esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background: Collaboration and Multimodal Learning Analytics
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Collaboration \&** & **Behavioral Indicators \&** & **Traces \&** & **Modalities \&** \\
**Constructs** & **Feedback Analytics** & **Feature Extraction** & **Sensors** \\ \hline \multirow{6}{*}{Cognition} & & BI: Quantity—Contribution through spoken interventions in the discussion & T: team member performs a spoken intervention & M: Audio, Non-verbal \\ \cline{3-5}  & \multirow{2}{*}{Contribution} & FA: Distribution of spoken interactions between team members & FE: Total Number and Duration of spoken interactions in the activity for each team number & **S:** Multidirectional microphone number \\ \cline{3-5}  & & BI: Criticality—Interpreting other team members’ contributions & T: Overlapping spoken interventions & M: Audio, Non-verbal \\ \cline{3-5}  & \multirow{2}{*}{Assimilation} & FA: Spoken interactions of each team member throughout the activity & FE: Duration and timestamp of spoken interactions of each team member throughout the activity & S: Multidirectional microphone \\ \hline \multirow{6}{*}{Metacognition} & & BI: Coverage—Tcam members interact with each other & T: Team member communicates with the other team members & M: Audio, Non-verbal \\ \cline{3-5}  & \multirow{2}{*}{Team Coordination} & FA: Sequence of spoken interactions between team members & FE: Trace of spoken interactions & S: Multidirectional microphone \\ \cline{3-5}  & \multirow{2}{*}{Self Regulation} & BI: Adapting team organization—An organized sequence of interactions needed for collaboration & T: Team members take turns to interact & M: Audio, Non-verbal \\ \cline{3-5}  & & FA: Spoken interactions of each subject during the activity & FE: Duration and timestamp of spoken interactions of each team member throughout the activity & S: Multidirectional microphone \\ \hline \multirow{6}{*}{Affect} & & BI: Reciprocal interaction—Team members have a positive attitude & T: Team members have an open attitude and non dominant behavior & M: Video, Non-verbal \\ \cline{3-5}  & \multirow{2}{*}{Cultivation of Environment during the activity} & FA: Body postures and hand gestures adopted during the activity & FE: body postures, hand gestures & S: Video camera (one for each team member) \\ \cline{3-5}  & & BI: Ownership—Participation in terms of spoken interventions in the discussion & T: Team members perform a spoken intervention & M: Audio, Non-verbal \\ \cline{3-5}  & \multirow{2}{*}{Integration} & FA: Distribution of spoken interactions between subjects & FE: Total Number and Duration of spoken interactions in the activity for each team member & S: Multidirectional microphone \\ \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Framework based on [21] for using MMLA in collaborative environments. Behavioral indicator (BI), Feedback Analytics (FA), Traces (T), Feature extraction (FE), Modalities (M), Sensors (S).
## 1 Introduction
This combined approach was applied on an idealized left ventricular phantom model with pediatric dimensions, as this has been demonstrated to evoke dispersive guided wave propagation patterns due to left ventricular geometry [23]. The proposed multiphysics model in this work thus adds an extra modeling layer to the previously presented SWE biomechanics model in [23], expanding our scope from studying the effect of _biomechanical_ factors on shear wave physics to investigating the effect of _imaging_ factors on shear wave physics. Our objective can be translated into two main study questions: (i) study the effect of compounding through comparison of single and compounded plane wave acquisitions from SWE experiments, for which more in-depth insights are realized by modeling both acquisitions using the multiphysics methodology, and (ii) study the effect of ultrafast imaging by analyzing the mechanical versus imaged shear wave acquisitions in the simulations. The study of each effect consisted of examining the shear wave propagation patterns in the time and frequency domain, and inspecting the accuracy of two different shear modulus estimation techniques, based on group and phase velocity, through comparison with the mechanically determined shear modulus.
## Preliminaries
### Twisted Edwards Curves  
The Edwards curves are a normal form of elliptic curves introduced by [PERSON] [28]. [PERSON] and [PERSON] [29] introduced Edwards curves defined by \(x^{2}+y^{2}=c^{2}(1+dx^{2}y^{2})\), where \(c,d\in\mathbb{F}_{q}\) with \(cd(1-dc^{4})\
eq 0\). In 2007, [PERSON] et al. [10] introduced twisted Edwards curves, which are a generalization of Edwards curves defined by  
\[E_{a,d}:ax^{2}+y^{2}=1+dx^{2}y^{2},\]  
where \(a,d\in\mathbb{F}_{q}\) with \(ad(a-d)\
eq 0\). The Edwards curves are a special case of twisted Edwards curves with \(a=1\). The point \((0,1)\) is the identity element and the point \((0,-1)\) has order two. The point \((1,0)\)and \((-1,0)\) have order four. The negative of a point \(P=(x_{1},y_{1})\) is \(-P=(-x_{1},y_{1})\). The ECADD operation of two points \(P=(x_{1},y_{1})\) and \(Q=(x_{2},y_{2})\) on a twisted Edwards curve \(E\) is defined as follows:  
\[(x_{1},y_{1})+(x_{2},y_{2})=\left(\frac{x_{1}y_{2}+y_{1}x_{2}}{1+dx_{1}y_{1}x_{ 2}y_{2}},\frac{y_{1}y_{2}-ax_{1}x_{2}}{1-dx_{1}y_{1}x_{2}y_{2}}\right).\]  
Because the addition law is unified, it can be used for computing the ECDBL operation. Suppose that two points \(P\) and \(Q\) have an odd order. Then, the denominators of the addition formula \(1+dx_{1}y_{1}x_{2}y_{2}\) and \(1-dx_{1}y_{1}x_{2}y_{2}\) are nonzero. Therefore, the doubling formula can be obtained as follows:  
\[2(x_{1},y_{1})=\left(\frac{2x_{1}y_{1}}{y_{1}^{2}+ax_{1}^{2}},\frac{y_{1}^{2}- ax_{1}^{2}}{2-y_{1}^{2}-ax_{1}^{2}}\right).\]  
Two relationships can be obtained by considering the curve equation: \(ax_{1}^{2}+y_{1}^{2}=1+dx_{1}^{2}y_{1}^{2}\) and \(ax_{2}^{2}+y_{2}^{2}=1+dx_{2}^{2}y_{2}^{2}\). After straightforward elimination, the curve parameters \(a\) and \(d\) can be represented by \(x_{1},x_{2},y_{1}\), and \(y_{2}\). Substitutions in the unified addition formula yield the addition formula as follows:  
\[(x_{1},y_{1})+(x_{2},y_{2})=\left(\frac{x_{1}y_{1}+x_{2}y_{2}}{y_{1}y_{2}+ax_{ 1}x_{2}},\frac{x_{1}y_{1}-x_{2}y_{2}}{x_{1}y_{2}-y_{1}x_{2}}\right).\]  
These addition and doubling formulas are used in the dedicated addition and doubling formulas described in Section 5. The features of these formulas are independent of the curve parameter \(d\)[30].
# Thermodynamic Analysis and Performance Evaluation of Microjet Engines in Gas Turbine Education
## 5 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Engine** & **FT** & **PJ** & **PE** & **h3t** & **ITt** & \(\Delta\)**hJ** & \(\Delta\)**hE** & **ITt.id** & \(\Delta\)**hJ.p.id** & \(\Delta\)**hE.id** & \(\Delta\)**hJ.id** \\ \cline{2-13}
**Model** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** \\ \hline P80-SE & 22.912 & 19.277 & 42.190 & 1039.296 & 93.887 & 78.993 & 172.880 & 114.497 & 83.605 & 198.102 & 64.734 \\ P100-RX & 33.076 & 21.259 & 54.335 & 1248.755 & 140.631 & 90.389 & 231.020 & 190.042 & 105.248 & 295.290 & 60.303 \\ P130-RX & 44.722 & 25.533 & 72.295 & 142.569 & 145.890 & 89.843 & 235.733 & 197.149 & 105.608 & 302.757 & 59.068 \\ P160-RX-B & 64.353 & 32.251 & 96.605 & 1127.453 & 166.278 & 83.332 & 249.610 & 221.704 & 91.371 & 313.075 & 41.155 \\ Pegasus HP & 63.455 & 34.318 & 97.773 & 1179.208 & 156.164 & 84.458 & 240.622 & 208.219 & 96.288 & 304.507 & 48.994 \\ P190-XV & 64.353 & 39.488 & 103.841 & 1198.369 & 165.954 & 101.830 & 267.784 & 218.360 & 112.414 & 303.0774 & 65.557 \\ P200-RX & 86.063 & 48.065 & 134.128 & 1199.415 & 187.603 & 104.774 & 292.377 & 246.846 & 115.783 & 362.629 & 63.217 \\ P220-RXi & 84.165 & 52.648 & 136.813 & 1250.528 & 183.103 & 114.539 & 297.642 & 240.926 & 129.283 & 370.209 & 78.269 \\ Olympus HP & 71.986 & 57.179 & 129.402 & 1323.616 & 156.264 & 124.639 & 280.903 & 202.940 & 142.946 & 455.886 & 101.657 \\ P300-PRO & 79.382 & 87.075 & 167.087 & 1411.256 & 154.715 & 170.938 & 325.653 & 193.394 & 192.821 & 386.215 & 159.647 \\ Titan & 118.998 & 113.489 & 232.487 & 1417.321 & 175.773 & 167.635 & 343.408 & 225.350 & 181.238 & 406.588 & 138.442 \\ P400-PRO & 117.664 & 114.639 & 232.302 & 1421.344 & 171.168 & 166.768 & 337.936 & 219.446 & 188.112 & 407.558 & 146.629 \\ P500-PRO GL & 148.484 & 131.466 & 279.950 & 1300.337 & 161.284 & 142.800 & 304.084 & 204.158 & 158.878 & 363.036 & 121.655 \\ P550-PRO GL & 157.199 & 158.876 & 316.076 & 1330.654 & 165.125 & 166.887 & 332.012 & 198.946 & 185.605 & 348.551 & 156.829 \\ Nike & 221.134 & 239.788 & 460.921 & 1398.775 & 172.536 & 187.091 & 359.627 & 210.410 & 205.232 & 415.642 & 173.269 \\ P1000-PRO & 286.36 & 329.019 & 615.379 & 1298.871 & 155.732 & 178.931 & 334.663 & 179.002 & 197.771 & 376.773 & 178.260 \\ Lynx & 436.807 & 480.813 & 917.620 & 1388.505 & 170.628 & 187.817 & 358.445 & 205.576 & 207.728 & 413.304 & 178.337 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The first series of data results, at take-off regime, for JetCat and AMT micro jet models.
## 3 Experimental Data and Results
### Usefulness of Camera-Based Optical Communications in the Detection Process
It should be emphasized that the variation in frames, the duration of exposure and the focusing time according to frames were analyzed. When the frame rate and the analysis process are linear, the processing is conducted smoothly, with the specification that autofocus is turned off. Starting with Android 5 (which allowed the camera app to be interfaced and programmed via an API), newer versions do not support full exposure control. There is a possibility for some devices that the secondary camera can be adjusted via an API through manual procedures. To have full access to cameras, devices must provide threads capable of enabling application mobility via the camera's secondary API. In the process of testing and implementation of the application, we used a smartphone device equipped with an image sensor with a nominal resolution of 9280 \(\times\) 6944 equivalent to 64 Mpx, with an optical format of 1/1.72\". The frame rate was e 21 fps; the Chroma type was Tetrapixel. In the case of pixel size, it had a size of 0.8 um and was of ISOCELL 2.0 type with a MIPI 4 Lane RAW interface, all benefiting from Super-PD autofocus. The operating system used was MIUI 12, a software version obtained by overlapping, which is based on Android 10, being one of the most stable versions available. The development of the Android application was conducted in the Android Studio Ultimate platform, for which the Java language was used to outline the functionalities in the first phase. The application will later benefit from an upgrade, including in terms of language, moving to Kotlin for much better versatility.
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
### Theory
#### 1.2.3 Pseudoinverse of the SRM  
The solution of the least-squares problem [40] for a system with linear equations (\(Y=A\cdot X\)) can be obtained using the pseudoinverse \(A^{\dagger}\) of the matrix \(A\)[41; 42; 43]:  
\[\|Y-A\cdot X\|_{2}\leq \left\|Y-A\cdot A^{\dagger}\cdot Y\right\|_{2}\to X_{argmin (|Y-A\cdot X|^{2})}=A^{\dagger}\cdot Y, \tag{4}\]  
Figure 1: (**a**) 3D Positron emission tomography (PET) scanner and computation of system response matrices (SRMs). (**b**) Axial SRM. A source placed in position \((z,w)\) in the rebinned data space, can produce a coincidence in a tube of response (TOR) \((z_{1},z_{2})\) of width \(\sigma\). The probability depends on the distance d from the source to the line of response (LOR). (**c**) Transaxial SRM. A source placed in position (x,y) in the field of view can produce a coincidence in a TOR \((\rho,\theta)\) of width \(\sigma\). The probability depends on the LOR-source d.  
The singular value decomposition (SVD) [44; 45; 46; 47; 48] was used here to compute the pseudoinverse of the matrix. In this method, matrix \(A\) is first decomposed into the product of two orthonormal matrices \(U\) and \(V\) and a diagonal matrix \(S\) containing the singular values (\(s\geq 0\)) of \(A\):  
\[A=USV^{T}, \tag{5}\]  
With that decomposition, the pseudoinverse \(A^{\dagger}\) of the matrix results:  
\[A^{\dagger}=VS^{\dagger}U^{T}, \tag{6}\]  
where the reciprocal singular values of the matrix \(S\) are given by \(S^{\dagger}\).
# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
## 2 Materials and Methods
### Building Classification Results  
Building classification is the first step in HF-ML localization and positioning. Only the generated data are used for classification. The results obtained by ML methods are presented in Table 6. The accuracy value in this table is provided for both the training and testing. The value given for the training is the average of the results obtained in the 10-fold cross-validation stage. The precision, recall, and F1 values obtained for the testing are calculated according to the distribution weights, since there are three different output values and these outputs are in different numbers in the dataset. According to the findings, the RF algorithm is considered to be the most successful algorithm in both training and test metrics. When the results are examined in terms of accuracy metrics, the RF algorithm produces a value of 0.993 in training and a value of 0.995 in the test. According to the results of the RF algorithm for the testing data, the confusion matrix is presented in Figure 5. In this figure, it is seen that the actual values and the predicted values match to a very high degree. Also, there are small numbers of confusion between close buildings.
# A Review of Prediction Methods for Ultra-Low Cycle Fatigue Damage of Steel Piers Under Earthquakes
## 2 Research Status and Development Trend at Home and Abroad
### Seismic Performance and Seismic Response Calculation Model of Steel Pier  
[PERSON] et al. [81] conducted an ultra-low cycle fatigue test on steel bridge piers. The material parameters of Q345 qC are provided in Table 2. \(e^{\prime}_{f}\) is the fatigue ductility coefficient; c is the fatigue ductility exponent. Figure 6 shows the specimens and test devices. A schematic of the weld of specimens is presented in Figure 7. Figure 8 presents the loading form of forced horizontal displacement. \(\delta_{0}\) is the loading strain, and \(\delta_{y}\) is the yield strain. In the test, the location of the crack initiation and cycle times of steel piers are analyzed. When a crack initiates, the bearing capacity does not decrease immediately but can withstand subsequent cyclic loads.  
bridge piers should extend beyond the crack initiation phase [39]. Existing seismic response models do not account for crack propagation effects, resulting in discrepancies between the predicted and observed horizontal load-displacement hysteresis and skeleton curves in the crack propagation stage [83]. The fatigue damage prediction limit (crack propagation length) under varying damage levels is closely tied to the seismic performance evaluation index and an accurate seismic response model for steel bridge piers. The following sections outline the current research and development trends in these areas.  
Figure 6: Specimens and test devices [81].  
Figure 7: Schematic diagram of the weld of specimens [81].
## 2 Description of Experimental Program
### \({}^{29}\)Si Mas Nmr  
The pozzolan reaction of the concrete specimens in this study was investigated using \({}^{29}\)Si MAS (magic angle spinning) NMR (nuclear magnetic resonance). The NMR can be used effectively to analysis the chemical bonds in materials. For the NMR analysis of concrete, solid-state \({}^{29}\)Si MAS NMR is used to avoid large peak broadenings caused by several nuclear interactions [21].  
As mentioned above, \({}^{29}\)Si NMR can be applied to investigate the silicate polymerization [22; 23]. A silicate tetrahedron having 1-4 shared oxygen atoms can be expressed as Q\({}^{1}\), Q\({}^{2}\), Q\({}^{3}\), and Q\({}^{4}\), respectively. The numbers 1-4 represent the number of shared oxygen atoms. In NMR spectra, the peaks of silicate tetrahedron having Q\({}^{1}\), Q\({}^{2}\), Q\({}^{3}\), and Q\({}^{4}\) are plotted at different chemical shift ranges. By integrating the NMR spectra at the respective chemical shift range, the fractions of silicate tetrahedron having Q\({}^{1}\), Q\({}^{2}\), Q\({}^{3}\), and Q\({}^{4}\) can be determined. The integration chemical shift ranges for Q\({}^{1}\), Q\({}^{2}\), Q\({}^{3}\), and Q\({}^{4}\) are considered in this study as \(>\)\(-\)75, \(-\)82 to \(-\)75, \(-\)95 to \(-\)82, \(-\)105 to \(-\)95, and \(<\)\(-\)105 ppm, respectively. Generally, for cement paste where sand and gravel are not included in the sample, Q\({}^{0}\) is observed in hydrated cement due to the remaining C\({}_{3}\)S and C\({}_{2}\)S. Q\({}^{1}\), Q\({}^{2}\), and Q\({}^{3}\) are detected due to layered structure of C-S-H. Q\({}^{4}\) represents the polymerization of quartz, and it is observed in silica rich products such as fly ash, silica fume, and nano-silica. Thus, by comparing the Q\({}^{4}\) before and after the pozzolan reaction, the degree of reaction can be estimated [12].  
In the case of concrete NMR analysis, the effect of sand must be carefully evaluated. Natural sand includes 80% of SiO2. For the spinning rate of NMR specimen by 1-35 kHz for several hours, solid-state NMR sample should be very fine. Thus, not all sand but some fine grain sand in concrete could be included in the sample. It might appear as Q\({}^{3}\) and Q\({}^{4}\) phase for OPC concrete. As a result, these Q\({}^{3}\) and Q\({}^{4}\) phases due to some fine grain sand must be removed to analyze the pozzolan blended concrete.  
In this study, the \({}^{29}\)Si chemical shifts were referenced with respect to tetramethylsilane Si(CH3)4 (TMS) at 0 ppm, using Si[(CH3)3]8 Si8O20 (Q8M8) as a secondary reference, with the major peak being at 11.6 ppm relative to the TMS. NMR spectra were acquired from finely ground powders at a magnetic  
Figure 1: Carbonation test specimens and measurement points.  
field of 9.4 T (Varian/Chemagnetics infinity spectrometer) corresponding to the Larmor frequencies 79.51 MHz for \({}^{29}\)Si. \({}^{29}\)Si experiments used 6 mm zirconia rotors spinning above 7.0 kHz, which was sufficient to concentrate essentially all signal components into their respective center bands.
## 2 Materials and Methods
### Components of the Control System
Laser interferometer systems provide highly accurate, non-contact measurements and are capable of working at long distances [12]. In addition, the use of plane mirrors as retroreflectors allows one to measure planar motion [13]. Two laser beams are needed to measure the displacement in the X- and Y-axes. In addition, one more beam is needed to determine the rotation around Z-axis. Thus, one laser head is placed projecting its beam in the X-axis, aligned with the reference system of the travel range, while the other two laser heads project their beams parallel to the Y-axis. The laser system components belong to the Renishaw RLE10 laser interferometer family, which consists of a laser unit (RLU), three sensor heads (RLD), two plane mirrors (one per axis), and an environmental control unit (RCU). Furthermore, an external interpolator is used to reduce the expected resolution of the system from 9.88 nm to 1.58 nm. Besides the readouts of the three laser encoders, the system also provides the readouts of the RCU sensors: Air temperature, material temperature, and air pressure. The measurement of each signal takes approximately 0.04 s; thus, the maximum speed at which it is possible to record the six measurements is every 0.25 s.  
In a previous work [9], an experimental setup, external to the NanoPla, was assembled for the development and experimental validation of the control system of one Halbach linear motor in 1D. In that setup, the magnet array of the motor was fixed, and the stator of the motor was the moving part, attached to a pneumatic linear guide. A control strategy was implemented in the DMC kit, and a 1D interferometer laser system was used as the positioning sensor. After that first validation, the four linear motors were installed in the NanoPla, and each of them connected to one DMC Kit for the project presented in this paper. In turn, all the DMC kits were connected to the host PC that coordinated the four motors. Movement was achieved while the moving platform was levitated by three air bearings. Figure 2a is a photograph of the NanoPla and the control system components, while Figure 2b illustrates a scheme of the connections between the host PC, the positioning sensors, the control hardware, and the linear motors. The input is the target position in the X, Y coordinates, which is entered by the user at the host PC. The control strategy is computed by the PC that receives the position feedback from the laser system. Then, the PC computes the phase voltages that must be generated for the control hardware to drive the linear motors that produce the movement. The plane mirrors are the moving target of the 2D laser system, and the magnet arrays are the part of the linear motors that perform the relative movement respective to the stator that is fixed. The plane mirrors, as well as the magnet arrays, belong to the moving platform.
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 2 Related Work  
### General Cooperative Edge Computing Approaches  
Various cooperative task computing approaches that utilize D2D task offloading to nearby end devices have recently been proposed in order to improve the MEC system performance in terms of energy efficiency [25; 26; 27; 28], time delay [29; 30], joint optimization of energy consumption and latency [31; 32; 33], IoT service enhancement [34], resource management [35; 36; 37; 38; 39; 40; 41; 42], as well as security [43; 44] and privacy [45].
# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
###### Abstract  
Monitoring ships on water surfaces encounters obstacles such as weather conditions, sunlight, and water ripples, posing significant challenges in accurately detecting target ships in real time. Synthetic Aperture Radar (SAR) offers a viable solution for real-time ship detection, unaffected by cloud coverage, precipitation, or light levels. However, SAR images are often affected by speckle noise, salt-and-pepper noise, and water surface ripple interference. This study introduces LCAS-DetNet, a Multi-Location Cross-Attention Ship Detection Network tailored for the ships in SAR images. Modeled on the YOLO architecture, LCAS-DetNet comprises a feature extractor, an intermediate layer (\"Neck\"), and a detection head. The feature extractor includes the computation of Multi-Location Cross-Attention (MLCA) for precise extraction of ship features at multiple scales. Incorporating both local and global branches, MLCA bolsters the network's ability to discern spatial arrangements and identify targets via a cross-attention mechanism. Each branch utilizes Multi-Location Attention (MLA) and calculates pixel-level correlations in both channel and spatial dimensions, further combating the impact of salt-and-pepper noise on the distribution of objective ship pixels. The feature extractor integrates downsampling and MLCA stacking, enhanced with residual connections and Patch Embedding, to improve the network's multi-scale spatial recognition capabilities. As the network deepens, we consider this structure to be cascaded and multi-scale, providing the network with a richer receptive field. Additionally, we introduce a loss function based on Wise-IoUv3 to address the influence of label quality on the gradient updates. The effectiveness of our network was validated on the HRSID and SSDD datasets, where it achieved state-of-the-art performance: a 96.59% precision on HRSID and 97.52% on SSDD.  
ship detection; SAR image; deep learning; remote sensing +
Footnote †: journal: Applied sciences