esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 3 The DAILC Algorithm of Voltage Control Subject to Sensor Saturation
### Design of The DAILC Algorithm
We can obtain the following tracking limit:  
\[\quad\times(\tilde{\varrho}(\hat{\kappa}+1,\hat{\rho}-1)-\hat{ \mathbf{h}}(\hat{\kappa},\hat{\rho}))+\Delta\hat{\mathbf{h}}(\hat{\kappa},\hat {\rho})\]5 \[\quad\times(\tilde{\varrho}(\hat{\kappa}+1,\hat{\rho}-1)-\hat{ \mathbf{h}}(\hat{\kappa},\hat{\rho}))+\Delta\hat{\mathbf{h}}(\hat{\kappa},\hat {\rho})\]6 \[\quad\times(\tilde{\varrho}(\hat{\kappa}+1,\hat{\rho}-1)-\hat{ \mathbf{h}}(\hat{\kappa},\hat{\rho}))+\Delta\hat{\mathbf{h}}(\hat{\kappa},\hat {\rho})\]7 \[\quad\times(\tilde{\varrho}(\hat{\kappa}+1,\hat{\rho}-1)-\hat{ \mathbf{h}}(\hat{\kappa},\hat{\rho}))+\Delta\hat{\mathbf{h}}(\hat{\kappa},\hat {\rho})\]8We can easily derive that \(\lim_{\beta\to\infty}\|\bar{q}(\bar{s}+1,\bar{p})\|=P/(1-\Im)h_{1}\). Hence, as the iteration count reaches infinity, the voltage tracking error remains within a bounded limit. Consequently, the voltage tracking error for the DAILC algorithm converges to a small residual set across all \(\bar{\kappa}\). This completes the proof. As shown in Figure 2, this paper proposes a distributed data-driven adaptive iterative learning secondary voltage control algorithm. Unlike the methods in [17, 18, 30], this paper does not rely on the microgrid model information, but only controls through input-output data. Considering sensor saturation and disturbances, the algorithm successfully restores the AC microgrid voltage to its reference value. A crucial aspect for the practical application of any control algorithm in microgrids is its computational requirement and suitability for real-time implementation on embedded controllers. The proposed DAILC algorithm is designed with this constraint in mind. The core computations for each agent, namely the PPD estimation (11) and the control law update (9), primarily consist of a small number of scalar algebraic operations (multiplications, additions, and divisions). This makes the algorithm computationally lightweight, especially when compared to model-based strategies that require evaluating complex nonlinear system dynamics, or AI-based methods like neural networks that involve computationally intensive matrix operations. Therefore, the DAILC method strikes an effective balance between advanced adaptive performance and computational feasibility, making it highly suitable for deployment on standard digital signal processors (DSPs) or ARM-based microcontrollers commonly used in power electronic inverters.
# Effect of Stirrup on Bond Strength Degradation in Concrete Cracked by Expansion Agent Filled Pipes
## 4 Evaluation of Bond Strength Degradation in Cracked Specim with Stirrup
### Comparison of the Test Results with Fib Model  
The fib Model Code 2010 [35] has introduced the reduction in bond strength depending on the surface crack width (hereafter, called the fib model). It suggests the variation of the residual bond strength for a specific range of surface crack width. We plot the degradation range versus the median values of the crack width range to illustrate the upper and lower limit of the bond degradation shown in Figure 15 with the three proposed formulas for S0, S1, and S2.  
In specimens without stirrup Series S0, the fib model is in good agreement with our prediction when the induced crack width is smaller than 0.7 mm. However, the fib model gives minor overestimations for the specimens with crack widths bigger than 0.7 mm.  
The fib model overestimates our model in specimens with stirrup when the induced crack width is inferior to 0.6 mm. Several reports have shown that bond strength increases in corroded specimens without induced crack [9; 12; 22; 28]. In cracked concrete with a stirrup, it can thus be suggested that the bond strength is higher for corroded rebar (with  
Figure 14: Proposed formulas comparison.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Series** & **pw (\%)** & **a** & **b** \\ \hline S0 & 0 & 0.84 & \(-1.89\) \\ S1 & \(1.10\) & 0.42 & \(-3.05\) \\ S2 & \(1.68\) & 0.28 & \(-6.32\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Regression analysis result.  
Figure 15: Comparison of the test results with the fib model: **(a)** specimen without stirrup; **(b)** specimen with stirrup.  
rust) than sound rebar. On the other hand, when the crack is bigger than 0.6 mm, the Series S2 model agrees well with the fib model. However, our prediction for Series S1 is slightly underestimated. The recommendations of fib model indicate only the presence or absence of stirrups (links), without pointing out the impact of their ratio on bond degradation. Therefore, these variations are likely related to the fact that the fib model does not take the stirrup ratio into account.  
Our results also seem consistent with other research, which found that in unconfined concrete, the corrosion-induced crack is more dominant than rebar profile change [21; 22]. However, this may not be applicable in corroded specimens with stirrup.
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
## 2 NDT Architecture for the RAN
### Apps  
The proposed architecture considers that different _Apps_ can make use of the RAN NDT. Each _App_ is provided with a RAN NDT instance customized according to the specific requirements of the _App_. This customization can embrace the activation of selected _service mapping models_, the specification of the data to gather in the data repository, the definition of the required KPIs, the specification of analysis and visualization capabilities for the _App_, the specification of emulations if required, etc.  
Figure 1 identifies different relevant _Apps_ related to the different stages of the lifecycle of the RAN that are identified as main use cases for the RAN NDT:  
* _Planning & Dimensioning Apps_: When deploying new gNBs in the RAN or configuring the existing ones, emulations can be performed on a RAN NDT instance to determine the most adequate gNBs deployment locations and configurations to enhance the coverage and capacity of the network. For this kind of _App_, the NDT instance will include the already deployed gNBs and their configurations. Then, diverse emulations associated with different configurations of the new gNB can be conducted on the RAN NDT instance.
* _Network Operation Apps_: During the operation of the network, a RAN NDT instance can contribute to the monitoring of the network by extending the performance metrics obtained from the real network with additional ones obtained from emulations on the RAN NDT. Based on this, possible failures or anomalies can be predicted and actionscan be taken to avoid them. Therefore, the RAN NDT instance for this _App_ should include all the elements (scenario topology, gNBs, UE characterization, etc.) as in the real RAN, which will be fed with real-time data from the RAN.
* _Network Optimization Apps_: The optimization of the RAN can benefit from a RAN NDT instance for emulating different RAN configurations as well as for testing algorithmic solutions before they are applied in the real RAN, allowing adjustment and validation of their parameters. The testing of configurations can be conducted by enforcing them on the RAN NDT instance and performing emulations. In the case of algorithmic solutions, whether they are based on heuristics, analytics or machine learning, they should be integrated within the RAN NDT instance to assess their performance through emulations. For instance, if a RRM policy for admission control is tested, it should be loaded as a functional model for _RAN management & optimization_, and the corresponding admission decisions should be made on the RAN NDT instance models to assess its performance. Furthermore, note that in the case of ML-based solutions, and specially RL-based solutions, their training can be performed on the NDT instance, as discussed in the next section.
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
###### Abstract  
Nodes in Mobile Opportunistic Network (MON) have to cache packets to deal with the intermittent connection. The buffer management strategy obviously impacts the performance of MON, and it attracts more attention recently. Due to the limited storage capacity of nodes, traditional buffer management strategies just drop messages based on the property of message, and they neglect the collaboration between neighbors, resulting in an ineffective performance improvement. Therefore, effective buffer management strategies are necessary to ensure that each node has enough buffer space to store the message when the node buffer is close to congestion. In this paper, we propose a buffer management strategy by integrating the characteristics of messages and nodes, and migrate the redundant messages to the neighbor to optimize the total utility, instead of deleting them. The simulation experiment results show that it can obviously improve the delivery ratio, the overhead ratio and the average delays, and reduce the amount of hops compared with the traditional ones.  
mobile opportunistic network; buffer management; node utility; message migration +
Footnote †: journal:  
0000-0001-419-3399/10/0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-0  
such as the lifetime or hops, and delete messages with lower priority when the buffer overflows. They do not consider the impact of collaboration between neighbors on the performance of the buffer management strategy. In addition, many works only consider the message attributes, while ignoring the characteristics of the nodes in MON [9; 10; 11]. Taking into account the above facts, we propose a reasonable queuing strategy for the buffer management strategy from the perspective of the message and node, which includes the size of the message, the hop count, time-to-live (TTL), and the node utility; meanwhile, our strategy includes an efficient message migration scheme that effectively deletes and transfers messages. We carried out the simulation experiment with four metrics: delivery ratio, overhead ratio, average delivery delay, and hops, respectively. Our goal is to create a comprehensive buffer management policy in mobile opportunistic network. The main contributions are summarized as follows.  
* We integrate the attributes of nodes into the utility value of messages and decide whether to receive a new message based on the utility value of the message and that of the node.
* We migrate the messages to the neighbor, rather than deleting them when the buffer space of nodes is full.  
The rest of this paper is organized as follows. We first review related works in Section 2. In Section 3, we describe the buffer management policy in detail. In Section 4, we conduct the simulation experiment. Finally, we conclude this paper and give a short discussion on future issues in Section 5.
## 2 Related Works
In a dynamically changing network, link quality is critically affected, resulting in frequent link failure in the network. Therefore, it is paramount to consider both link quality and switch resource parameters during routing path selection decisions. This is a non-trivial problem because link quality and switch resources provide information on how well the network can handle additional demand as the network evolves. To this end, this paper proposed an optimized route selection scheme-based link quality estimation and critical switch awareness.
# Analysis of Uncertainty in Conveyor Belt Condition Assessment Using Time-Based Indicators
## 3 Results
### Multiple Regression Analysis  
To predict Damage Density using available operational metrics, a multiple regression model was constructed with the following variables: Age, Resurs, Loop Length, and Section Length.  
Initial Model:  
\[\text{D}=0.00387\cdot\text{A}+0.57366\cdot\text{R}-0.000215\cdot\text{LL}+0.002 29\cdot\text{SL} \tag{1}\]  
where:  
* D--Damage Density [1/m]
* A--Age [months]
* R--Resurs [%]
* LL--Loop Length [m]
* SL--Section Length [m]
* R\({}^{2}=46.74\%\), Adjusted R\({}^{2}=46.31\%\)
* Standard Error = 0.890, MAE = 0.563
* Durbin-Watson Statistic = 0.557 (indicating possible autocorrelation)  
Figure 11: Calculated Pearson product–moment correlations.  
While the model showed a moderate fit, the variable Age had a \(p\)-value of 0.0575, indicating it was not statistically significant at the 95% confidence level. Therefore, it was removed in the final model.  
Final Model:  
\[\mathrm{D=0.62643\cdot R-0.000138\cdot LL+0.00241\cdot SL} \tag{2}\]  
* \(\mathrm{R^{2}=46.22\%}\), Adjusted \(\mathrm{R^{2}=45.93\%}\)
* Standard Error = 0.894, MAE = 0.588
* All the remaining variables are statistically significant (\(p<0.05\))  
For the final model, the Durbin-Watson statistic was calculated and yielded a value of 0.570, indicating strong positive autocorrelation in the residuals. This is likely due to the structure of the dataset, which was sorted by conveyor loop and belt segment. As a result, neighboring rows in the dataset often correspond to segments affected by similar operational conditions.  
The predictor variable Age was excluded from the model due to its strong correlation with Loop Length (PPMC = 0.79), which reduced the risk of multicollinearity.  
A residual plot (Figure 12) illustrates Studentized residuals plotted against the Resurs value. A group of seven outliers (residuals \(\geq\) 3) is visible, corresponding to segments with Resurs > 0.8, located on a loop of 1881 m, operating in an overburden transport system. These segments showed very high Damage Density values (up to 4-5), indicating severe wear beyond the limits for refurbishment.  
Component effect plots (Figure 13) and predicted vs. actual plots (Figure 14) further validate the model's performance. Though the \(\mathrm{R^{2}}\) values indicate moderate explanatory power, the model can be a useful tool for forecasting belt Damage Density using existing database parameters, which are retained (Age in months) and calculated (Resurs) for all belt segments in the mine in the Tasma program.  
The results are plotted on two charts: the Component Effects Plot (Figure 13) and Plot of Belt Section Density with predicted values (Figure 14). We see significant deviations in actual values from the predicted values. This is a consequence of the fact that the obtained model explains only 46% of the variability.  
Figure 12: Studentized residuals of the final model as a function of the Resurs value. Outliers (\(\geq\)3) correspond to excessively worn belt segments operating on overburden conveyors.
## 4 Discussion
GANF is the best-performing deep learning-based method in this study, which suggests that density-estimation-based methods are effective when it comes to detecting anomalous sequences. Additionally, GANF is capable of being applied online once trained and has the potential to learn the dependency graph of multiple time series, which, although not analyzed in this study, could be beneficial in specific applications. A major disadvantage of GANF is that it requires values to be selected for numerous hyperparameters. In our experiments, we used Bayesian Optimization to determine suitable values for the three most important hyperparameters (latent space dimension, learning rate and number of blocks), as identified by [12], using 10% of the time series in the UCR Anomaly Archive. We used the default values from [12] for the remaining eight hyperparameters, as they had not been tuned in that study either. Using default values from [12] for the three tuned hyperparameters leads to a decrease in the F1 score of 2-5% and a drop in the UCR score of up to 19%. However, averaging the two sets of hyperparameter values used in [12] increases the UCR score by approximately 6%. These better hyperparameters were not identified during the hyperparameter search, which highlights the general disadvantage of methods with a high number of hyperparameters. The results for AE and TranAD are inconclusive, but are generally worse when compared to GANF. However, they are not as poor as the results for RRCF. Additionally, these two methods also have the disadvantage of having various hyperparameters that need to be set. When using the default parameters from [13] for TranAD, the results for F1- and UCR score decrease by about 4-2%, depending on which set is used. The values mentioned in the paper differ from those used in the repository. For AE, we do not have a set of default parameters, but we observed comparable or slightly worse results when choosing an arbitrary set of parameters. Like GANF, both methods have the advantage of being able to be applied to data streams after training.
## 4 Challenges and Limitations of Nanosensor Deployment
### Environmental Factors and Durability
#### 4.3.2 Durability against Nanoscale Wear and Tear  
The durability of nanosensors could be compromised by nanoscale wear and tear, not typically encountered by their macro-scale counterparts. Developing materials and deployment strategies that safeguard them against nanoscale erosion, possibly due to interactions with atmospheric molecules or other nanomaterials, is imperative [97].  
Incorporating specialized maintenance protocols, which ensure the stability and functionality of nanosensors amid the distinct and complex challenges they encounter, underscores the prerequisite for a comprehensive strategy, bridging nanotechnology and SHM to advance the future of resilient infrastructure [98].
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 3 Singular Value Decomposition (SVD)
### Proper Orthogonal Modes (POD)  
One useful measurement is the proper orthogonal modes (POD) which are the optimal distributions of signal power; the calculation of the POD by using modal projection is done with the following loop:  
\[\begin{array}{c}\text{For}\ j=1:k\\ A_{k}=u(:,1:j)\times\sigma(1:j,1:j)\times v(:,1:j)^{T}\\ \text{End}\end{array} \tag{16}\]  
The loop above processes the sum of the first to \(k\) modes, resulting in the modes of interest, the energy in each mode and the \(POD\) approximation is computed in the following manner:  
\[sig=\text{diagonal}(\sigma) \tag{17}\]  
\[energy(r)=\frac{sig(1:k)}{sum(sig)} \tag{18}\]  
Alternatively, by computing the log of the diagonal singular values \(\sigma_{ii}\). Regarding the selection of PODs, a well-known solution based on a scree plot was developed by [PERSON] [33] as shown in Figure 6. The PODs can be graphically found by localizing the inflection point on the semi-log scale where the PODs remain flat, and where the sloppy line and the flatline intersect (\"elbow\"). After plotting the entire spectrum of singular values, it is expected a clear dominance of the first modes, those are the columns of the matrix \(U\) or column space, and constitute the orthonormal expansion basis of interest, where \(\sigma_{ii}\) are the first \(k\) singular values of interest.  
Figure 6: Scree plot.
# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 3 Proposed Scheme
### Workflow of the Proposed Scheme  
This section describes the process flow of the proposed scheme based on four phases: First, clusters are assigned for a set of virtual edge nodes (VEN) and IoMT devices. Secondly, data transmission from IoMT devices to the physical base station is secured to identify any possibility of replay and delay attacks. Thirdly, data received by the base station is encrypted using homomorphic encryption. Fourthly, secret sharing distributes encrypted data to multiple VENs in a cluster, preventing untrusted cloud servers from determining the computation process. Finally, the decrypted results by the DU are sent back to the IoMT devices (DU). Figure 2 illustrates the workflow process of all four phases in the proposed scheme.  
Figure 1: Proposed scheme overview.
# A 60 GHz CMOS I/Q Receiver for High-Speed Wireless Communication System
### Design of 60 GHz LO Buffer Amplifier  
The LO buffer proposed in this work adopts a cascode structure. The schematic of the LO buffer amplifier is shown in Figure 4. It consists of 2 stages of cascode differential amplifier stages. The first stage amplifier has a gain control feedback network. The second stage has only a cascode amplifier to boost the gain of the first stage. The same switched resistive feedback in S2D is implemented to control the gain. The cascode topology is chosen to achieve the advantage of its higher gain and proper isolation of input and output ports which facilitates impedance matching. The transformer is considered to form the part of the matching network. The simulated gain of the 60 GHz LO buffer amplifier is 16 dB with a 4 dB gain control range.
# NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing
## 3 Compressed Spectrum Sensing
However, by taking advantage of the sparse nature of the spectrum, we can transform the problem into the following \(l_{0}\)-norm minimization problem:  
\[\underset{\theta(f)\in C}{\arg\min}\;\big{|}\hat{\theta}(f)\big{|}_{0}\;\; \text{s.t.}\;\;\big{|}y(f)-\textbf{A}\hat{\theta}(f)\big{|}_{2}<\epsilon. \tag{7}\]  
This optimization problem is NP-hard but can be approximated by several methods within polynomial time, e.g., convex optimization methods such as basis pursuit (BP), greedy algorithms such as orthogonal matching pursuit (OMP), as well as sparse Bayesian learning algorithm. Among the approaches, greedy algorithms cost far less computation burden than optimization algorithms and yield acceptable solution in high-SNR scenarios. Thus, greedy algorithms, instead of optimization algorithms, are usually adopted in the practical system for better real-time performance. With the above sub-Nyquist sampling and spectrum reconstruction scheme, the average sampling rate acquired by the Ku-band satellites can be significantly reduced. We used a satellite ground station to monitor a Ku satellite, as shown in Figure 2, and obtained the real star signal spectrum of the satellite in the 500 MHz band range (from 12,300 MHz to 12,800 MHz). The frequency became 1-1.5 GHz after antenna reception and down-frequency conversion amplification (as shown in Figure 3). Six unused spectrum holes were extracted which occupied spectrum band of 296 MHz. Moreover, 69 subcarriers can be allocated in these unused spectra (as shown in Table 2). Figure 2: The satellite ground station.