterns of Videos
## 5 Experimental Evaluation  
### Experimental Setup  
To implement the proposed two-stream model, we used Keras [44], a high-level deep learning framework, which was built on top of TensorFlow. For building neural network models, Keras provides a scikit-learn type API, written in Python. Without worrying about the mathematical aspects of tensor algebra, optimization methods, and numerical techniques, researchers can easily use Keras to develop neural networks. Apart from that, in the area of deep learning, TensorFlow has gained much more momentum than other competitors: Theano, Torch, Caffe, and other well-known frameworks. TensorFlow can help to implement a multilayer model that is broadly used in voice recognition, image recognition, and text-based applications like Google Translate, and video classification. Furthermore, we executed our models with a Ge-Force GTX 1080-GPU machine with 32 GB RAM. Both two-stream networks were trained using Adam [49] optimizer with a momentum fixed to 0.9. To fine-tune the model, we gradually decreased the learning rate from \(10^{-2}\) to \(10^{-4}\) after 50 epochs and then to \(10^{-5}\) after 100 epochs. Also, dropout is applied to the visible and hidden layers with a ratio of 0.5 and 0.25, respectively, to overcome the problem of overfitting. To train two-stream networks, we used the adaptive gradient descent algorithm Adam to update to the RMSProp [50] optimizer, which is like RMSprop with momentum as well. To evaluate the performance, we trained the networks by scheduling the learning rate rather than the adaptive learning rate.  
To determine whether the model has converged or generalized, we analyzed graphs of its performance during the training with two common performance metrics of loss and accuracy. The loss metric gives a numerical estimate of how far the model is from producing the expected answers. The accuracy gives the percentage of the time that it chooses the correct prediction. Therefore, during this study, we used these similar evaluation methods to investigate the significance of the proposed method and compare the performance of previous works.
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
## 3 Heterogeneity-Aware Federated Optimization Framework
### Mechanisms for Measuring Heterogeneity  
In federated learning, the presence of non-independent and identically distributed (non-IID) data across clients poses significant challenges, such as a slow convergence and degraded model performance. To systematically quantify distributional differences among clients, we propose a multi-scale heterogeneity quantification mechanism that integrates local feature statistics with a manifold structural analysis. This dual-perspective metric supports both the modeling of heterogeneity and the subsequent optimization strategies.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 4 MASU Evaluation Using Simulations
### Simulations Setup
#### 4.1.4 Role Selection and Activation Methods  
The simulations consider Dynamic Centralized and Distributed role architectures. Therefore, we implemented the three methods defined in the framework for Role Selections and Activation in the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **AP and T2T** \\ \hline Control messages & UDP broadcast \\ Data messages & UDP broadcast \\ \hline
**Variable** & **MANET** \\ \hline Control messages & UDP broadcast \\ Unit detection messages & - \\ Data messages & UDP unicast \\ Routing protocol messages & UDP broadcast \\ HELLO interval & 2 s \\ Transmission control interval & 5 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Message setup for AP, T2T and MANET networks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Random Walk** & **Value** \\ \hline Maximum speed & 1.5 m/s \\ Maximum pause & 60 s \\ \hline
**SLAW** & **Value** \\ \hline Cluster ratio & 25 m \\ Maximum pause & 60 s \\ \hline
**Nomadic** & **Value** \\ \hline Avg. nodes per group & 4 \\ Group size deviation & 1 \\ Maximum speed & 1.5 m/s \\ Maximum pause & 60 s \\ Maximum distance & 15 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the mobility models of the nodes.  
Sensing and Control Tiers: (i) the manager decides; (ii) the leader election algorithm decides or (iii) the consensus algorithm decides. We implemented a distributed leader election algorithm based on the proposals presented in [35; 36]. Similarly, the distributed consensus algorithm used in the simulator was based on [37; 38].