esa-sceva/satcom-chunk-collection huggingface dataset:
# A Communication Framework for Image Transmission through LPWAN Technology
## 2 Related Work
In the transmission test, an image can be completely sent with a number of information packets between 8 and 10. The system operates in a range of 1800 m without packet loss. This results are obtained when at least 50% of the original information is needed in order to recover the image without considerable losses. The work in [18] presents a medium-access mechanism to implement an image sensor with LoRa technology. An adapted Carrier Sense Multiple Access (CSMA) mechanism is presented for avoiding packet collision and packet losses. The mechanism is combined with a shared active time frame schedule technique to mitigate duty cycle issues. Thus, a high trade-off between image size and visual quality is presented. This feature gives the possibility to transport an image size between 900 to 1200 bytes, which can be encapsulated into five LoRa packets. The work in [16] proposes a low-power wide-area network protocol, which combines the LoRa modulation technique with embedded microprocessor technology. The proposal network is composed of three LoRaWAN nodes that provide three physical channels. The camera node allows bidirectional communications where each uplink transmission is followed by two short downlink windows. When the camera node needs to upload the data, it randomly selects a channel to execute channel activity detection. The proposal only gives a theoretical analysis without real testbed implementation. The work in [76] presents a method to transmit continuous images through LoRa. The samples are taken from a camera in a static environment where the critical challenge is to reduce the amount of transmitted data while preserving the image quality. The image-splitting technique consists on the use of grid patches that are only transmitted when there is an image modification based on a dissimilarity measure. The implementation and evaluation were on Raspberry Pi for data compression and an Arduino Uno with LoRa shield for data transmission. The work in [77] aims to review the available methods applied to transfer images via LoRa infrastructure. The limitations of each method are pointed out, and the challenges that need to be managed in the future are also defined toward establishing a reliable image transfer over a LoRa network. A review of the LoRa approaches toward transmitting visual data was presented. Only few methods explore the modification of the LoRa physical layer to overcome the packet collision problem, and few methods deal with the compact image representation. Table 1 presents a summary of the methods found in the literature for image transmission through LoRaWAN, where parameters such as transmission time, number of packets, and the employed image compression method are presented.
# Two-Phase Flow Simulations Using 1D Centerline-Based C- and U-Shaped Pipe Meshes
## 2 Numerical Methods
### Benchmark Case  
This section presents the benchmark cases for validating CFD results. A C-shaped curve pipe with an upward injection (Figure 4A) was used to evaluate the interaction between the bubble and the liquid in the internal flow. In the benchmark case, water was used for filling an initial computational domain. The pipe dimensions were 560, 28, and 125 mm for \(L\), \(D\), and \(R\), respectively (see Figure 2), consistent with the experimental device [19]. Air bubbles and water were uniformly injected from the whole inlet face with 0.94% air volume fraction. To be similar with the experimental condition, the inflow velocity magnitudes were set to 1.43 m/s for water phase, and 0 m/s for air phase [19], resulting in a Reynolds number of 40,000 for the water phase. The Re was computed as  
\[Re_{w}=\frac{\rho_{w}U_{w}D}{\mu_{w}} \tag{7}\]  
where \(\rho_{w}\), \(U_{w}\), \(D\), and \(\mu_{w}\) are the water density, average velocity magnitude of water, diameter of the pipe, and dynamic viscosity of water, respectively.  
After validating the velocity behaviors with the experiment, the inflow velocity magnitudes were set to 1.43 m/s for both water and air phases. This leads to a superficial velocity of 1.416 and 0.0134 m/s for water and air phases, respectively. We then performed two additional simulations with air volume fractions of 2% and 3%, resp  
Figure 4: Gravitational conditions for cases (**A**) 1, (**B**) 2, (**C**) 3, and (**D**) 4.  
Figure 3: Generation of (**A**) geometry, (**B**) uniform mesh, and (**C**) non-uniform mesh.  
of volume fraction in the two-phase flow through 180 bends. The superficial velocities of water and air phases for 2% were 1.401 and 0.028 m/s, respectively; those for 3% were 1.387 and 0.043 m/s, respectively. No-slip boundary conditions were imposed at all walls. A zero-gradient velocity condition and a constant pressure were applied at the outlet. The detail numerical settings and boundary conditions are listed in Table 1. The fluid is injected to the pipe inlet with the uniform velocity (\(\textbf{u}_{\text{face}}=\textbf{u}_{\text{ref}}\), where ref is the reference value) and the air fraction of 0.94%; the velocity at the outlet is extrapolated to the patch from the nearest cell value, and thus its gradient is equal to zero in direction perpendicular to the outlet (\(\frac{\partial\textbf{u}}{\partial\textbf{n}}=0\), where **n** is the unit outward normal vector on the boundary). The direction of gravity was \(-\)z (Figure 1) with 9.81 m/s\({}^{2}\) magnitude. A two-phase fluid was injected at the inlet (i.e., the start of the upper horizontal pipe). It then went downside through the curve pipe and emitted at the outlet at the end of the lower horizontal pipe (Case 1, Figure 4). For a quantitative validation, the axial velocities of the fluids were compared at angles of 0\({}^{\circ}\) (curve inlet), 90\({}^{\circ}\) (curve center), and 180\({}^{\circ}\) (curve outlet) of the curve region.
## 5 Conclusions  
* The largest ME voltage coefficient of \(\approx\)7.85 V/A was measured on the fundamental resonance frequency of bending oscillations in an MAE-PEP cantilever comprising an MAE layer with an intermediate thickness of \(\approx\)2 mm.
* For three samples with thinner MAE layers, there was an optimum magnetic field where the magnetoelectric voltage coefficient at resonance frequencies \(f_{\rm r1}\) and \(f_{\rm r2}\) had a maximum. In the L-T-configuration, the mechanism of the resonance enhancement of the MAE voltage coefficient seemed to be the same as in conventional structures containing metallic FM layers.
* The softness of constitutive materials led to a significant reduction in the resonance frequency of bending oscillations in the absence of a magnetic field down to about 23 Hz.
* The resonance frequencies of the MAE/PEP heterostructure increased with an increase in the bias magnetic field. The change of the resonance frequency of the lowest bending mode in the bias magnetic field of \(\approx\)240 kA/m could reach approximately 200%.
* The hysteresis behavior of the ME voltage coefficient and the resonance frequency on the applied constant magnetic field was observed for the second lowest resonance frequency of bending oscillations.  
Conceptualization, M.S. and L.Y.F.; methodology, D.V.S., G.G. and I.A.B.; validation, D.V.S., L.Y.F. and M.S.; investigation, D.V.S. and V.O.B.; resources, I.A.B., G.G. and L.Y.F.; data curation, D.V.S. and L.Y.F.; writing--original draft preparation, M.S. and D.V.S.; writing--review and editing, M.S., D.V.S. and L.Y.F.; visualization, D.V.S.; supervision, L.Y.F. and M.S.; project administration, L.Y.F. and M.S.; funding acquisition, L.Y.F. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Foundation of Basic Research, grant number 20-32-90-190, and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project number 466920132. I.A.B. was supported by The State Conference of Women and Equality Officers at Bavarian Universities (LaKoF Bayern).  
The authors declare no conflict of interest.
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 4 Methodology
### Data Augmentation  
The measurement data are composed of real and reactive power measurements from the load buses, voltage measurements from choice buses following optimal measurement device placement results presented by [39], and some power flow measurements for the AC subgrid and current measurements for the DC subgrid. It is also assumed to be transmitted through a noisy channel that has additive Gaussian noise. The mean and variance that the noise introduces is influenced by the choice of measurement devices as specified in Section 2. To populate the dataset, random values are acquired that follow the normal distribution of the measurements with sample mean, \(\mu=0\), and standard deviation, \(\sigma=\{1\%,\ 3\%,\ 15\%\}\). In MATLAB, this is realized by the normrnd() function. To ensure that the distribution is well represented, normrnd() is arbitrarily set to generate \(n_{a}=1000\) samples. Therefore, for each measurement \(z_{t}\) at time \(t\), the noise, \(e_{i}\), is a random sample generated by normrnd()  
Figure 8: The test system to perform state estimation on. IEEE 34-bus test system to serve as the AC subgrid with the IEEE 9-bus test system at the far end of the IEEE 34-bus network to serve as the DC subgrid.  
following the ziggurat rejection algorithm proposed by [40]. The augmented data are mathematically shown in Equation (17).  
\[Z=\begin{bmatrix}z_{0}\\ z_{1}\\ \vdots\\ z_{T}\end{bmatrix}\rightarrow\forall\,z_{t}\in Z,\,z_{t}=z_{t}+\begin{bmatrix} \epsilon_{0}\\ \epsilon_{1}\\ \vdots\\ \epsilon_{n_{a}}\end{bmatrix} \tag{17}\]  
The next step is preparing the data for neural network training. For supervised neural network training, the data are fed in the format of (inputs, targets), or mathematically, \((x,y)\). Recall that the reason why data are augmented, aside from lack of actual data, is due to the modeled uncertainty from the measurement device of choice. So, when forming the input-target pairs, the inputs encompass the whole augmented dataset, while the targets come only from the pseudo-measurement set. Mathematically, this is shown in Equation (18).  
\[\begin{bmatrix}z_{t}\to y_{t}\\ z_{t}+\epsilon_{0}\to y_{t}\\ \vdots\\ z_{t}+\epsilon_{n_{a}}\to y_{t}\end{bmatrix} \tag{18}\]  
This tells the neural network that the measurements will be noisy; thus, the network must learn to account for the specified uncertainties. The data are also transformed such that the training process is regularized: a process to prevent the neural network from overfitting into the small dataset which results in generalization failures, especially when deployed.
# Improved DRUS 4H-SiC MESFET with High Power Added Efficiency
## Device Structure and Simulation Method and Fabrication Feasibility
### Simulation Method  
Two dimensional numerical device characteristics are realized with ISE-TCAD. The simulator is calibrated with experimental data in the micrometer regime [15], and the agreement between experimental data and simulation results is obtained as shown in Figure 2. In order to accurately simulate the electrical characteristics of the 4H-SiC MESFET device structure, the main physical models used are mobility (Enormal, Doping Dep, High Field saturation (GradQuasiFermi)), recombination (Auger, SRH (DopingDep)), incomplete ionization, effective intrinsic density (Band Gap Narrowing (OldSlotboom)). The DC and AC parameters of the device are obtained by ISE-TCAD simulation, and these parameters are input into the ADS software to simulate the power added efficiency of the device. In the ADS simulation, the model used is EE-FET3, an empirical analytical nonlinear model used to fit the electrical properties of MESFETs. And the working bias conditions were set as follows: \(V_{\text{gs}}\) was \(-3.5\) V, \(V_{\text{ds}}\) was \(28\) V, RF was \(1.2\) GHz and input power Pavs was \(24\) dBm.
## 4 Multicast Routing Approaches in DTN
### Social-Aware Multicast Routing in DTN
#### 4.1.1 Contact Probability
Though the second level can make a more accurate relay selection, it results in higher computing overhead compared to the first level. TLMR maintains a balance between the computing overhead and the transmission rate. Social Differences Multicast Routing: Different from typical social-based routing, Social Differences Multicast Routing (SDMR) [37] forwards multicast bundles to nodes with larger social differences. SDMR relay selection is based on the following two social difference metrics: Similarity difference and centrality difference. The former refers to the difference between the two encountered nodes to the bundle destination, and the latter refers to the difference between nodes to all the nodes in the network. A greater similarity difference increases the ratio of the bundle delivery, while a higher  
Figure 7: A cloud-based multicasting scenario in DTN [35]. centrality difference expands the data coverage. In the SDMR scheme, data are transmitted with minimum cost, without compromising the delivery ratio and round-trip time. Such an efficient scheme, according to [PERSON] and [PERSON] [37], is well suited for DTNs with limited energy and buffer capacity.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 5 Future Research Directions
### IoT and Cloud Integration  
The integration of IoT and cloud computing with indoor localization systems offers vast potential for real-time data collection, storage, and analysis. Future research should focus on developing efficient cloud-based platforms that support predictive analytics, automated decision-making, and remote monitoring [23]. For example, IoT-enabled RFID and UWB systems can provide real-time tracking of assets and patients while offering advanced analytics to optimize hospital workflows and industrial operations [17].
# Deep Learning Models for Yoga Pose Monitoring
## 3 Proposed System
### Pose Correction  
After the predicted pose is classified to be correct, with respect to the chosen pose, the user is provided with the appropriate feedback and in following, the similarity percentage (using cosine similarity) is calculated to be presented to the user.  
For all the six yoga poses present in the dataset, which will be introduced below, important and critical angles have been identified and rules have been formulated for each pose. For each rule, a threshold is set, which constitutes the maximum deviation allowed for the user from the standard pose. If the user exceeds this threshold value, a corresponding feedback is given accordingly in the form of text and speech. The angle between two keypoints can be found out by calculating the tangent inverse of the slope with positive \(X\)-axis. Equation (11) depicts the formula for finding the angle, given the two coordinates of the keypoints:  
\[\Theta=tan^{-1}(\frac{y_{2}-y_{1}}{x_{1}-x_{2}}) \tag{11}\]  
where \((x_{1},y_{1})\) and \((x_{2},y_{2})\) are the coordinates of two keypoints.  
Feedback is initially obtained in the form of text, which is then converted into speech using Pyftsx3 library [41]; it is a text-to-speech converter and works offline as well.  
Cosine similarity, which is a measure that compares two vectors by calculating the cosine of angles between them [42], is also reported to the user. The value of this metric varies from \(-1\) to \(+1\). If the similarity score lies between \(-1\) and \(0\) then it is multiplied with \(-1\) to transform it to positive side. Then, the similarity is calculated according to the score in the range \(0\) to \(+1\). The mathematical formula of cosine similarity is given in Equation (12):  
\[cos\theta=\frac{A\cdot B}{\left|\left|A\right|\cdot\left|B\right|\right|} \tag{12}\]where, \(A\) and \(B\) are two vectors in a multidimensional space.  
In this work, cosine similarity is calculated between keypoints of the user's pose and the standard pose. This way, this measure reveals the degree of closeness to the actual pose. Since the distance of different users can vary based on their position from the camera, all keypoints are initially normalized for determining a similar scale for all of them.
## 2 The FOWT-SFFC Concept
### Merits of FOWT-SFFC Concept  
A number of advantages are achieved by integrating the steel fish-farming cage with a floating offshore wind turbine. Firstly, as the copper alloy nets are fitted onto the steel members, unlike conventional HDPE cages, the nets of FOWT-SFFC will experience very small deformation during fish culturing. Therefore, a constant water volume favorable for fish growth is created in cage in low and moderate sea conditions. Even in a harsh environment, the change in water volume would be caused by cage motions and fluctuating water surface, instead of deformation of nets. Secondly, as fish is protected by steel cage, fish farming becomes much safer than by HDPE cages. Considering the huge water volume in cage, the majority of economic profits of the floating system will be contributed by fish farming, rather than by power generation. In turn, the payback period of heavy investment is greatly shortened which will promote the confidence of the investors in offshore wind power. Meanwhile, wind power supplies electricity for aquaculture use, such as lighting, machinery and monitoring. Thirdly, the tower and turbine can be fully integrated with the cage at quayside during fabrication. After construction, the FOWT-SFFC is towed to its operational site using a tugboat or can be transported by a semisubmersible vessel. Note that the wet tow operations have been successfully carried out for giant Condeep concrete oil platforms from the 1970s to the 1990s [40] and recently the dry transportation of Ocean Farm 1 has been fulfilled by a semisubmersible vessel from Qingdao, China to Frohavet, Norway [41]. At quayside, use of buoyancy cans is an option to reduce the draft of the whole system before tow. It means that for construction the water depth of quayside may not necessarily exceed the draft. It is obvious that the on-site installations merely involve deploying mooring lines and power cables, saving substantial marine operation cost. In addition, owing to the small diameters of cage columns (Table 1), the wave force on the substructure is significantly reduced. What is more, due to the living quarter at the top of the steel cage where the fish-farming and turbine maintenance personnel live, the fish culture and maintenance of wind turbine can be conducted without external help and personnel need not board the platform frequently. If needed, the shuttle boat can dock near side columns. The personnel can reach living quarter by ladders fixed at side columns and then by pathways along top ring and radial braces (Figure 2). The above merits of FOWT-SFFC secure its competitiveness in the offshore wind power and aquaculture markets.
# A Novel Analytical Approach for Spacecraft Fly-Around Formation Design with a Low-Thrust Maneuver
###### Abstract
[15] derived a non-linear dynamical model for spacecraft formation systems based on the circular restricted three-body problem (CR3 BP) and proposed a hybrid continuous low-thrust propulsion scheme combining solar sail and Coulomb force propulsion. [PERSON] et al. [16] analyzed satellite motion characteristics under continuous low-thrust conditions using Hamilton functions, establishing optimal transfer mathematical models for velocity-inclination and velocity-RAAN (Right Ascension of the Ascending Node) relationships, thereby providing continuous maneuver strategies for small satellites. To address long-term formation maintenance, [PERSON] et al. [17] developed a continuous low-thrust control strategy for loose displacement formations by transforming optimal control problems into nonlinear programming frameworks. [PERSON], [PERSON] [18] compared electric, chemical, and cold gas propulsion systems for formation flight and end-of-mission deorbiting, demonstrating the advantages of resistojet thrusters in low-altitude orbits. Additionally, [PERSON] et al. [19] proposed analytical natural circumnavigation formations for dual-spacecraft systems, enabling periodic revisits to designated points and their vicinity. In recent years, micro-nano satellites have been widely used in space missions [20; 21], such as deep space exploration [22; 23; 24], technology demonstration [25], etc. In these tasks, continuous low-thrust technologies such as cold gas propulsion and electric propulsion, which can produce thrust ranges from uN to N levels, have played important roles. Electric propulsion systems, on the other hand, can achieve great improvement in specific impulses (Isp) over chemical propulsion, and will play a greater role in the future. Thus, this article focuses on the design of fly-around formation based on the low-thrust maneuver. The purpose of this paper is to develop an intuitive, analytical, and systematic guidance law for fly-around formation design based on low-thrust control. Throughout this study, the relative motion dynamics are modeled under the classical two-body problem assumption. The outline of this paper is as follows. In Section 2, the coordinate systems, relative dynamics, and analytical guidance law are introduced. In Section 3, three kinds of fly-around trajectories are discussed in detail, including spatial ellipses and circles, and numerical simulations are conducted to evaluate the influence of design parameters on the size, shape, and period on the fly-around trajectory. The extension to the design of planar fly-around formation and fuel consumption is also analyzed. Conclusions are drawn in Section 4.
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 2 Corrosion Detection Method of Substation Grounding Grid
### Surface Electromagnetic Field Method
The electromagnetic field method is much simpler than the electric network method in terms of working mode and fault diagnosis characteristic analysis. Moreover, the operability is significantly improved, but the method still has many problems: The noise from the substation is very large, which will affect the judgment accuracy. Moreover, the injection current position is difficult to normalize as it is easily affected by the injection down-line. In addition, the layout of the down-line has not yet been thoroughly investigated. Given the soil dispersion characteristics, the optimal injection frequency and waveform shape are not clear. Moreover, research on the electromagnetic field distribution in the case of the uneven distribution of grounding conductors and complex soil resistivity is still underdeveloped, and the transformer, cable trench, bracket, and other electrical equipment can be easily influenced.