esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Multi-Energy Microgrid Structure  
The multi-energy microgrid is an energy system that runs autonomously. The system consists of energy management devices, distributed renewable energy sources, energy storage devices, energy conversion devices, and energy loads; structurally, the microgrid is composed of energy input, conversion, and storage, as well as output components. In this study, a multi-energy microgrid was built based on the energy hub model, which consisting of a combined cold heat and power (CCHP) system, a gas heat pump (GHP), a distributed photovoltaic (PV), central air conditioning (CAC),electric storage (ES), and heat storage (HS) components with power, gas, cold, and heat energies. The structure of the multi-energy microgrid is shown in Figure 1.  
The multi-energy microgrid contains various equipment units that couple with each other to meet the needs of different energy loads. Power loads are supplied by the CCHP system and PV. The ES or power grid (PG) make up for insufficient outputs; heat loads are supplied by the CCHP and GHP. HS devices make up for insufficient outputs; cold loads are supplied by the CCHP and CAC. In the case of failure, different forms of energy give priority to the supply of energy loads and supplement of energy storage devices of the same kind. Extra energy can be employed for conversion or backup to reflect the differences among energy grades.  
The multi-energy microgrid is an independent and controllable basic unit of the energy Internet at the user terminal. Its flexible operation mode can reduce the energy consumption during transmission and realize the effective consumption of renewable energy sources. The reliability of two typical microgrid system operation modes--the grid-connected mode and the electric-isolated mode--was analyzed to fully consider the reliability factors. Subsequently, a novel planning method for the electric-isolated mode system was proposed. The main assumptions regarding the multi-energy microgrid model are as follows.  
1. Energy distribution networks are placed into a single bus radiation network structure with a certain level of isolation between the devices in the multi-energy microgrid.
2. Different types of energy loads are intensively provided by relevant devices.
3. Failure in any one-unit device occurs independently; only single failure is considered [22].
# An Improved Cohesive Zone Model for Interface Mixed-Mode Fractures of Railway Slab Tracks
## 1 Introduction
[38] noted that the unloading/reloading relationship, which was commonly utilized in conjunction with the PPR model, produced self-healing behavior when the crack underwent unloading/reloading. To address this issue, a new coupled unloading/reloading relationship, which maintained the thermodynamic consistency of the PPR cohesive model, was developed [38]. More recently, the research by [PERSON] et al. [39] showed that the new unloading/reloading relationship prevented the questionable features that might appear when the original model [35; 34] was used, but also bred a new issue regarding damage initiated from the very beginning of the loading process. This model ignores the initial elastic region. In this paper, an alternative simplified PPR traction-separation law and an improved unloading/reloading relationship are developed and validated using multiple cases that  
Figure 1: Interfacial cracks between layers. could effectively eliminate the above issues and preserve all essential features of the original one. The modeling method of connections between the layers of the slab track as proposed in this paper can contribute to the mechanism of high-speed railway (HSR) interlayer defects, on-site monitoring, inspection, and maintenance. This paper is organized as follows. The review of the original PPR model (traction-separation law) and unloading/reloading relationship are presented in Section 2. Section 3 shows the modification of the original PPR model and the comparison of the modified model and original through example cases. Section 4 introduces the improvement of the unloading/reloading relationship and demonstrates that the improved one is effective with the example used in [39]. Then, Section 5 presents the application of the proposed model to analyze interface damage of railway slab track. Finally, the paper is summarized in Section 6.
## Methods and Materials  
### Device Elements  
The cathodes investigated in this work were fabricated and life-tested by collaborators at eBeam, Inc. (Beaverton, OR, USA). The cathode fabrication process is largely proprietary; however, certain general practices are known. The cathodes are comprised of a porous W matrix doped with ~4 wt% Sc\({}_{2}\)O\({}_{3}\) by the liquid-liquid (L-L) method [15], then die pressed into a loose pellet. A near-eutectic mixture of solid BaCO\({}_{3}\)-CaCO\({}_{3}\)-Al\({}_{2}\)O\({}_{3}\) with a molar ratio of 6:1:2 was calcined in air until it reduced to a BaO-CaO-Al\({}_{2}\)O\({}_{3}\) powder. The oxide impregnate powder was distributed across the top of the cathode pellet, which was then heated above 1500 \({}^{\circ}\)C, and the (now liquid) oxide mixture infiltrated the pellet in a process called \"impregnation\". Excess impregnate on the macroscopic cathode surface was removed mechanically and the cathode surface was then washed with deionized water. The cathodes were then placed on individual W/Re wire heaters encompassed in Mo/Re sleeves.  
Figure 1 shows two images of (a) the glass envelope containing the cathode assembly, and (b) an \"unrolled\" schematic highlighting views at right angles around the circumference of the envelope. Figure 2 shows the anode plate after removal from the envelope and cathode assembly. Once the cathode assembly was inserted, the glass envelope was mechanically pumped, and the cathodes were heated to ~1000 \({}^{\circ}\)C\({}_{b}\) (brightness temperature) for ~1 h. This initial heating stage is referred to as \"pre-activation\". The envelope was then sealed by heating and pinching closed a molten glass nipple around the leads (left end of envelope in Figure 1). To remove remaining gases trapped within the envelope, pans of metallic Ba-Al alloy included as part of the cathode assembly were flashed to a high temperature, sublimating Ba and Al, and gettering residual gases to yield a final vacuum atmosphere. Residue from the two flash pans is apparent along the 90\({}^{\circ}\) longitude in Figure 1. The enclosed cathodes were then \"activated\" by heating with their attached heaters to ~1150 \({}^{\circ}\)C\({}_{b}\) for 1-2 days. The cathodes assembled in the test vehicle studied here were then operated for ~26,000 h at ~950 \({}^{\circ}\)C\({}_{b}\) and exhibited an emission of ~1-5 A/cm\({}^{2}\).  
After testing, the entire sealed test vehicle (including the cathodes) was sent to the University of Kentucky for opening, disassembly, and analysis. The test vehicle arrived to the authors in the same configuration and condition it was in after testing conducted at eBeam, Inc. To explore the fate of species desorbed from the cathodes during pre-activation, activation, and/or operation, two elements of the cathode test vehicle were examined: (1) the interior surface of the glass envelope enclosing the cathode assembly and (2) the Mo anode positioned in the cathodes' line-of-sight.
## 3 Circuit Design and Implementation
### Output Matching Network  
Based on the analysis in Section 2, the output fundamental impedance needs to be matched to the overlapping region of the equal power contour and the CCBJ-type impedance space. Through our calculations, the real part of the fundamental load impedance is determined to have a range of [0.89, 1.12], while the imaginary part has a range of [\(-\)0.45, 0.45]. The corresponding ranges for \(\alpha\), \(\beta\), and \(\gamma\) are [\(-\)0.4, 0.4], [0, 0.13], and [1, 1.1].  
To match the output impedance to the target impedance space shown in Figure 3b, an L-C-L three-section impedance matching network was employed. The equivalent lumped-element circuit is illustrated in Figure 6a, with \(TL_{1}\) serving as an RF clock connected to the drain voltage supply, while C\({}_{2}\) serves as a DC-blocking capacitor. By combining calculations with ADS optimization, the parameters of the matching network can be determined, as shown in the table in Figure 6a. The simulated impedance trajectory of the final output matching network is represented by the black line in Figure 7. It is evident that the fundamental impedance falls perfectly within the target impedance space, while the harmonic impedance also lies within the CCBJ-defined impedance space. The simulation results show that the implemented impedance matching network achieves an insertion loss between 0.4 dB and 0.48 dB across the operating frequency band. The large-signal simulation results further demonstrate that the output stage delivers power in the range of 47.8 dBm to 48.2 dBm, exhibiting excellent in-band flatness that is consistent with the theoretical analysis. The simulated drain voltage and current waveforms of the output stage are shown in Figure 6b, aligning well with the theoretical waveforms of the CCBJ mode.
## 1 Introduction
Then through the backbone feature extraction network and multiple attention mechanisms, the algorithm's ability to discriminate between the object features and the background features can be effectively enhanced. Figure 2: Structure of Siamese network single-object tracking algorithm combining a multiple attention mechanism and background feature adaptive updating strategy. 3. The fusion of multiple attention mechanisms is proposed in our algorithm, which consists of an improved hybrid attention mechanism connected in parallel with the CA attention mechanism. Applying it to the object template branch and the search region branch enables the algorithm to locate and recognize the tracking object more precisely, and makes the algorithm adaptively learn to focus on the channel, spatial, and coordinate feature information of the tracking object, thus improving the attention ability and precision of the algorithm. 4. In our algorithm, we propose a response fusion operation, a mutual correlation operation, where the original object features are convolved with the original search region features, and the object features after multiple attention mechanisms are convolved with the search region features after multiple attention mechanisms, and the results of the two convolutional mutual correlations are linearly fused to obtain the final response map in order to enrich the output response of the algorithm. Our algorithm is compared with other Siamese network framework tracking algorithms, such as SiamFC [20], SiamRPN [22], etc. (1) SiamFC and SiamRPN use shallow AlexNet for their backbone feature extraction network, while our algorithm uses a deep backbone feature extraction network designed by us. (2) SiamFC and SiamRPN do not use any other strategies to distinguish between object and background features, while our algorithm uses our designed adaptive updating strategy for background features to distinguish between object and background features. (3) SiamFC and SiamRPN do not use any attention mechanism to enhance the algorithm's attention to object features, while our algorithm uses a fusion of multiple attention mechanisms designed by us to enhance the algorithm's attention to object features. (4) The internal structure of SiamFC and SiamRPN is double-branching without feature fusion, while the internal structure of our algorithm employs multi-branching feature fusion. (5) The output response of SiamFC and SiamRPN is a single response, while our algorithm employs a response fusion operation after correlating with each other, which enriches the algorithm's output response and ensures that the algorithm's output results are not degraded. The remainder of this paper is organized as follows. Section 1 introduces the related algorithms and the proposed algorithm. Section 2 presents research work on the proposed algorithm. Section 3 presents the training and testing of the proposed algorithm. Section 4 presents the test results for the proposed algorithm. Section 5 concludes the paper.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 5 Performance Evaluation
### Performance Metrics  
Performance metrics used in this paper are: energy tax, packet delivery ratio (PDR), end-to-end delay and accumulated propagation distance (APD).  
Energy tax: average energy consumption per node while a packet successfully reaches the destination. Energy consumption includes energy consumed in transmission and reception of a packet and exchanging control packets. The mathematical notation for this metric can be written as:  
\[EnergyTax=\frac{E_{t}}{NodeNum\times packets}. \tag{35}\]  
where \(E_{t}\) total energy consumption of the network is divided by the number of nodes deployed and the number of packets successfully reached at the sink. Energy tax is measured in joules.  
PDR: ratio of packets successfully received at the sink to packets sent by the sender node.  
End-to-end delay: average time taken in transmitting packets from the sender node to successfully being received by the sink. It includes propagation delay, transmission and reception delay, holding time and calculation time taken in a successful transmission to the sink. End-to-end delay is measured in seconds.  
APD: the average propagation distance traveled at every hop traversed for all of the packets successfully reaching the sink. This is measured in km.
## 4 Simulation Evaluation
### Methodology and Metrics  
We implement the four schemes by Qualnet Network Simulator 4.0 [26] and utilize IEEE 802.15.4 as the MAC protocol. The model of the sensor nodes is followed by the specification of MICA2 [27]. The transmission range of sensor nodes is set to 50 m. The simulation scenario is a 500 m \(\times\) 500 m area in which a varying number of nodes (from 200 to 900 nodes) are deployed. To conduct a fair comparison, each protocol sets \(T_{max}\) as 300 ms. For the proposed protocol, \(T_{interval}\) is set to 10 ms. For each scenario, the results presented here are the average of 10 separate simulation runs.  
In order to evaluate the performance of the proposed protocol, we consider the following metrics. Furthermore, the 95% confidence intervals on the mean are computed.  
* Data throughput: we evaluate the data throughput of the route from a source to the destination of each protocol by using the number of packets per second delivered.
* Hop counts: the number of hops of the path from the source to the destination.
* Total number of packets: this metric presents the total number of transmissions, including both data packets and control packets by sensor nodes.
* Total number of perimeter packets: this metric presents only the number of transmissions made during perimeter routing.
* Packet delivery ratio: this metric represents the ratio of the number of successfully reached data packets at the destination among generated data packets by the source.
* End-to-end delay: the average time for sending data packets from the source to the destination, including all kinds of delay.
# The Influence of Axial Compressive Stress and Internal Pressure on a Pipeline Network: A Review
## 3 Pipeline Residual Strength Assessment Methods
### Dnv-Rp-F101  
DNV-RP-F101 (DNV) is a corrosion assessment method that was developed by Det Norske Veritas. DNV is the least conservative method for predicting the burst pressure of corroded pipelines [13; 16; 46] for single, interacting, and complex-shaped defects subjected to internal pressure only, and single defects subjected to both internal pressure and longitudinal compressive stress. DNV states that the source of the longitudinal compressive stress may be due to longitudinal loads, bending loads, temperature loads, etc. DNV provides two alternative methods for determining the burst pressure of corroded pipes.  
The first method is known as DNV Part A. It has calibrated safety factors while considering the natural variation in pipe material properties, wall thickness, and internal pressure applications. Uncertainties associated with the dimension of the corrosion defect are explicitly considered in the determination of the pressure resistance (capacity). Probabilistically calibrated equations (with partial safety factors) are utilized for predicting the burst pressure of corroded pipelines. Equation (7) shows the DNV Part A equation with respective partial safety factors and fractile levels.  
\[P_{f}=\gamma_{m}\bigg{(}\frac{2t\omega_{UITS}}{D-t}\bigg{)}\bigg{(}\frac{1- \gamma_{d}(d/t)^{*}}{1-\frac{\gamma_{d}(d/t)^{*}}{M}}\bigg{)} \tag{7}\]  
Figure 2: Ovalization of a pipe bend under opening bending moment [29].  
The geometry factor and the modified version of the depth over the full wall thickness ratio are given by Equations (8) and (9), respectively.  
\[M=\sqrt{1+0.31\bigg{(}\frac{l}{\sqrt{Dt}}\bigg{)}^{2}} \tag{8}\]  
\[(d/t)^{*}=(d/t)_{meas}+\epsilon_{d}\cdot StD[d/t] \tag{9}\]  
The second method, DNV Part B, is based on the Allowable Stress Design approach. The predicted burst pressure using Equation (10) is multiplied by a single usage factor based on the original design factor, as per Equation (11). Uncertainties associated with pipe material properties, pipe dimensions, and the dimensions of the corrosion defect are left to the user's discretion.  
\[P_{f}=\bigg{(}\frac{2t\sigma_{UITS}}{D-t}\bigg{)}\Bigg{(}\frac{1-\gamma_{d}(d/ t)}{1-\frac{\gamma_{d}(d/t)}{Q}}\Bigg{)} \tag{10}\]  
\[P_{sw}=F_{DNV}\cdot P_{f} \tag{11}\]
# Quaternion Codes in MIMO System of Dual-Polarized Antennas +
###### Abstract
Therefore, it was deem necessary to develop codes that work for any number of antenna systems besides having the main advantage of attaining decoupled decoders in the presence of quaternionic channel as was the case with iteratively generated designs [14]. This has been done following the line of approach indicated in [3] which gives us a class of QODs that are non-square and the code rate is bounded below by 1/2. The motivation behind this was to exploit the impact of a dual-polarized transmission channel at the receiver side in such a way that ML quaternion norm criterion simplifies to a decoupled decoding solution which reduces the decoder complexity significantly. After obtaining a generalized ML decoder, we take one step further by exploring quasi-QODs. The proposed quasi-QOD provides a code rate of two for four transmit dual-polarized antennas. However, the quasi nature of these codes leads to a slight comprise on decoding complexity. The solutions obtained with this compromised complexity-based decoder are still better than the coupled traditional ML decoder based solutions. A detailed complexity analysis of the proposed constructions have been provided to illustrate the significance of the proposed low-complexity decoder for both pure or quasi QODs. Briefly, we summarize the main contributions made in this paper. * We propose a new class of QODs based on Liang mechanism [3] with stable code rate as the number of transmit antennas increases. * The class is shown as best suitable in describing point-to-point communication among dual-polarized antennas. * The proposed decoder is shown to provide linear decoding solution for all STBCs obtained from QODs. * A brief performance analysis is carried out for all obtained QODs. Some remarks on notation are as follows--all matrices are denoted with bold letters. The real and complex numbers fields are denoted in standard form \(\mathbb{R}\) and \(\mathbb{C}\), respectively. \(\Re\) and \(\Im\) represent real and imaginary parts of a complex number. \(\mathbb{Q}\) denotes a quaternion field comprise of a basis of non-commuting elements \(1,i,j,k\), such that \(ij=k=-ji\), \(jk=i=-kj\), \(ki=j=-ik\). The superscript \(q^{Q}\), refers to quaternion conjugate and responsible for sign changes in \(i,j\) and \(k\). In matrices, it also includes transpose \((.)^{T}\). Moreover, operators \((. )^{H}\) and \(tr(. )\) refer to the Hermitian transpose and trace operator, respectively. Both matrices \(\mathbf{C}_{q}\) and \(\mathbf{C}_{Q}\), corresponds to quasi-orthogonal STBCs; however, the subscript \(Q\) indicates that the STBC \(\mathbf{C}_{Q}\) is obtained from a QOD.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 2 M2D2 System Framework
### Interpretability and Active Learning Loop  
(1) Drift Explanation and Root Cause Analysis:  
Alerting users to an anomaly is often insufficient by itself; actual practitioners, whether engineers or management personnel, need clarity on which measurements or textual entries triggered the warning. M2D2 addresses this by incorporating interpretability mechanisms. If the underlying model leverages Transformer-based architectures, attention-weight visualizations can highlight which sensor channels or textual tokens contributed most. Alternatively, for more opaque deep neural networks, SHAP can approximate the feature-level contributions behind the drift score. This transparency expedites root-cause analysis--be it \"current fluctuations\" becoming prominent in maintenance logs or a temperature sensor exhibiting statistically unusual oscillations--and thus leads to more targeted remediation.  
(2) Active Learning and Feedback:  
Even the most refined detection model is prone to occasional misclassifications, especially as production environments evolve in unforeseen ways. M2D2 counteracts this problem via an active feedback loop: whenever the system flags a drift, domain experts have the opportunity to label the alert as a genuine anomaly or a false alarm. For false alarms, the model or threshold can be adjusted accordingly, reducing the recurrence of the same misclassification. For newly discovered anomalies, the labeled samples are incorporated into an \"anomaly database\", used in future retraining or incremental updates to enhance the coverage of novel drift patterns. In this manner, M2D2 continuously adapts to dynamic manufacturing conditions, reducing the burden of frequent manual inspections and lowering the rate of missed detections.