esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Phase PV CHB Inverter for a Distributed Power Generation System
## 2 System Topology and Control Strategy
### CHB Inverter Modeling
As can be seen, the first PI evaluates the reference currents \(I_{p}^{ref}\) for each phase, and, from them, the reference active powers \(p_{p}^{ref}\). In the inner loop, a multivariable PI controller [34] regulates both the direct and the reactive grid currents. To this end, the three reference powers given by the first PI are summed in order to get the direct current reference \(i_{d}^{ref}\), while the reactive current reference \(i_{q}^{ref}\) can either be set to zero (in order to achieve unity power factor) or adjusted in order to control the reactive power. The adoption of a multivariable PI controller allows a simple implementation, while assuring fast dynamics and zero steady-state error for three-phase application. Currents \(i_{d}\) and \(i_{q}\) are achieved by converting the measured grid currents (_abc_ coordinates) into _dq_ coordinates. The outputs of the PI are the inverter reference voltages in _dq_ coordinates, which are then converted to the three-phase reference voltages (_abc_ coordinates); the synchronization of the _dq_ transformation is performed by means of the phase-locked loop (PLL) circuit. It is important to note that the independent MPP tracking performed for each cell may generate unbalanced phase power, thus inherently leading to unbalanced grid currents. In order to overcome this undesired drawback, a proper homopolar (zero-sequence) voltage component \(v_{0}\) is added to the direct sequence. Indeed, according to [43], for given power unbalances among the phases, defined as  
\[\Delta P_{p}=P_{p}^{ref}-\sum_{h=1}^{3}\frac{P_{h}^{ref}}{3}\quad p=1,2 \tag{4}\]  
a voltage \(v_{0}\) allowing the inverter neutral point to move so as to have unbalanced inverter voltages (\(v_{im,p}\) in Figure 1), while assuring balanced grid currents, can be found in accordance with the following equations:  
\[\left\{\begin{array}{l}\vspace{0.2 cm}\vspace{0.  
the innovative core of the present work, because it is at this stage that (thanks to a proper sorting algorithm) it is dynamically decided which cell of each leg has to be driven in PWM mode. Indeed, as is shown in Figure 3, the reference signal entering the PWM block depends on the index \(K\), already defined in (2). The detailed description of the sorting algorithm is given below.
# Neural Network-Based Reference Block Quality Enhancement for Motion Compensation Prediction
## 3 Proposed Method
### Dataset for Training  
In video coding, quantization will cause an irreversible loss of high-frequency image details. The aim of an RBENN is to restore the high frequency details of the image according to the low frequency information of the image.  
In H.266/VVC, the relationship between different CUs is shown as  
\[\begin{cases}CU_{pre}=DCTIF(CU_{ref}),\\ CU_{res}=CU_{ori}-CU_{pre},\\ CU_{rec}=CU_{pre}+T^{-1}(Q^{-1}(Q(T(CU_{res})))),\end{cases} \tag{4}\]  
in which, \(CU_{rec}\), \(CU_{pre}\), \(CU_{ref}\), \(CU_{ori}\), \(CU_{res}\) represent the reconstruction of the current CU at the decoder, the predicted CU, the reference CU, the original CU, and the residual CU, respectively. The reference CU is the CU found on a reference frame according to MV. The predicted CU is obtained from the reference CU through the interpolation filter. The residual CU is transformed \(T(\cdot)\), quantized \(Q(\cdot)\), inversely quantized \(Q^{-1}(\cdot)\), and inversely transformed \(T^{-1}(\cdot)\) to obtain the reconstructed CU.  
In our proposed method, with the reference CU as input, the \(RBENN()\) enhances the quality of the reference CU to replace the initial reference CU:  
\[CU_{pre}=DCTIF(RBENN(CU_{ref})). \tag{5}\]  
For easy implementation, the input of the training data is the predicted CU, which is extracted from the decoder directly, and the label of the training data is the original CU, which is extracted from the original video sequence.  
The specific details are shown in Figure 7, where the purple dots represent the integer pixel position of the picture. The current CU in the position \((X,Y)\) of the current frame finds the reference CU in the reference frame \((X+I_{mv_{x}},Y+I_{mv_{y}})\) position through MV, where \(I_{mv}\) represents the integer displacement of MV and \(F_{mv}\) is the fractional displacement. The reference CU is then filtered to obtain the predicted CU.  
We used 650 video sequences to generate the training data. These 650 video sequences came from the dataset of BVI-DVC [28]. The resolutions were \(3840\times 2176\), \(1920\times 1088\), \(960\times 544\), and \(480\times 272\). To better adapt to video contents with different resolutions, all these video sequences were encoded using VTM-9.3 with LDP configuration [29]. The quantization parameter was 22. The first 32 frames of each sequence were encoded, the predicted CU of the integer MV was obtained from the compressed bitstream, and the original CU was obtained from the video sequence. The dataset contained \(16\times 16\), \(32\times 32\), and \(64\times 64\) CUs for training RBENN1, RBENN2, and RBENN3, respectively. There were about 10,000,000 blocks for training RBENN1, 2,000,000 blocks for training RBENN2, and 300,000 blocks for training RBENN3.  
Figure 6: Framework of an RBENN.
# The Case for a More Holistic Approach to Dry Eye Disease: Is It Time to Move beyond Antibiotics?
## 2 Dry Eye Disease as an Inflammatory Disorder
### The Anti-Inflammatory Role of Antibiotics in DED  
The use of systemic antibiotics for DED is well established. Anti-inflammatory effects of bacteriostatic tetracyclines such as oxytetracycline, doxycycline and minocycline are advocated for meibomian gland dysfunction induced DED though treatment breaks after three months are recommended [33]. Anti-inflammatory actions include the suppression of leucocyte migration [34] and nitric oxide and reactive oxygen species production [35].  
Macrolides such as azithromycin and tacrolimus exert similar anti-inflammatory activities as evidenced in trials to treat pleharitis and MGD [25; 36; 37]. Tacrolimus stimulates T cell activation by potentially blocking cytokine receptor expression and has shown evidence of effecting improved tear stability and ocular surface status in DED subjects [38]. Azithromycin has also been reported to effect improvement in DED through direct action on meibomian gland epithelial cells by stimulating lipid accumulation and meibomian gland cell differentiation in vitro [39].  
Additionally, low dose antimicrobials inhibit production of bacterial lipases and cognate free fatty acids that exacerbate inflammation, leading to improved meibomian gland function and stable tear film [40]. The release of toxic bacterial products such as lipases--or the secondary production and release of pro-inflammatory cytokines--is pathogenic [14]. Hence, in addition to anti-inflammatory and immunosuppressive action mediated through inhibition of lymphocyte activation and release of inflammatory mediators [41], low dose topical antibiotics have been used to inhibit production of bacterial toxins locally [42].  
Even more so than biocidal use of antibiotics, such treatment is open to all the attendant disadvantages of chronic antibiotic use with low dosage more likely to lead to increased antibiotic resistance.  
As useful as antibiotics, corticosteroids and other agents employed to modulate inflammation have been for DED, we need to move beyond merely keeping inflammation in check but rather, determining and ameliorating the source of inflammation [43].
###### Abstract  
Acoustic emission (AE) signals exhibit a strong correlation with concrete damage. However, the relationship between column damage and AE signals under eccentric loading conditions, combined with the application of traditional RA-AF classification methods for crack characterization, demonstrates limitations. These approaches provide insufficient resolution to accurately identify damage types throughout the entire structural failure process. This study employed K-means clustering algorithm and Gaussian mixture models (GMMs) to analyze AE signal features from reinforced concrete (RC) columns undergoing failure under the eccentric compression loading of different eccentricity. Subsequently, a random forest model was used for automated damage stage classification. Experimental results demonstrate that the damage progression in eccentrically compressed columns comprises four distinct stages, each exhibiting unique AE signal characteristics. The integrated approach of clustering and random forest modeling demonstrates robust feasibility in identifying AE signal patterns associated with specific damage stages, achieving an 85% recognition rate for damage stage classification. These findings provide quantitatively validated evidence supporting the efficacy of machine learning-based methodologies for enabling stage-specific damage characterization in structural health monitoring applications.  
eccentrically load; acoustic emission; K-means; RA-AF analysis Article
# Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis
## 4 Discussions  
In the experimental analysis, the db10 mother wavelet was used in the WPD processing. In order to compare different mother wavelets in the WPD processing, the fault detection performance was analyzed using six different mother wavelets. Table 4 shows the comparison results. The wavelet-nonlinear ICA was used as the source separation method in the comparison. As can be seen in Table 4, the fault detection rates of different mother wavelets are very close. As a result, the choice of the mother wavelet is not the key factor that influences the fault detection performance. According to the fault diagnosis results in Table 4, one can note that the proposed wavelet-nonlinear ICA is the key factor. Because the useful vibration source of the faulty part can be effectively extracted by the key wavelet-nonlinear ICA method, the fault detection rate is improved.
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 2 Materials and Methods
### Methods
#### 2.3.4 Validation  
Validation statistics, including the coefficient of determination (R2), bias, the mean absolute error (MAE) and the root mean square error (RMSE) were used to access the performance of the original satellite-based precipitation products and the downscaled results against precipitation estimates observed from rain gauges. The R2 values indicate the strength of the linear relationship between the precipitation estimates and ground observations, ranging between 0 and 1 with an optimal value of 1. Bias denotes the degree either over- or underestimated, with a perfect value of 0. Overestimation is represented as positive bias indicating the satellite value is higher than gauge data. MAE shows the mean magnitude of the errors and RMSE was used to represent the distribution of errors. Smaller values of MAE and RMSE indicate more accurate and precise results. The perfect score for both the MAE and RMSE is 0.  
\[\text{MAE}=\frac{\sum_{i=1}^{n}\left|P_{i}-M_{i}\right|}{n} \tag{4}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(P_{i}-M_{i}\right)^{2}}{n}} \tag{5}\]  
\[\text{bias}=\frac{\sum_{i=1}^{n}P_{i}}{\sum_{i=1}^{n}M_{i}}-1 \tag{6}\]  
where \(P_{i}\) is the satellite precipitation measurement; \(M_{i}\) is the measured precipitation from rain gauge stations; \(n\) is the total number of samples.
## 2 Materials and Methods
### Computational Domain and Boundary Conditions  
[PERSON] et al. [17] designed the conventional flow frame used in this study, comprising of vertically symmetrical flow channels and an electrode, as shown in Figure 1. The flow frame comprises an inlet channel, an electrode, and an outlet channel. Each channel has bifurcations at five stages. At the last stage of the inlet channel, the solution flows into the electrode through the 32 electrode inlets.  
The commercial software, ANSYS Fluent, was used for the flow analysis; an incompressible laminar flow (maximum Re <1300) was considered for all of the sections. Using a three-dimensional unstructured grid system, we constructed the flow channels with tetrahedral grids and the electrode with hexahedral grids. The operating flow rate of the RFB was 18 LPM(Liter per minute) based on 60 cells. Based on a single cell, the 0.3 LPM electrolyte was introduced at the inlet with a specified mass flow inlet condition, and atmospheric conditions were applied at the outlet (Table 1). For the anolyte, an oily bromide complex with a higher density and viscosity than the conventional electrolyte was formed. Herein, for convenience, we conducted an analysis based on an SOC (State of charge) of 0%, for which the oily bromide complex was not formed. The density and viscosity of the Zn/Br solution were 1402 kg/m\({}^{3}\) and 0.00216 kg/m\(\cdot\)s, respectively [18].  
\begin{table}
\begin{tabular}{c c} \hline
**Inlet** & 0.3 LPM (0.007 kg/s) \\
**Outlet** & atmospheric pressure \\ \hline \end{tabular}
\end{table}
Table 1: Inlet and outlet boundary conditions for numerical computation.
# Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
## 4 Simulation and Analysis  
### Experimental Settings  
In this study, we chose classical MNIST [43] and FashionMNIST [44] datasets for image classification tasks, both widely used benchmark datasets. MNIST consists of handwritten digit images, and we conducted benchmark testing using the MNIST dataset in Section 4.2.1. FashionMNIST includes images of clothing and accessories, and we performed noise-resistant experiments using the FashionMNIST dataset in Section 4.2.2. Our objective is to explore quantum computing-based image classification in the noisy intermediate-scale quantum (NISQ) era. Considering the potential impact of noise on quantum devices in practical applications during the NISQ era, we specifically focused on the influence of noise on image classification tasks. In our simulation experiments, we employed the depolarizing channel model to simulate the effects of noise, a widely used noise model for describing real quantum devices.  
We utilized the PennyLane library, which offers robust support for quantum neural networks and seamlessly integrated it with the PyTorch interface to facilitate the training process of quantum and classical neural networks [45]. To simulate the effects of quantum computing, we selected the Qulacs quantum simulator [46], known for efficiently simulating the evolution of quantum circuits. In our experimental setup, we set the learning rate to 0.001, a common hyperparameter setting to balance model convergence speed and stability. Additionally, we defined the batch size to optimize training efficiency and framed the task as a four-class classification task to align more closely with real-world applications. In our experiment, after testing, we set up 8 data qubits, four classifications require 2 readout qubits, and the entire circuit has 56 adjustable parameters. In this simulation experiment, we aim to enhance our understanding of how noise affects image classification tasks in quantum computing. Furthermore, we aim to assess the performance of the suggested hybrid classical QNN classification model in the NISQ era. Additionally, we aim to demonstrate the effectiveness of our combined training strategy for hybrid quantum neural networks and error-mitigation strategies in image classification, as well as the model's robustness and generalization capabilities in the presence of noise.
# Progress on Single-Feed Quality Wideband Linear Wire Array
## 8 Discussion and Future Directions
### Future Directions  
The QWL antenna offers several advantages, including wideband performance, high gain, a single feed point, low cost, low wind resistance, light weight, and a compact structure. As shown in Figure 14, advancements have been made in various aspects of the QWL antenna, including the driven element, feed, director, reflector, and connector. The WHEMS antenna, which serves as the driven element of the QWL antenna, can achieve good impedance matching and over 2 dB of broadside gain across a continuous bandwidth exceeding 150%. The proposed W-shape reflector for the WHEMS antenna, when used as a driven element, can theoretically provide over 150% of 3 dB gain bandwidth. The low-common-mode-current connector enables a more compact structure and further reduces wind resistance for the QWL antenna. Progress in the series-fed array configuration allows the antenna to achieve over 13 dBi of gain with a single feed point. The three-dipole array director effectively directs the electric field on the aperture of the WHEMS antenna, making it a highly efficient director; however, its operational bandwidth is limited, and it is not a high-efficiency broadband director.  
Therefore, the future directions for the QWL antenna include the following:  
1. Designing a broadband high-efficiency director.
2. Expanding the bandwidth of the QWL antenna by incorporating a broadband high-efficiency director.  
**Author Contributions:** Conceptualization, Y.Q. and L.C.; methodology, Y.Q. and L.C.; validation, L.C. and L.X.; formal analysis, L.C.; writing--original draft preparation, Y.Q., L.C. and L.X.; writing--review and editing, Z.W.; visualization, L.C. and L.X.; supervision, Y.Q. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** Author [PERSON], [PERSON] and [PERSON] were employed by the company LinkE Technologies (Hengqin) Co., Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 4 PoENF: A Proof-of-ENF Consensus Protocol
### PoENF-Based Block Proposal Mechanism
#### 4.3.2 PoENF Consensus Algorithm
```
1:procedure: generate_block(\(v_{i}\))
2:\(hc\leftarrow\mathcal{H}(head(\mathcal{C}))\)
3:\(height\gets head(\mathcal{C}).height+1\)
4:\(mt\_root\leftarrow\text{MTree}(v_{i}.TX)\)
5:\([E_{1},E_{2},...,E_{n}]\leftarrow\text{ENF\_vect}(v_{i}.TX)\)
6:\(ent\_score\leftarrow[]\)
7:for\(E_{i}\) in \([E_{1},E_{2},...,E_{n}]\)do
8:\(s_{i}\leftarrow\sum_{i\to j}\|E_{i}-E_{j}\|^{2}\)
9:\(ent\_score.append(s_{i})\)
10:endfor
11:\(s^{*}\gets Min(ent\_score)\)
12:if\(s_{i}\leq s^{*}\)then
13:\(new\_block\leftarrow(hc||mt\_root||v_{i}.TX||v_{i}.pk||s.t||height)\)
14:\(\sigma_{i}\leftarrow\text{Sign}(new\_block,v_{i}.sk)\)
15:return\((new\_block||\sigma_{i})\)
16:endif
17:procedure: verify_block(\(new\_block,\sigma_{i}\))
18:if\(\text{Verify\_Sign}(new\_block,\sigma_{i})\
eq\text{True}\) OR
19:\(\text{Verify\_TX}(new\_block)\
eq True\)then
20:return\(False\)
21:endif
22:\(hc\leftarrow\mathcal{H}(head(\mathcal{C}))\)
23:if\(new\_block.height\
eq head(\mathcal{C}).height+1\) OR
24:\(new\_block.hc\
eq hc\)then
25:return\(False\)
26:endif
27:\([E_{1},E_{2},...,E_{n}]\leftarrow\text{ENF\_vect}(new\_block.tx\_list)\)
28:\(ent\_score\leftarrow[]\)
29:for\(E_{i}\) in \([E_{1},E_{2},...,E_{n}]\)do
30:\(s_{i}\leftarrow\sum_{i\to j}\|E_{i}-E_{j}\|^{2}\)
31:\(ent\_score.append(s_{i})\)
32:endfor
33:\(s^{*}\gets Min(ent\_score)\)
34:if\(s_{j}>s^{*}\)then
35:return\(False\)
36:endif
37:return\(True\)
```  
**Algorithm 1** The PoENF-based block generation procedures. In block verification process, each validator calls the \(verify\_block()\) function to determine whether or not the received \(new\_block\) can be accepted and appended to chain. The \(Verify\_Sign()\) checks if a \(new\_block\) sent by \(v_{j}\) has a valid signature \(\sigma_{j}\), while \(Verify\_TX()\) validates that all transactions recorded in \(new\_block\) are sent from valid nodes and have the same Merkle tree root as \(new\_block.mt\_root\). After validating that \(new\_block\) is generated in the current round slot \(sl_{t}\) with the correct chain header, a PoENF algorithm verifies if \(v_{j}\) has a valid ENF proof with minimum score. If all conditions are satisfied, the \(new\_block\) is accepted into confirmed status, and \(v_{i}\) updates the head of local chain as \(head(\mathcal{C})=B_{i+1}\) accordingly. Otherwise, the \(new\_block\) is rejected and discarded.
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 3 Scaling up RINA Networks  
Before the start of ARCFIRE, the IRATI prototype supported short-lived tests with minor traffic variations on a relatively small number of systems. Two tools were available to validate the implementation: the configurator and the demonstrator [9]. The demonstrator allows a user to define and run a RINA network on local QEMU Virtual Machines (VMs) using configuration files. IRATI had to be installed before running the demonstrator. The configurator allowed defining RINA networks through Extensible Markup Language (XML) files, and would run this on physical machines in an EMULAB testbed. The configurator starts the basic daemons of IRATI on the machines but more advanced configuration was not supported.  
These tools showed it was necessary to automate the execution of large experiments, since doing this manually is error-prone, hard to reproduce, and very time consuming. To fill this gap, ARCFIRE developed Rumba [10]. Rumba is an experimentation framework that supports existing testbeds provided by the American GENI (Global Environment for Network Innovation) and the European FIRE initiatives via the jFed and Emulab experiment management software. Rumba provides a simple yet powerful API for experimenters to configure all the aspects of a RINA network, deploy it, run it on testbeds, and gather the results. It is implemented as a Python library that allows the user to programmatically define (i) the physical connectivity graph of the network; (ii) how RINA layers are laid out on the nodes, without any restriction on layer membership and stacking geometry; (iii) the policies to be used by each layer; (iv) where and when distributed applications should run. Rumba already supports several testbeds and prototypes and is easily extendable. Rumba has been used by all experiments carried out within ARCFIRE, and helped further test scalability of the supported prototypes. At the end of the projects all supported prototypes can scale up to hundreds of nodes, thousands of IPCPs (IPC Processes, the individual instances of DIFs in a system) and ten-thousand flows.
## 2 Models and Methods
### Models
#### 2.2.2 Reduced-Order Model of the Rear-Mounted Three-Point Linkage  
The rear-mounted or semi-mounted machines are connected to the tractor through the rear three-point linkage. Figure 2a shows a typical rear three-point linkage and highlights the main components as defined in the international standard ISO 730:2009 [25]: 1 lower links; 2 upper link; 3 lift rods; 4 implement; 5 stabilizers. The implement is part of the operating machine, whilst all the other bodies are connected to the tractor.  
In Figure 2b the boundary nodes of the different components are highlighted: \(P_{i}\) and \(E_{i}\) indicate the boundary nodes connecting the three-point linkage to the tractor and the operating machine, respectively; \(V_{i}\) are the boundary nodes connecting the components of the linkage each other.  
To obtain the model of the rear three-point linkage, each component was modeled using commercial FE software; subsequently, a modal reduction was performed, using the Craig Bampton approach and retaining only the physical connecting DoFs with other  
Figure 2: Rear-mounted three-point linkage: (**a**) assembly; (**b**) boundary nodes of the reduced-order model.  
components. Finally, the model of the whole rear-mounted three-point linkage was built by coupling the reduced-order models of the different components. Note that the linkage can assume different geometric configurations and the coupling conditions must be defined accordingly.