esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The charging/discharging rate of the battery is controlled based on the frequency droo characteristic. In [16], the authors propose a power transfer strategy (PTS) by which, through a specific EMS, output fluctuation of renewable energy, load demand, state of the grid, peak load period and state of charge (SOC) of the battery are taken into account to effectively operate the grid-connected hybrid system. Numerous models are developed by considering multiple sources of power [17; 18; 19]. Sizing of components in distributed renewable energy systems are analyzed in [17], while in [18; 19], methods that regulate the power generation units and the energy storage are proposed, with reference to the state of energy storage. In these studies, PV/Wind/Battery systems are considered. In [19], particularly, a Hybrid Power System (HPS) for residential applications is analyzed. The system is characterized by two PV panels inclined in two different directions, one Wind Generation System (WGS) and Battery Storage (BS) connected to a DC bus and then to the AC side through pulse width modulation inverter. In addition to the solar-batteries technology, an efficient residential Energy Management System has to consider Demand Response (DR) potential. DR encourages the interaction and responsiveness of the customers by offering a broad range of benefits on system operation and on market efficiency. In [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32], these issues are analyzed and different solutions are proposed, with particular reference to the benefits in a smart grid and to the market efficiency. A probabilistic methodology for evaluating the impact of residential DR choices is proposed, by using a combination of Monte Carlo Simulation (MCS) and Real Time (RT) distribution market simulation based on Distribution Locational Marginal Price (D-LMPs) in [20; 25]. Real-time pricing (RTP) at the residential level is considered in combination with small-scale Distributed Generation (DG), in order to suggest an energy scheduling solution [23]. In [26; 30; 31], electrical and thermal appliances are jointly scheduled, considering the residents' consumption behavior, seasonal probability and other aspects. The economic aspects related to the installation of distributed energy storage systems designed to reduce the electricity cost for a customer-side application, are discussed in [32], by considering flexible electricity tariffs. In these works, the economic benefits of matching the load demand to power supply are evaluated; furthermore, objectives like peak shaving and reduction of energy costs are focused with the aim of not harming the comfort of residents. The aim of this paper is to highlight, based on the literature described above, the features that an efficient energy management system must have in order to optimize the aspects of cost saving, comfort and efficient utilization of renewable sources and storage systems. To this end, the proposed system develops, in an innovative way, the simultaneous management of the shiftable loads, the storage of electric power and the PV system considering the real-time price acquisition.
# Noise Reduction Method of Nanopore Based on Wavelet and Kalman Filter
## 3 The Kalman Filter Method  
Kalman filtering is a kind of sequential data assimilation, using the minimum mean square error as the best estimation criterion, using the state space model of signal and noise, using the estimated value of the previous moment and the observed value of the current moment to update the estimation of the state variables, and finding the estimated value of the current moment, the algorithm makes an estimate of the signal to be processed that satisfies the minimum mean square error according to the established system equation and the observed equation [24]. The following is the Kalman filter correlation equation:  
\[X(k|k-1)=AX(k|k-1)+BU(k) \tag{8}\]  
\[P(k\Big{|}k-1)=AP(k\Big{|}k-1)A^{{}^{\prime}}+Q \tag{9}\]  
\[X(k|k)=X(k|k-1)\]  
\[+Kg(k)(Z(k)-HX(k|k-1)) \tag{10}\]  
\[Kg(k)=P(k|k-1)H^{\prime} \tag{11}\]  
\[/(HP(k|k-1)H^{\prime}+R) \tag{12}\]  
\[P(k|k)=(1-Kg(k)H)P(k|k-1)\]  
Equation (8) is the system state model update equation, and \(X(k)\) is the predicted value of the system, and P is the system covariance. Based on the predicted value and the measured value \(Z(k)\), the optimal estimation of the current state is carried out, where \(Kg(k)\) is the Kalman gain, and the covariance of the current state needs to be updated at the same time, and the optimal estimation of the signal can be completed through continuous recursion.  
It can be known from (10) that the measured value \(Z(k)\) has an effect on the Kalman filter estimated value \(X(k\mid k)\), and the \(Kg(k)\) represents the magnitude of this effect. The noise in the current signal will make the gain smaller and smaller, which affects the estimation accuracy of the Kalman filter [25; 26; 27; 28]. According to the four wavelet transform processing results in the previous section, it is known that the wavelet mode maxima method is a better method for noise reduction of nanopore signal, so the mode maxima method is chosen to process the nanopore current signal first, enhance the Kalman filter gain through noise reduction, improve the estimation accuracy of Kalman filter, and optimize the signal processing results [29; 30; 31].  
Due to the large amount of data, part of the IgG nanopore current signal was intercepted for testing, as shown in Figure 6.  
Figure 5: Processing of IgG antibody signals by modulus maximum method.
# \(L_{1}\) Adaptive Control for a Vertical Rotor Orientation System
## 3 \(L_{1}\) Adaptive Control Design of the Rotor Orientation System
### \(L_{1}\) Adaptive Control Algorithm  
To streamline the following derivation, we need to use the projection operator \(\text{Proj}(\cdot,\cdot)\), which is defined in [12]. The projection operator has the following property: For any \(\mathbf{y}\in\mathbb{R}^{n}\), \(\mathbf{\theta}^{*}\in\Omega_{0}\subset\Omega_{c}\) and \(\mathbf{\theta}\in\Omega_{c}\), we have:  
\[\left(\mathbf{\theta}-\mathbf{\theta}^{*}\right)^{\top}\left(\text{Proj}(\mathbf{\theta}, \mathbf{y})-\mathbf{y}\right)\leq 0 \tag{13}\]  
With the projection operator, the \(L_{1}\) adaptive control algorithm can be expressed as:\[\hat{\mathbf{\hat{K}}}_{\mathbf{\theta}}=\Gamma\mathrm{Proj}\left(\hat{\mathbf{K}}_{\mathbf{\theta}}, -\mathbf{B}^{\top}\mathbf{P}\mathbf{e}\|\mathbf{x}(t)\|_{L_{\infty}}\right),\quad\hat{\mathbf{K}}_{ \mathbf{\theta}}(0)=\hat{\mathbf{K}}_{\mathbf{\theta}0}\in\Theta \tag{14}\]  
where \(\Gamma>0\) is the adaptive gain and \(\mathbf{P}=\mathbf{P}^{\top}>\mathbf{0}\) is a positive matrix that solves the Lyapunov equation \(\mathbf{A}_{\mathrm{m}}\mathbf{P}+\mathbf{P}\mathbf{A}_{\mathrm{m}}=-\mathbf{Q}\) for any \(\mathbf{Q}=\mathbf{Q}^{\top}>\mathbf{0}\). \(\mathbf{e}(t)=\hat{\mathbf{x}}(t)-\mathbf{x}(t)\) is the prediction error. The projection operator ensures that \(\hat{\mathbf{K}}_{\mathbf{\theta}}\in\Theta\) for all \(t\geq 0\).  
The adaptive control signal is:  
\[\mathbf{u}_{\mathrm{ad}}(s)=-C(s)(\hat{\mathbf{\eta}}(s)+\mathbf{r}_{g}) \tag{15}\]  
where \(C(s)\) is a low-pass filter, \(\hat{\mathbf{\eta}}(s)\) is the Laplace transform of \(\hat{\mathbf{\eta}}(t)=\hat{\mathbf{K}}_{\theta}\|\mathbf{x}(t)\|_{\infty}\) and \(\mathbf{r}_{g}\) is the reference input signal. In this paper, the control aim is \(\mathbf{x}=\mathbf{0}\) at \(\mathbf{u}=\mathbf{0}\), so \(r_{g}=\left[0\quad 0\right]^{\top}\).  
The block diagram of the adaptive control is illustrated in Figure 2.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 3 Methodology  
This paper presents an end-to-end variational neural network for the blind motion deblurring of images. The approach begins with image feature extraction, followed by the estimation of the blur kernel using the HQS method combined with an \(L_{0}\) regularizer. Subsequently, proximal gradient descent is employed for iterative image restoration.
# Convective-Microwave-IR Hybrid Drying of Kaolin Clay--Kinetics of Process
## 3 Results and Discussion
### Energy Consumption  
Today, energy consumption is one of the most important parameters in determining the usefulness of processes. New techniques are expected to be energy-efficient and sustainable. Hybrid drying, which combines several drying techniques into one process, is part of this trend. The drying techniques are selected in such a way as to eliminate the disadvantages and highlight the advantages of each of them. For example, the combination of convection and microwave drying allows the whole body to be heated, which leads to faster and shorter drying, eliminating the basic disadvantages of the convection technique. However, such a connection must be implemented in an appropriate manner. It should be remembered that supporting the convection process with additional energy sources (e.g., microwaves) requires powering additional systems, which requires higher instantaneous energy consumption. If such an enhancement does not translate into a sufficiently large shortening of the drying time, it will increase the energy consumption of the process.  
Figure 9 presents the values of specific energy consumptions for particular drying schedules. The SEC for convection (CV) was assigned as a reference. The gray area of the graph indicates a SEC higher than that of the reference process.  
The use of additional energy sources undoubtedly had a positive effect on the kinetics of convective drying. Unfortunately, in some cases, the benefits were not sufficient, and the SEC was significantly higher than for the reference process. In the case of processes where support was used only at the beginning, higher energy consumption was observed, and the increase was 2.8, 16.4 and 34.9% for schedules 3-5, respectively. This is undoubtedly caused by the too short operation time of additional energy sources and the long process time. The greatest reduction in energy consumption (51.3%) was observed for the convection-microwave (CV-MW) process, where microwaves were used continuously throughout the drying. Low energy consumption correlates with a short drying time; in this case, it was only 110 min. In the case of hybrid, nonstationary schedules (CV + MW\({}_{\text{per}}\)  
Figure 9: Specific energy consumption (SEC) for particular drying schedules.  
CV + (MW \(-\) IR)\({}_{\rm per}\)), the specific energy consumption was higher compared to CV \(-\) MW but lower than for CV (decrease of 18.1 and 3.6%, respectively). A positive result in relation to CV results from the periodic use of additional energy sources throughout the drying period, while a negative result in relation to CV-MW results from a longer drying time. This shows that a properly constructed hybrid drying program allows significant savings not only in terms of time but also energy while maintaining a high-quality product.
# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 2 Related Work
### Clustering in IoV
Moreover, lack of robust algorithms that will be able to untangle the challenges faced when tasked with ad hoc wireless multi-hop network routing is also addressed in the literature. In this context, we use promising techniques derived from the sphere of artificial intelligence to overcome this issue. The other gap in existing studies is how a problem that is dynamic, very challenging, and realisticcan be solved. In other words, most studies on swarm-based clustering are performed based on single-objective problems that are not appropriate, and are not able to solve dynamic multi-objective problems. In this regard, we proposed a new dynamic algorithm named DA-TRLD, which allocates dynamic transmission range for vehicle communication based on local traffic density to retain the connectivity of IoV network. Various optimization problems can be solved by ACO, which is a Swarm-Intelligence (SI) technique. This algorithm can be used in finding near-optimal solutions. Even though the ACO algorithm has really strong abilities in solution discovery, search for optimization problems, robustness, easy computer realization, and mechanism of distributed calculation, there are weaknesses such as the longer time for searches, slow speed in terms of convergence, usual stagnation, etc. A lot of researchers have proposed algorithms which are improved for ACO to overcome some shortcoming of ACO. In this regard, our proposed framework improved some drawbacks of the ACO such as uncertain convergence time and random decisions. We listed the pros and cons of the ACO protocol below:  
* Search can be conducted in parallel among a population. * It can adapt to changes such as novel distances. * It discovers good solutions rapidly. * Convergences are guaranteed. Cons:
* There is the probability of change in each iteration. * Difficulty in theoretical analysis and has more experimental basis than theoretical research. * Random decisions are dependent sequences. * Uncertain convergence time.
###### Abstract  
Amidst the rapid development of the fifth generation (5G) networks, Internet of Things (IoT) is considered as one of the most important part of 5G next generation networks as it can support massive object communications. These massive object communications in the context of IoT is expected to consume a huge power. Furthermore, IoT sensors or devices are rather power constrained and are mostly battery operated. Therefore, energy efficiency of such network of IoT devices is a major concern. On the other hand, energy harvesting (EH) is an emerging paradigm that allows the wireless nodes to recharge themselves through radio frequency (RF) signals directed to them from the source node and then relaying or transmitting the information. Although a myriad of works have been carried out in the literature for EH, the vast majority of those works only consider RF EH at the relay node and successfully transmitting the source node data. Those approaches do not consider the data transmission of the relay node that may be an energy deprived IoT node which needs to transmit its own data along with the source node data to their respective destination nodes. Therefore, in this paper, we envisioned a RF EH and information transmission system based on time switching (TS) relaying, power splitting (PS) relaying and non-orthogonal multiple access (NOMA) which is suitable for wireless powered IoT relay systems. A source node information data is relayed through power constrained IoT relay node \(IoT_{R}\) that first harvests the energy from source node RF signal using either TS and PS relaying protocol and then transmits the source node information along with its information using NOMA protocol to the respective destination nodes. Considering NOMA as a transmission protocol, we have mathematically derived analytical expressions for TS and PS relaying protocol for our proposed system. We have also formulated an algorithm to find out optimal TS and PS factor that maximizes the sum-throughput for our proposed system. Our proposed system analytical results for TS and PS protocol are validated by the simulation results.  
Internet of Things; time switching; power splitting; NOMA; energy harvesting; radio frequency; relaying; outage probability; sum-throughput +
Footnote †: journal: Sensors
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 2 Methodology
### Quality Assessment Framework  
The quality assessment method was used to evaluate each paper, from the result found in stage 2, across three key dimensions: technical depth, research contribution, and implementation maturity.  
The technical depth dimension examined the level of implementation detail and validation. For instance, papers like [21], which presented a novel application architecture with detailed security testing framework, showed the highest score (3) in this category. Papers with partial implementation or simulation results, such as [18], received a medium score (2), while conceptual studies without implementation details were assigned the lowest score (1).  
For research contributions, we evaluated the novelty and significance of each paper's approach. The highest scores were awarded to papers that introduced novel approaches advancing the field, such as [16] that proposed a grid intelligence enhancement framework using DTs. Papers that offered significant improvements to existing methods, like the method in [20] that showed an enhanced energy management system, received medium scores, while those presenting minor modifications to existing approaches were scored lowest.  
Implementation maturity was assessed based on the level of practical deployment and validation. Production-level implementations with documented results, like the real-time monitoring system proposed in [19], earned the highest scores. Pilot implementations and extensive simulations were scored as medium, while theoretical proposals or limited prototypes received the lowest scores.  
Several remarkable trends resulted from this assessment. For example, security-focused papers [22; 23; 24] consistently achieved high technical depth scores, while AI integration studies [19; 20] demonstrated particularly strong research contributions. Real-world implementations [16; 17] excelled in implementation maturity, and papers published between 2022 and 2024 generally scored higher in technical depth, indicating increasing use of cutting-edge technologies in recent research.  
For the final analysis, we selected papers that scored at least medium in two dimensions and showed no low scores in implementation maturity. This rigorous selection process resulted in 49 papers that formed a robust foundation for our subsequent analysis.
## 4 Results and Discussions  
In this section, the OOA and WOA are implemented to find the optimal locations and sizes of the wind turbines in the IEEE 85-node RDPG for active power loss reduction. All of the related data for the considered distribution network are cited by [22]. Originally, active and reactive power demand by all loads connected to the grid was 2570.28 kW and 2622.08 kvar. In addition, the original active and reactive power loss of the grid are 224.32 kW and 141.00 kvar. The single-line illustration of the grid is depicted in Figure 1. The placement of WTs in the IEEE 85-node RDPG is implemented in two cases as follows:  
* Case 1: Placing two WTs. The WTs generate active power only. Suppose that the active power supplied by WTs varies from 0 to 3000 kW.
* Case 2: Placing two WTs. The two WTs can generate both active and reactive power. The active power generation capacity is the same as Case 1. In addition, the power factor is constricted within the range from 0.85 to 0.95 lead as evaluated in [35; 36]. In practice, the particular value of power factors of WTs is decided by the operator and the grid's specific operational condition [37].
* Case 3: Placing two WTs. The two WTs can generate both active and reactive power. The active power generation capacity is the same as Case 2; however, the range of power factor for WTs are extended from 0.95 lag to 0.95 lead as conducted in [38; 39].
* Case 4: Evaluating the load demand variation and power supplied from WTs within 24 h in two scenarios:  
* Scenario 1: WTs' power factor is only optimized.  
The results obtained by the two algorithms will be discussed and compared based on different criteria. For a fair comparison, the initial control parameters of the applied algorithms, population size (\(N_{ps}\)), and the highest iteration (\(H\Pi\)), are equally set to 20 and 50 for Case 1, and 30 and 50 for Case 2, Case 3, and Case 4. In addition, each algorithm is operated for 50 trial runs to find the best solution.  
Suppose that the considered RDPG IEEE-85 bus is located in the plain. Besides that, except for the bus one, the remaining busses of the network are supposed to satisfy all of the engineering conditions for placing WTs, including the availability of space, short-circuit ratings of existing switchgear, circuit capacity, the feasibility of connection, and environmental effects. On top of that, the availability of wind power on the buses having WTs is also fulfilled.  
The whole study is conducted on a personal computer with the following basic specifications: 2.6 GHz of CPU and 8 GB of RAM.
# New Results for the Error Rate Performance of LoRa Systems over Fading Channels
## 3 Main Results
### Symbol Error Probability for Physical \(\eta\)-\(\mu\) Fading Channels  
Under \(\eta\)-\(\mu\) fading, the PDF of \(h^{2}\) is given by [27]  
\[f_{h^{2}}(y)=\frac{2\sqrt{\pi}\mu^{\mu+0.5}\theta\mu^{\
u}\mu^{-0.5}}{\Gamma( \mu)H^{\mu-0.5}}\exp(-2\mu\theta y)I_{\mu-0.5}(2\mu_{\ell}H\gamma) \tag{38}\]  
where \(\mu\) is related to the fading severity. The \(\eta\)-\(\mu\) fading is quite general as it can accurately model small-scale variations of the fading signal under non line-of-sight (NLOS) conditions and includes as special cases both the Nakagami-\(m\) and the Hoyt fading models. The PDF of \(h^{2}\) may be expressed in two formats, namely Format 1, where \(\theta=(2+\eta^{-1}+\eta)/4\) and \(H=(\eta^{-1}-\eta)/4\) with \(0<\eta<\infty\) and Format 2, where \(\theta=1/(1-\eta^{2})\) and \(H=\eta/(1-\eta^{2})\) with \(-1<\eta<1\). As pointed out in [27], Format 1 can be converted into Format 2 by employing a bilinear transformation. Thus and without loss of generality, Format 1 will be assumed next. Moreover, the special case of integer \(\mu\), termed _physical_\(\eta\)-\(\mu\) model, assumes a finite number of multipath clusters and has been adopted in several works, e.g., see [27; 37; 38; 39]  
Using [30] (Equation (8.467)), the modified Bessel function \(I_{\pm(n+1/2)}(z)\), with \(n>0\) being an integer, can be expressed as a finite sum, namely  
\[I_{\pm(n+1/2)}(z)=\frac{1}{\sqrt{\pi}}\sum_{k=0}^{n}\frac{(n+k)!}{n!(n-k)!} \left[\frac{(-1)^{k}e^{z}\mp(-1)^{n}e^{-z}}{(2z)^{k+0.5}}\right]\!. \tag{39}\]  
Substituting (39) into (38) and employing [30] (Equation (8.353/7)), \(f_{h^{2}}(y)\) can be expressed as \[f_{h^{2}}(y)=\frac{\mu\theta}{H\Gamma(\mu)}\sum_{k=0}^{h-1}a_{k}y^{\mu-k-1}\Big{[} (-1)^{k}e^{-\mathcal{A}y}+(-1)^{\mu}e^{-\mathcal{B}y}\Big{]} \tag{40}\]  
where  
\[a_{k}=\frac{(-1)^{k}(\mu+k-1)!(4\mu H)^{-k}}{k!(\mu-k-1)!} \tag{41a}\] \[\mathcal{A}=2\mu(\theta-H),\ \mathcal{B}=2\mu(\theta+H). \tag{41b}\]  
An exact expression for \(P_{s}\) can be obtained using the following proposition.  
\[P_{s}^{\eta-\mu}=\frac{0.5}{\Gamma(\mu)}\bigg{(}\frac{\mu\theta }{H\gamma}\bigg{)}^{\mu}\bigg{\{}\sum_{k=0}^{h-1}a_{k}\gamma^{k}(-1)^{k}\Gamma (\mu-k)2^{\mu-k}\bigg{[}(-1)^{k}\bigg{(}\lambda+\frac{2\mathcal{A}}{\gamma} \bigg{)}^{k-\mu}\] \[\times\int_{0}^{\infty}e^{-x/2}[1-(1-e^{-x/2})^{L}]\mathrm{L}_{k- \mu}\bigg{[}\frac{\lambda\gamma x}{2(\lambda\gamma+2\mathcal{A})}\bigg{]} \mathrm{d}x\] \[+(-1)^{\mu}\bigg{(}\lambda+\frac{2\mathcal{B}}{\gamma}\bigg{)}^{ k-\mu}\int_{0}^{\infty}e^{-x/2}[1-(1-e^{-x/2})^{L}]\mathrm{L}_{k-\mu}\bigg{[} \frac{\lambda\gamma x}{2(\lambda\gamma+2\mathcal{B})}\bigg{]}\mathrm{d}x\, \big{]}\}. \tag{42}\]  
**Proposition 8**.: _The exact symbol error probability of LoRa systems operating under physical \(\eta\)-\(\mu\) fading channels can be expressed in terms of a single integral as (42)._  
Proof.: The proof can be readily deduced by following the same steps as in the proof of Proposition 1.