esa-sceva/satcom-chunk-collection huggingface dataset:
# An Eccentricity Based Data Routing Protocol with Uniform Node Distribution in 3D WSN
## 2 Related Works
It considers the residual energy of nodes during CH nodes election like as EADEEG and forms clusters, which is similar to LEACH. Besides, the authors extended the protocol for single-hop to multi-hop communication. To select a route towards the BS, each CH node broadcasts a route message within the radio range \(R_{\text{r}}=2R_{\text{c}}\). It selects a CH node as an intermediate node based on some factors, such as the minimum number of member nodes obtained cluster, a higher residual energy obtained CH and a minimum distance of CH node from the BS. The CH node which satisfies the factors is selected as a data forwarding node. However, its CH nodes election policy encourages broadcasting an additional number of control messages in each round. It incurs a large control message overhead that consumes additional energy of nodes and results in significant effects on the network lifetime. Meanwhile, its data forwarding strategy influences a data message to traverse from a CH node to the BS through the more hops in the network and consumes additional energy of the constituted nodes. The proposed EDR differs from the above mentioned protocols due to its unique RC nodes election and data routing policy. It emphasizes to minimize the overall data routing cost which includes minimum intra-subspace and inter-subspace communications that prolongs the steady state and network lifetime. Although the LEACH and EADC protocols are proposed in 2D WSNs with nonuniform node distribution, we are interested in implementing those protocols in a 3D WSN named as 3D LEACH and 3D EADC with uniform node distribution. Moreover, we are obliged to exhibit the performance of our proposed protocol in comparison with 3D LEACH and 3D EADC in terms of steady state and network lifetime.
# Design of a Rotary Transformer for Installations on Large Shafts
## 2 Rotary Transformer: Model and Parameters
### Equivalent Circuits  
In view of the analysis with electromagnetic finite elements (FEM) software, an equivalent circuit based on a mutual inductor was considered (see Figure 2). This explains why the extraction of the parameters of the equivalent circuit, \(L_{1}\), \(L_{2}\) and \(M\), is simpler in an FEM environment. \(L_{1}\) and \(L_{2}\) are the self-inductances of the two windings, whereas \(M\) stands for the mutual parameters between them.  
It can be noted that \(L_{1}\), \(L_{2}\) and \(M\) allow for the evaluation of the magnetic coupling coefficient between the two windings as well as the leakage \(L_{\text{lk}}\) and magnetizing inductances \(L_{\text{m}}\) (referring to an approximate \(\Gamma\)-equivalent circuit for power transformers, see [16; 20]) as follows:  
\[k =\frac{M}{\sqrt{L_{1}L_{2}}} \tag{1}\] \[L_{\text{lk}} =\big{(}1-k^{2}\big{)}L_{1}\] (2) \[L_{\text{m}} =k^{2}L_{1} \tag{3}\]  
Based on the equivalent circuit reported in Figure 2, the resistances \(R_{1}\) and \(R_{2}\) model the resistance of the copper for both windings. Therefore, the equations that model this two-port system are the following:  
\[\left\{\begin{array}{l}v_{1}(t)=R_{1}i_{1}(t)+L_{1}\frac{i_{1}(t)}{dt}+M \frac{i_{2}(t)}{dt}\\ v_{2}(t)=R_{2}i_{2}(t)+L_{2}\frac{i_{2}(t)}{dt}+M\frac{i_{1}(t)}{dt}\end{array}\right. \tag{4}\]  
This mathematical representation of the system allows us to determine some useful analytical expressions. In particular, it can be assumed that the secondary is connected to a load resistance \(R_{\text{L}}\) (such as in de-icing applications, see [11; 12]) and the input impedance \(\hat{Z}_{\text{in}}\) seen from the primary terminals can be determined. To this aim, it is useful to consider  
Figure 1: Coaxial topology for rotary transformers.  
Figure 2: Equivalent circuit of the rotary transformer with mutual inductor.  
the phasor domain (see Figure 3) to provide an expression of \(\hat{Z}_{\text{in}}\), which is frequency-dependent, as well.  
The solution of this circuit allows us to state that:  
\[\hat{Z}_{\text{in}}(\omega)=\frac{\hat{\mathcal{V}}_{1}}{\hat{I}_{1}}=R_{1}+j \omega L_{1}+\frac{\omega^{2}M^{2}}{(R_{2}+R_{\text{L}})+j\omega L_{2}} \tag{5}\]  
where \(\omega=2\pi f\) is the pulsation corresponding to the considered harmonic at frequency \(f\). By splitting the real and the imaginary parts of this impedance, it is possible to identify \(R_{\text{in}}\) and \(X_{\text{in}}\), which are both frequency-dependent:  
\[R_{\text{in}}(\omega) =R_{1}+\frac{\omega^{2}M^{2}(R_{2}+R_{\text{L}})}{\left(R_{2}+R_{ \text{L}}\right)^{2}+\omega^{2}L_{2}^{2}} \tag{6}\] \[X_{\text{in}}(\omega) =\omega L_{1}-\frac{\omega^{3}M^{2}L_{2}}{\left(R_{2}+R_{\text{L }}\right)^{2}+\omega^{2}L_{2}^{2}} \tag{7}\]
# Research on Multi-Directional Spalling Evolution Analysis Method for Angular Ball Bearing
## 5 Conclusions  
In this study, the stress within a spalling region and the multi-directional propagation of spalling were analyzed, and the efficiency of the analysis was enhanced through further simplification of the model. The key conclusions drawn from this study are as follows:  
1. A simplified finite element simulation model of a bearing with a spalling fault is proposed. Based on the combination of spalling defects and the outputs from the EHL model, a 2D rolling contact fatigue crack propagation mechanism model was established. With this model, the variations in SIFs at the crack front under different normal loads were analyzed. As the normal load \(Q_{\text{e}}\) increased, the variation in the equivalent SIF also increased. The position where the maximum equivalent SIF occurred also advanced.
2. A multi-directional fatigue spalling evolution model for bearing raceway surface spalling has been established. The subsurface cracks around the spalling region were analyzed by changing the relative motion and position relationship between the roller and the spalling defect. The analysis results show that under the working conditions of a radial load \(F_{\text{z}}=2000\) N and an axial load \(F_{\text{x}}=10,000\) N, spalling in 7208 AC bearings is more likely to propagate in the direction parallel to the roller movement, particularly in the same direction as the roller movement. Additionally, the propagation behavior of initial cracks at various angles has been investigated. The spalling propagation rate gradually slows down as the initial crack angle increases up to 90\({}^{\circ}\).  
Methodology, Z.D., X.W. and H.W.; Investigation, T.H.; Writing--original draft, Z.D.; Writing--review & editing, H.H.; Funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Science and Technology Major Project under the grant number J2019-IV-0004-0071, which is gratefully acknowledged by the authors.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
Figure 15: Two-dimensional spalling crack propagation path under 3 different initial angles.
### Case Study No. 2: CODESYS Linked with Node-RED and Microsoft Azure
#### 6.2.3 OPC UA Server in CODESYS  
CODESYS is relatively easy to work with. It is important to note that we need to name the variables so that Factory I/O can easily identify them and filter out the ones it needs. The entire OPC UA address space also contains a large number of different configuration and status variables that come with the CODESYS runtime. That is why all our useful variables have _FIO_ prefix. We do this in order to be able to recognize our variables and make them easier to read, and most importantly, to be able to retrieve them easily in Factory I/O. In the Symbol configuration of the CODESYS project we select these variables, which actually specifies that they will be offered by the OPC UA server. Then we initialize CODESYS Control Win PLC, which is a softPLC running under Windows. Then, in the CODESYS project, we connect to this runtime and load a program into it. The connection is made by scanning the network and selecting the control unit, which can be the aforementioned softPLC or also a classic hardware PLC unit.  
In Symbol configuration, we can click on our entire PLC_PRG program to mark all variables (Figure 19). In this way, all the variables we use are available in the OPC UA server. We then build and run the program.  
In Factory I/O we open our scene and set the OPC Client DA/UA as driver in the configuration. We will specify _opc.tcp://localhost:4840_ as the server and set \"FIO\" as the variable filter, this will obtain our variables from the OPC UA address space as we mentioned above. We assign all the variables and the communication between Factory I/O and CODESYS is implemented (Figure 20).
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 3 System Model, Problem Formulation and Proposed Scheme
### System Model  
In this paper, we consider the cooperative transmission in which multiple FBSs jointly transmit data to one MS simultaneously, i.e., downlink transmission. It is worth noting that the switching-off of FBS will not result in the additional end-to-end delay, because more than one FBS transmit data to MS. The set of all FBSs, active FBSs, and FBSs in shutdown state are denoted as \(\mathbb{N}\), \(\mathbb{A}\), and \(\mathbb{I}\), respectively, and \(|\cdot|\) denotes the number of elements in the set, e.g., \(|\mathbb{N}|\) corresponds to the number of all FBSs. We assume that the number of MSs is \(K\) and \(K_{i}\) represents the set of FBSs which can participate in transmission to MS \(i\). We assume that the transmit power of FBSs is denoted as \(P\) and this value is same for all FBSs, i.e., heterogeneous transmit power is not considered. Furthermore, both fast fading and slow fading are considered, which are denoted as \(h_{i,j}(t)\) and \(G_{i,j}\), respectively. Here, \(i\) is the index of MS, \(j\) is the index of FBS, and \(t\) represents the time. It is worth noting that the value of fast fading changes over time while that of slow fading remains the same in our system model. In addition, the noise at the MS is modeled to be independent and identically distributed (i.i.d.) circularly symmetric complex Gaussian (CSCG) with a zero mean and \(N_{0}\) variance, i.e., \(\mathcal{CN}(0,N_{0})\), and the MS is interfered with by other communication systems, whose power is \(I_{inter}\). Finally, we assume that the power consumption of active FBS is \(P_{active}\) and that of turned off FBS is \(P_{inactive}\), where \(P_{active}\gg P_{inactive}\). Our system model is depicted in Figure 2.  
Figure 1: Toy example that depicts the limitation of conventional on–off power control scheme.  
For the cooperative transmission, we assume that collaborating FBSs transmit the same signal at the same time [28; 34]. Then, the throughput of MS \(i\) at time \(t\) can be formulated as follows:  
\[R_{i,t}(\mathbb{A})=W\log_{2}\left(1+\frac{\sum\limits_{j\in\{\Delta tK_{j}^{ 2}\}}|h_{i,j}(t)|^{2}G_{i,j}P}{N_{0}W+I_{inter}}\right). \tag{1}\]  
Here, \(j\) represents the index of active FBS which participates in the cooperative transmission for MS \(i\).  
We also derive the average traffic load of FBS \(j\), which we denote as \(L_{j}(\mathbb{A})\), as follows.  
\[L_{j}(\mathbb{A})=\mathbb{E}\left[\sum\limits_{i=1}^{K}R_{i,t}(\mathbb{A}) \mathbb{1}_{j\in K_{i}}\mathbb{1}_{j\in\mathbb{A}}\right], \tag{2}\]  
where \(\mathbb{1}\left(x\right)\) is the indicator function whose value is 1 when \(x\) is true and 0, otherwise. Moreover, \(\mathbb{E}(\cdot)\) is the expectation.
## 2 Experimental Research
### Preliminary Experiment  
A preliminary experiment was carried out on two types of material: galvanized steel (S235)R + Z) (test mark: PL) and carbon steel (S235) (test mark: UC). The size of the test samples was 150 \(\times\) 100 mm. The thickness of the powder coating was analyzed to assess its dependence on the varied combinations of current and voltage values, as summarized in Table 1. Electric current and voltage varied between the lower value of 20 and the upper value of 100. The minimum and maximum values of 20 and 100 were chosen for a reason. Namely, by studying the production process in the plant where the experiment was performed, it was observed that all the varied parameter values for different cases always moved within this range. Since no production parameter went below 20 or above 100 (as far as voltage and electric current values are concerned), it was decided to stay within these limits. This range was set because the results of scientific research ultimately have real applications in the production process when it comes to optimizing and selecting operating parameters in accordance with specific job requirements.  
The sheets on which the preliminary experiment was performed (Figures 2 and 3) were prepared and pre-treated for powder coating, which was necessary for the achievement of a high-quality protective layer that would be suitable for experimental analysis.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Galvanized** & **Carbon Steel** \\  & **Steel** & **C**urbon Steel** \\ \hline \(U\) = 20 kV and \(I\) = 20 \(\mu\)A & PL1 & UC1 \\ \(U\) = 20 kV and \(I\) = 100 \(\mu\)A & PL2 & UC2 \\ \(U\) = 100 kV and \(I\) = 20 \(\mu\)A & PL3 & UC3 \\ \(U\) = 100 kV and \(I\) = 100 \(\mu\)A & PL4 & UC4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of voltage and electric current in the preliminary experiment.  
Figure 2: Test sample made out of galvanized steel.  
Results of the preliminary experiment are presented in Table 2.  
The results of the preliminary experiment indicated clear differences in the thickness of the powder-coated layer when changing the electric current and voltage parameters. Therefore, it was justified to test the stated two parameters as input variables for conducting a detailed experiment in order to determine their optimal values for the achievement of the satisfactory thickness of powder coating on the material surface. Besides galvanized steel (S235 JR + Z) and carbon steel (S235), the main experiment was also performed on aluminum (AlMg3).
## 2 Proposed Framework (RIS-DUG)  
The framework mainly uses two deep learning models partitioned into two phases. The first phase of RIS-DUGL is initialized by exploiting unsupervised representational learning using a deep generative model without using any prior information about the dataset, as shown in Figure 1. We refer to phase-1 as the generative phase. The initialization in this phase assists in extracting useful information hidden in the original form of input. Moreover, to capture pixel-wise low-level details, each image is split into RGB channels before proceeding to phase-1. Each channel is sequentially forwarded to three SAEs, each of the SAEs in two layers deep to extract a useful and compact representations. The channel-wise output of SAEs generates three feature vectors that are concatenated with the respective original channels and then provided as input to phase-2, which we also refer to as the discriminative phase. Transfer learning is employed in this phase using a pertained, sixteen-layered deep VGG network. Finally, fine-tuning this pertained model is performed using custom-defined convolution and fully connected layers followed by their activation to compose the final feature database. The importance and intentions of transfer learning have been described in the feature extraction step in this section.  
Figure 1: Architecture of the proposed RIS-DUGL retrieval model.
## 2 Materials and Methods
### SMD Algorithm
#### 2.2.3 Step 3  
Once the shift vector and all the waveform positions are determined, the shift vector and shift operator are used to shift the columns of the data matrix \(\mathbf{M}\) so that the wave appears in the same set of rows in each column. At that point, the waveform is similar to the one shown in Figure 1, and it can be very accurately described as an outer product between two singular vectors. The number of rows storing the waveform pattern is much smaller than the total number of rows in \(\mathbf{M}\). Therefore, most of the elements in the column vector are not describing the waveform, hence are not relevant, and can be ignored. The length of the column vector need not be as long as columns in \(\mathbf{M}\), so it is reduced such that its first element specifies the number of zeros before the waveform and the last element specifies the number of zeros after the waveform. For example, the waveform row (\(w_{r}\)) would then be equal to the sum of the first element of the column vector (\(u_{1}\)) and half of the length of the recorded waveform (\(l_{w}\)). The optimal length of the waveform that is recorded in the column vector (\(l_{w}\)) (i.e., the number of elements in the column vector excluding the first and the last element), is determined in the machine learning stage. The row vectors and shift vectors are reduced in a similar manner to the column vector since  
Figure 8: (**a**) Zoomed in area around position \((w_{r},w_{c})\) from matrix \(\mathbf{M}\). (**b**) The selected waveform sequence \(\psi\). (**c**) The location of \(\psi\) in the surrounding columns.  
the waveform is often contained in only a subset of columns of \(\mathbf{M}\), i.e., the wave packet may not be recorded at a subset of receiver locations. The effect of these changes is to decrease the number of elements required to represent \(\mathbf{M}\), thus decreasing the memory requirement of the SMD algorithm.  
Once the two singular vectors (the column vector \(u\) and the row vector \(v\)) are extracted, their outer product is subtracted from the matrix \(\mathbf{M}\). The columns of \(\mathbf{M}\) are also shifted back to their original locations. The new matrix \(\mathbf{M^{new}}\) can be described with the equation:  
\[\mathbf{M^{new}}=\mathbf{M}-\chi(\mathbf{uv}^{T},-\mathbf{s}). \tag{7}\]  
As a result, the majority of the located wave disappears which allows the algorithm to focus on a potential second wave present in \(\mathbf{M}\). At the end of this step of the algorithm, a wave has been subtracted from matrix \(\mathbf{M}\) and added to the compressed data as a pair of singular vectors and a corresponding shift vector.
## 3 Demand-Response Aggregator Frameworks in Future US Electricity Markets
### Open Research Questions for Future Demand-Response Aggregators' Operations
In other words, during the negotiation with the DR aggregator, each prosumer can run their day-ahead HEMS/BEMS and provide not only feasible but also optimal demand profile responses to the DR aggregator, which will minimize costly demand discrepancies. Day-ahead and spot market bidding operations can be combined in the same solution so that the cost from real-time demand discrepancies is minimized in the settlement process (multi-horizon frameworks). Such integration of algorithmic solutions and software could facilitate the large-scale deployment of DR aggregators and prosumers in future US electricity markets and help achieve environmental and grid reliability goals. To enable the integration of energy management systems (HEMS/BEMS) and aggregator-prosumer market bidding games, aggregator optimization programs with prosumer device constraints would be transformed into bi-level programs with lower-level prosumer objectives and device constraints. Such a large-scale bi-level program would be very hard to solve for thousands of prosumers, and it would traditionally require all prosumer information to be available to the aggregator. However, with decentralized, scalable, flexible, uncertainty-quantifying, and privacy-preserving algorithms, such obstacles can be overcome. Some examples satisfying these criteria are federated multi-agent learning with MPC and game theory, or hierarchical distributionally robust optimization with privacy guarantees (coordination at higher levels using differentially private updates) and blockchain-enabled transactive energy markets. Lastly, explainable AI can be utilized to better interpret the main drivers behind demand, price forecasting, etc., with methods like SHAP, LIME, saliency maps, etc., most of which are appropriate for privacy-sensitive applications as well.
## 4 Simulation Results and Discussions
### Network Lifetime  
After introducing the managed transmission power function in SEEC, as seen in Figure 4, the network lifetime is enhanced. In SEEC, each sensor node was transmitting packets at a predefined transmission power which made the network die prematurely. In the controlled transmission power (CTP) strategy, transmission power is adjusted by the source node based on Euclidean distance to the destination node. These adjustments of power vary energy consumption for each time when the packet is sent. This variation in energy consumption increases the network lifetime. In Figure 4, it can be seen that the nodes in SEEC with CTP are alive at round 3500 whereas the SEEC without CTP is dead at round 2500 as shown in Figure 5. In DBR During the information sending, the sender incorporates its depth; the data packets accepting nodes contrast their depths with the sender node. The node with a small depth advances the packet to the sink. For each data packet, each node has a fixed holding time, so nodes with greater depths have a smaller holding time. Therefore, because only the depth of the sensor node is used for propagation, these nodes die before alternative nodes that lead to energy holes; see Figure 5. The nodes in DBR with CTP are alive at round 3500, whereas the DBR without CTP is dead at round 2000, as seen in Figure 4.  
In EEDBR, the stability duration is better than DBR, as seen in Figure 4; and when the network is sparse, node neighbors quickly decrease the network's instability and the node load is also higher in EEDBR, so the number of dead nodes in Figure 5 increases suddenly. It can be shown in Figure 4 that the EEDBR nodes with CTP are alive at round 3500, whereas the EEDBR without CTP is dead at round 2000, as seen in Figure 5.