esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
## 1 Introduction
[PERSON] [15] used the multi-factor hidden Markov algorithm to disaggregate the continuous value of each electrical appliance according to the given total power data. Some excellent machine learning algorithms, such as the support vector machine [16] and the adaboost algorithm [17], achieved certain processes, but these methods shared the same problem: a large number of load characteristics were required for identification, a requirement that was often difficult to meet in practice. Different from traditional methods, the deep learning method [18; 19] is able to automatically extract features from original data [20]. In [PERSON]'s [21] experiment, various NILD algorithms using deep learning were proposed, such as the Delousing AutoEncoder (DAE), the long-short term memory network (LSTM), the gatedrecurrent unit (GRU), the Factorial Hidden Markov model (FHMM), and the CO method. The DAE algorithm was proven to have good disaggregation results. [PERSON] [22] used two convolutional neural network algorithms for load disaggregation. Compared with [PERSON]'s method, the two CNN methods, sequence-to-sequence and sequence-to-point, achieved better performance [23], but their layer numbers were small, and hence there were unable to extract higher level load characteristics. In the above methods, the CO algorithm, the DAE, and the two CNN methods were all trained by low-frequency data from the REDD dataset, which was first processed by the NILM-TK toolkit. The sampling interval of the data was 3 s. With an improvement of the model structure, [PERSON] [24] proposed a semisupervised deep learning framework based on BiLSTM and the temporal convolutional network for multi-label load classification. [PERSON] [25] proposed a multilayer deep neural network based on the sequence-to-sequence methodology, and the algorithm, by reading the daily load profile for the total power consumption, could identify the state of the appliances according to the device-specific power signature. Since the neural networks were only trained for each appliance and the computational cost was high, [PERSON] [26] proposed UNet-NILM for multi-task appliances' state detection and power estimation, which had a good performance compared with traditional single-task deep learning. The innovation of the algorithm proposed in this paper lies in the following: The multi-scale structure is used to extract different load information according to the characteristics of load disaggregation. The attention mechanism is used to fuse the load information at different scales to further enhance the feature extraction ability of the network, especially for the extraction of electrical features that are not frequently used. The overall architecture uses a skip connection of the residual network [27] to improve network performance. Experimental results on two benchmark datasets show that our method is superior to other present methods.
## 2 Materials and Methods
### Mathematical Model
#### 2.2.2 Kinetics and Equilibrium Models  
In order to describe the adsorption rate of the water vapour on zeolite, a linear driving force (LDF) model has been implemented. This type of model is widely used for describing adsorption and desorption processes due to its analytical simplicity and physical consistency [26; 27; 28]. Its mathematical expression is the following:\[\frac{dx}{dt}=K_{LDF}(T_{r},p)\left(x_{eq}(T_{r},p)-x\right) \tag{11}\]  
where \(x\) and \(x_{eq}\) are, respectively, the adsorbed and equilibrium water loading per kg of dry zeolite, and \(K_{LDF}\) is the kinetics coefficient depending on the bed temperature and the water vapour pressure, according to the following equation:  
\[K_{LDF}(T_{r},p)=\ \frac{15\ D_{eff}\ \varepsilon_{b}}{{R_{p}}^{2}}\ \frac{ \varepsilon_{p}}{\rho_{z}R_{W}T_{r}}\left(\frac{\partial x_{eq}}{\partial p} \right)^{-1} \tag{12}\]  
where \(D_{eff}\) is the macropore diffusivity which represents the dominant part of the mass transfer resistance of water vapour on zeolite [28].  
The adsorption equilibrium \(x_{eq}\) is modelled using Langmuir-Freundlich isotherms, modified by the Aranovich and Donohue term [29], which takes into account the condensation effects occurring when water vapour approaches the saturation pressure. It can be expressed with the following equation:  
\[x_{eq}(T_{r},p)=\ x_{max}\ \frac{\left(b\ p\right)^{1/k}}{1+\left(b\ p\right)^{ 1/k}}\left(1-\frac{p}{p_{sat}}\right)^{-d} \tag{13}\]  
where \(x_{max}\) is the maximum amount of adsorbed water, \(k\) and \(d\) are system-dependent parameters, while \(b\) is a temperature-dependent parameter which can be described as follows:  
\[b(T_{r})=\ b_{0}\ \exp\left(\frac{\Delta H}{R_{W}T_{r}}\right) \tag{14}\]  
where \(b_{0}\) is the adsorption affinity and \(\Delta H\) is the activation energy.
###### Abstract  
This study introduces four novel configurations of first-order and second-order multifunction inverse filters in both voltage-mode (VM) and current-mode (CM) using second-generation voltage conveyors (VCIIs). The first-order VM and CM inverse filters utilize only three passive components together with one VCI for VM and two VCIIs for CM realizations, which can provide lowpass and highpass inverse filter responses. The latter, second-order VM and CM multifunction inverse filters, can be constructed using the corresponding first-order inverse filters as their core circuits. These filters offer all the basic inverse filter functions, including lowpass, bandpass, and highpass inverse responses with all gains obtained from the same design. All the inverse filter realizations are cascadable. No component matching requirements are necessary for all filter responses. The non-ideal effects of the VCII on the performance of the proposed inverse filters are thoroughly examined. To prove the feasibility of the designs, the PSPICE program performed several simulations, utilizing model parameters of 0.18 \(\upmu\)m CMOS technology. Some testing experiments were conducted using the commercially available IC-type AD844s for evaluating the practical performance of the designed inverse filters.  
second-generation voltage conveyor (VCII); inverse filter; first-order filter; second-order filter; voltage-mode circuit; current-mode circuit 2020
## 3 Experiment and Result Analysis
### Comparative Analysis of Fitting Equation and Measured Value  
According to the fitting equation obtained above, the twisting tension of the direct-twisting machine under different process conditions can be predicted, and the results of the comparison with the measured values are shown in Table 5.  
Figure 8: Fitting surface of the relationship between yarn tension of the balloon, spindle angular velocity, and yarn fineness.  
It can be seen from the above table that the predicted value obtained by using the fitting equation in this paper was very close to the actual measured value. For example, when the configuration of the twisting \(\mathrm{ray}/\mathrm{yarn}\) storage \(\mathrm{ray}/\mathrm{spindle}\) was: 220, 120, and 266 mm, and the yarn fineness was 930, 1100, 1440, 1670, 2200 dex, the absolute value of the absolute error about the twisting tension was 4.5 cN\(\sim\)26.0 cN, and the average value was 7.2 cN, the absolute value of the relative error was 0.28\(\sim\)1.92%, and the average was 0.38%. The results show that the research method and the established fitting equation of the twisting tension of the direct-twisting machine in this paper are feasible.
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 3 Construction of the Digital Twin Monitoring System
### Construction of the Main Modules of the Digital Twin Monitoring System
#### 3.1.2 Network Communication Module  
The network communication module is the data channel of the digital twin monitoring system for the roadheader. It connects to the twin data processing program using the TCP/IP protocol to achieve two-way exchange and two-way mapping of virtual and real data. The data protocol reads the required information in the byte stream according to different communication rule algorithms, and simultaneously converts the information within the client into a byte stream and sends it to the server. Correspondingly, when sending data, the network communication module reads the twin information and environmental information of the roadheader, encodes and concatenates this information according to the protocol, converts it into a byte stream, and sends it to the twin data exchange processing program of the roadheader.
# DNA-PRESENT: An Improved Security and Low-Latency, Lightweight Cryptographic Solution for IoT
## 3 Proposed DNA-PRESENT Block Cipher
### The Design Rationale  
DNA cryptography has served as a robust and fast technique in different scenarios while improving the security of modern cryptosystems [21; 47; 48; 49; 50; 51; 56; 57; 59; 62]. The core of the diffusion improvement concept resides in the monitored cost and performance of the system. Diffusion is achieved at the minimum possible cost of implementation, particularly from the hardware perspective. This section offers an in-depth perspective on the design rationale while articulating the justifications for the design decisions implemented. The importance of each layer is given as follows:  
* Left shift of three bits breaks the original sequence of the bits received from _SBoxLayer()_, and removing these three bits changes the sequence as a whole. It is evident from the hexadecimal notation that the entire sequence has changed. For example, if at any round the value of STATE from _SBoxLayer()_ is \"B3 AE7609 FAFF1F45\", after passing through the _LeftShift()_, the obtained bits are \"9D73B04 FD78 FA2D\".
* _DNAReplication()_ is applied on half of the sequence, which means the rest of the bits are unchanged after this iteration. The bits subjected to DNA replication have changed entirely, for example, if the STATE for the DNA layer is \"9D73B04 FD78 FA2D\", from the preceding phase and after the replication process, the output is \"9D73B04 FD78B0705D2\". The DNA replication process increases the diffusion and not the complexity [51; 56], as it only required 16 new GEs for implementation on hardware.
* The _RightShift()_ of nine bits improves the original sequence by breaking the already present bit sequence. If the value of STATE for the _RightShift()_ is \"9D73B04 FD80705D2\", then the sequence after the _RightShift()_ will be \"E94 EB9D827940382\", which is entirely different from the previous round bits. The permuted bits from Figure 13 are, when subjected to original pLayer(), the resultant matrix is presented as Figure 14, where the first cell \([S_{0,58,0}]\) indicates a transition of bit zero with bit 58; however, in the original design, this transition never happened. Similarly, for second cell \([S_{1,10,16}]\), indicate the initial transition of bit 1 to the 10 th position using new layers followed by the original pLayer() with the transition of this 10 th bit to the 16 th location.
* The reduced round concept is inspired by PRESENT's improvements from [63; 46] for low-power devices. They validated their improved design using the avalanche effect, throughput, and execution time. DNA-PRESENT has also observed 17 rounds and follows the same evaluation parameters for a fair comparison. Moreover, differential cryptanalysis is also used to establish confidence for the minimum number of rounds of DNA-PRESENT.  
Figure 12: The right shift of STATE in DNA-PRESENT.  
Figure 13: The cipher state after introducing three new layers in DNA-PRESENT.
# A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises
###### Abstract
[PERSON] [8] defined DT as an \"appropriately synchronised body of useful information (structure, function, and behaviour) of a physical entity in virtual space, with flows of information that enable convergence between the physical and virtual states\". In the machine tool sector where accuracy in the machined geometry is sought, DT has been used for error mapping simulation with the use of three axis milling machine tools [9]. In the tool making industry, even the accurate method of wire electro discharge machining is being improved by computer simulation [10]. The authors reported that although experimental modelling of wire deformation provided a slight increase in accuracy over computer simulation, computer simulation modelling was far more practical in terms of time and cost savings. In addition, DT models have been deployed in other sectors such as education [11], where DT has been implemented to aid electrical engineering students in diagnosing electrical faults in a controlled environment. This has given the students the opportunity to apply their learning to situations where it would otherwise not be safe to do so while also building versatility in tackling unexpected occurrences. Another example is service engineering [12], where DT has enabled the virtual commissioning of projects such as plants, production lines, control systems and complete robot systems. DT models have also been used in healthcare [13] and with continued effort and development, medical DTs can change the way personal health is approached. With increased data for diagnostics, the optimum diagnosis and treatment can be found, which could save lives. Furthermore, DT in the agriculture and food industry has been proposed in [14], which suggests the benefits of implementing DT in \"Agrifood\" industry ranges from improved quality and shelf life, cost reduction, minimised losses, improved logistics and more. From our review of the reported studies discussed briefly above, it is apparent that engineering and manufacturing are the fields in which DT and its enabling technologies are most developed with the most sophisticated DT models. These industries and companies are those with the largest resources. Industry sectors such as Agrifood, machine and tooling are considering upgrading their existing system and investigating a process to develop system linking the physical and digital spaces.
# Electric Load Forecasting Based on Deep Ensemble Learning
## 1 Introduction
### Related Works
#### 1.1.2 Intelligent Forecasting Methods
Initially, VMD is employed to decompose features into modal components representing various scales, which reduces the non-stationarity of the original sequence. Concurrently, the decomposed residuals represent the strongly nonlinear parts of the load data. By leveraging powerful algorithms, these features are predicted. Each modal component is forecasted using single-feature prediction through LSTM. Subsequently, all components are incorporated as multi-features into LightGBM for load forecasting. [PERSON] et al. (2016) introduced a multifrequency composite electric load forecasting model that blends CNN, GRU, and multiple linear regression (MLR). Initially, the time series load data undergo ensemble empirical mode decomposition (EEMD), reconstructing it into high and low frequencies. Notably, significant meteorological factors are incorporated within the high-frequency domain, which is then predicted using the CNN-GRU model. In contrast, the low-frequency section employs multiple linear regression for forecasting. Ultimately, predictions derived from each model are superimposed, yielding the final forecasting outcome. [PERSON] et al. (2017) established a combined framework based on LSTM and XGBoost, using the inverse error method to combine both results. [PERSON] et al. (2018) developed a time series model that combines a convolutional neural network with adaptive learning named ConvAdaRNN. This model first employs a CNN to extract relevant influencing factors of the electrical load. It then segments the dataset using its temporal characteristics based on the minor correlation in the time series. The AdaRNN model is utilized for the final prediction, and as a result, ConvAdaRNN achieves better prediction accuracy.
## 4 Analysis of the Impact of Air Quality on Residents' Health in China
### Research Method  
To analyze the interaction between endogenous variables, referring to the literature of [PERSON] [62], this paper constructed a cross-regional spatial simultaneous equation model.  
\[\mathbf{Y_{n}}\mathbf{=Y_{n}B+\mathbf{X_{n}C+Y_{n}\Lambda+U_{n}}} \tag{2}\]  
where, \(\mathbf{Y_{n}}\ =\ (y_{1n},y_{2n},\cdots,y_{mn})\), \(\mathbf{X_{n}}\ =\ (x_{j1n},x_{j2n},\cdots,x_{jkn})\), \(\mathbf{U_{n}}\ =\ (u_{1n},u_{2n},\cdots u_{mn})\), \(\mathbf{\widetilde{Y_{n}}}\ =\ (\mathbf{\overline{y}}_{1n},\mathbf{\overline{y}}_{2n}, \cdots,\mathbf{\overline{y}}_{mn})\), and \(\mathbf{\overline{y}}_{jn}\ =\ \mathbf{W_{n}}y_{jn}\), \(j\ =\ 1,\cdots,m\). \(y_{jn}\) represents the observed value of the endogenous variable in region n in the \(j^{th}\) equation. \(x_{jln}(l\ =\ 1,2,\cdots k)\) denotes the observed value of the \(l^{th}\) exogenous variable in the region n in the \(j^{th}\) equation. \(u_{jn}\) represents the random disturbance term in region n of the \(j^{th}\) equation. \(\mathbf{W_{n}}\) represents the weight matrix. \(\mathbf{B,C}\) and \(\mathbf{\Lambda}\) represent the parameter matrix of \(m\times m\), \(k\times m\) and \(m\times m\).  
This paper analyzed the impact of air quality on residents' health, using panel data.  
According to the variable description and Formula (1), the spatial panel simultaneous equation is,  
\[\mathit{Peh_{it}}\ =\ \beta_{1}w_{ij}AQI_{it}+\beta_{2}w_{ij}\mathit{Peh_{it }}+\beta_{3}AQI_{it}+\beta_{4}\mathit{Pup_{it}}+\beta_{5}\mathit{Dgv_{it}}+ \beta_{6}\mathit{Erg_{it}}+c_{i}+\gamma_{t}+u_{it} \tag{3}\]  
\[AQI_{it}\ =\ \alpha_{1}w_{ij}AQI_{it}+\alpha_{2}\mathit{Peh_{it-1}}+ \alpha_{3}\mathit{Eci_{it}}+\alpha_{4}\mathit{Pgdp_{it}}+\alpha_{5}\mathit{Psi }+\alpha_{6}\mathit{Tei}+\delta_{i}+\lambda_{t}+{v_{it}}^{0} \tag{4}\]  
where, \(i,j\) represent different provinces, \(i,j\ =\ 1,2,\cdots,n\), and \(i\
eq j\). \(t\) represents time, \(t\ =\ 1,2,\cdots,T\). \(\beta_{1},\beta_{2},\beta_{3},\beta_{4},\beta_{5},\beta_{6}\) and \(\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4},\alpha_{5},\alpha_{6},\alpha_{7}\) indicate the parameters to be estimated. \(w_{ij}\) indicates the spatial weight. When two provinces are not adjacent, \(w_{ij}\ =\ 0\). When two provinces are adjacent, \(w_{ij}\ =\ 1\). \(c_{i},\delta_{i}\) represent individual fixed effects. \(\gamma_{t},\lambda_{t}\) indicate time fixed effects. \(u_{it},v_{it}\) represent a random disturbance term.  
Estimating Formulas (3) and (4) will produce a simultaneous equation deviation. Therefore, this paper adopted a systematic estimation method, which is called 3 SLS.
## 2 Strain Gauge Location Optimization Based on a Minimum Correlation Criterion  
In order to estimate the aircraft wing servicing life, the main object is to monitor actual operational load/strain information, which is obtained by placing strain gauge sensors on the aircraft wings. By establishing the mapping relationship between the operational loads and the strain gauge data, the actual flight loads can be obtained by strain inversion. In this study, the optimization of the strain gauge locations of an aircraft wing box segment will be investigated based on a two-stage correlation measure method. The flowchart of this paper is given in Figure 1.  
The major steps of this study illustrated in Figure 1 are summarized here. Firstly, the flight operation loads of the aircraft wing are simulated based on the actual flight load condition, and then the training and testing datasets are obtained. The measured values of the strain gauges are obtained via the finite element model (FEM). For this proposed two-stage minimum correlation method, a Pearson correlation approach and a minimum distance constraint are taken to significantly decrease the strain gauge monitoring points in the first stage. Based on the above preliminary selection, an improved correlation coefficient method is proposed to further optimize the strain gauge locations in the second stage, in which a new strain gauge is determined by minimizing the average correlation with the preselected strain gauges. Many more details are presented in the following.  
Figure 1: Flowchart of two-stage strain gauge location optimization method.
## 4 Conclusions  
In this study, we demonstrated the characteristics of (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl on an AlN/sapphire substrate heterostructure. A quasi-2D structure was observed. Six XRD spectra peaks of  
Figure 5: Current-voltage (I-V) characteristics of Ag/(PEA)\({}_{2}\)PbBr\({}_{3}\)Cl on AlN/sapphire substrate heterostructure.  
Figure 6: (**a**) Schematic energy level diagram of Ag and Ag\({}_{2}\)O and their contact area with (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl (**b**) before and (**c**) after annealing.  
perovskite-like (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl on both the AlN/sapphire substrate and glass substrate were observed at around 10.83\({}^{\circ}\), 16.15\({}^{\circ}\), 21.50\({}^{\circ}\), 26.90\({}^{\circ}\), 32.38\({}^{\circ}\), and 37.93\({}^{\circ}\), corresponding to the (004), (006), (008), (0010), (0012), and (0014) phases of the quasi-2D (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl structure, respectively. The peak position of photoluminescence (PL) of (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl on the AlN/sapphire substrate heterostructure was 372 nm. The emission wavelength was lower than that of the traditional lead halide perovskite light-emitting diodes (typically 410 to 780 nm). This indicates the potential for application in UV perovskite light-emitting diodes. The conductivity between the Ag contact area and as-deposited (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl on the AlN was almost non-conductive. However, after annealing treatment at 150 and 300 \({}^{\circ}\)C for 10 min, the conductivity of the Ag/(PEA)\({}_{2}\)PbBr\({}_{3}\)Cl structure on the AlN/sapphire substrate improved. The work function mechanism of Ag\({}_{2}\)O with 5.7 eV caused the Fermi level of the (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl layer to lower because of the thermal equilibrium with Ag and Ag\({}_{2}\)O contacts after the annealing process, such that the electron in the Ag contact could easily transfer to the (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl layer via the thermionic field emission (TFE) mechanism. Due to having higher stability than that of 3D perovskite materials, the two-dimensional, perovskite-like (PEA)\({}_{2}\)PbBr\({}_{3}\)Cl layer could be a candidate material for UV LEDs and AlN-based devices for ohmic contact and high-power devices.  
Conceptualization, Y.-H.L. and L.-C.C.; methodology, J.-S.N.; formal analysis, Y.-H.L. and L.-C.C.; resources, Y.-H.L. and L.-C.C.; data curation, J.-S.N.; writing--original draft preparation, Y.-H.L. and L.-C.C.; writing--review and editing, L.-C.C.; supervision, L.-C.C.; funding acquisition, L.-C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council of Taiwan, grant number 111-2221-E-027-040-MY3.  
Not applicable.  
The data presented in this study are available in the article.  
This work was supported by the National Science and Technology Council of Taiwan (no. 111-2221-E-027-040-MY3).  
The authors declare no conflicts of interest.