esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision Support Application for Energy Consumption Forecasting
## 2 Material and Methods
### Genetic fuzzy systems for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL)  
GFS.FR.MOGUL forecasting methods have been proposed by [PERSON] et al. in 1995 [PERSON] et al. (1995). This method implements a Genetic Algorithm (GA) determining the structure of the fuzzy IF-THEN rules and the membership function parameters. This method considers two types of IF-THEN rules.  
* Descriptive rules
* Approximate/free semantic approaches  
In the first type, the linguistic labels illustrate a real-world semantic, and the linguistic labels are uniformly defined for all rules. In contrast, in the approximate approach, there is any associated linguistic label.  
To obtain an acceptable performance from Fuzzy Rule-Based Systems different statistical properties have to be considered [PERSON] et al. (1995). In the Generated Fuzzy Rule Bases (GFRB) obtained from MOGUL, two statistical properties will be considered, which are:  
* completeness
* consistency  
As an inductive approach to building GFRBSs is considered, both properties will be based on the existence of a training data set, Ep, composed of numerical input-output problem variable pairs. In the field of inductive learning \(\tau\)-completeness means that the system should be able to infer a proper output for all possible system input [PERSON] et al. (1995). Expressions (3)-(5), present this property mathematically formulated:  
\[C_{R}(e_{l})=\bigcup_{i=1\ldots t}R_{i}(e_{l})\geq\tau,\;l=1,\ldots,p \tag{3}\]  
\[\left[R_{i}(e_{l})=*\left(A_{i}\left(ex^{l}\right),B_{i}\left(ey^{l}\right) \right)\right] \tag{4}\]  
\[A_{i}\left(ex^{l}\right)=*\left(A_{i1}\left(ex_{1}^{l}\right),\ldots,A_{in} \left(ex_{n}^{l}\right)\right) \tag{5}\]  
where * is a t-norm, and \(R_{i}(e_{l})\) is the compatibility degree between the rule \(R_{i}\) and the example \(e_{l}\).  
For the Consistency of a FRBS, a generic set of IF-THEN rules is considered while it does not contain contradictions. It is necessary to relax the consistency property to consider the fuzzy rule bases, and it will be done by means of the positive and negative examples of concepts. Following equations present the examples of the positive and negative set for the rule \(R_{i}\).  
* Positive: \[E^{+}(R_{i})=\left\{e_{l}\in\frac{E_{p}}{R_{i}(e_{l})}\geq 0\right\}\] (6)  
Figure 3: The sequence of fuzzy rules creation in WM method.  
* Negative: \[E^{-}(R_{i})=\left\{e_{l}\in\frac{E_{p}}{R_{i}(e_{l})}=0\;\text{and}\;A_{i}\Big{(} ex^{l}\Big{)}>0\right\}\] (7) And by giving the value and Equations (8) and (9): \[n_{R_{i}}^{+}=\left|E^{+}(R_{i})\right|\] (8) \[n_{R_{i}}^{-}=\left|E^{-}(R_{i})\right|\] (9) We get that: \[R_{i}\;\text{is}\;k-\text{consistent}\;\text{when}\;n_{R_{i}}^{-}\leq k.n_{R_{ i}}^{+}\] (10)  
Therefore, the way to incorporate the satisfaction of this property in the designed GFRBSs is to encourage the generation of k-consistent rules. Those rules not verifying this property will be penalized so as not to allow them to be in the FRB finally generated.
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 6 Conclusions  
This study addresses a critical gap in the abstraction of the physical layer for sensing applications within ISAC systems. By leveraging existing channel models and developing algorithms for target detection and localization services, we demonstrate the feasibility of abstracting performance metrics essential for future system-level simulations.  
The findings highlight two distinct conditions for estimation: target-dominated, characterized by low errors, and noise-dominated, where high noise levels result in a uniform distribution of errors across all possible outcomes. We focused on the target-dominated condition to simplify the PLA process, which offers the most actionable insights for enhancing system accuracy and reliability. Furthermore, doubling the number of OFDM symbols was observed to achieve a 3 dB improvement in SNR, providing a straightforward method to enhance estimation and detection performance. Results are provided in lookup tables, publicly available in [16], enabling comprehensive evaluations of sensing services within the ISAC framework.  
Future work should expand this abstraction to encompass complex environments, such as Non Line of Sight (NLoS) scenarios, to enhance the robustness of ISAC systems and widen their applicability. Additionally, outdoor scenarios should be investigated to evaluate sensing services over extended distances. Furthermore, future studies should integrate additional sensing services beyond target detection and localization to create a more versatile and comprehensive abstraction framework.  
Conceptualization, A.R., S.I., D.C. and S.R.; methodology, A.R., S.I., D.C. and S.R.; software, A.R., S.I. and M.F.; validation, A.R., S.I., M.F., D.C., S.R. and J.F.M.; formal analysis, D.C., S.R. and J.F.M.; investigation, A.R., S.I. and M.F.; resources, A.R., S.I., M.F., D.C., S.R. and J.F.M.; data curation, S.I. and D.C.; writing--original draft preparation, A.R., S.I., M.F., D.C. and S.R.; writing--review and editing, A.R., D.C., S.R. and J.F.M.; visualization, J.F.M.; supervision, J.F.M.; project administration, J.F.M.; funding acquisition, J.F.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The research data has been provided in [16].  
The authors declare no conflicts of interest.  
Figure 7: Detection probability vs. SNR for different OFDM symbols.
# Research on Underwater Wireless Optical Communication Channel Model and Its Application
## 3 Underwater Optical Wireless Communication Experiment
### Experimental Results and Discussion
Similarly, for long-range communication in the pond with higher interference, the differences in the communication range are also maintained around 1 m for angles \(\theta<30^{\circ}\). However, at \(\theta=45^{\circ}\), the error reaches a magnitude of meters, and the actual effective communication space is smaller than the value predicted by the model. In cases of communication with poor water quality and shorter ranges, the experimental data tend to align more closely with the results of the model using higher sensitivity (\(-26.4\) dBm). Conversely, in scenarios with better water quality and longer ranges, the experimental data tend to align more closely with the results of the model using lower sensitivity (\(-22.4\) dBm). As a result, in practical applications, higher sensitivity coefficients may be employed to predict the effective communication space for shorter ranges, while lower sensitivity coefficients may be more suitable for modeling longer ranges. The experimental data reveal that the effective communication space measured is narrower in shape compared to the space predicted by the model. We attribute this discrepancy to the omission of the optical characteristics of the waterproof casing of the optical communication device during the modeling process. The waterproof casing has an impact on the scattering characteristics of the light source and causes light reflection phenomena at the receiver, resulting in a  
Figure 6: Communicable space under different water quality conditions. (**a**) Pond, (**b**) Guanshan Lake, (**c**) Benxi, (**d**) Qipan mountain (25 m from the shore). reduction in the received energy. Additionally, when the transmitter and receiver are misaligned with a large deviation, it poses a greater challenge to the receiver's sensitivity at different angles. The greater the angular deviation, the weaker the signal received by the receiver, resulting in a smaller effective communication space.
## 3 Graph Representation of the IoT Network
### Graph Generation Using a Formal Grammar
For a given graph \(G\), application of the \(\pi\) transformation rule is defined (like in [23]) as follows:  
* The left-hand side (_llhs_) graph is marked on \(G\) by the morphism \(\mathrm{h}\);
* The nodes belonging to the \(n\in h(V_{lhs})-h(V_{rhs})\) graph are removed from \(G\), and we denote the new graph as \(G^{\prime}\);
* The right-hand side (_rhs_) graph is added to \(G^{\prime}\), and the nodes \(m\) in \(G^{\prime}\) that belong to \(h(V_{lhs})\cap h(V_{rhs})\) are used to glue \(G^{\prime}\) and _rhs_ (note: the edges containing \(m\) were not removed from \(G\)). In the example presented here, when one of the sensors is broken, we replace it with the virtual sensor that interpolates its value based on the neighboring sensors. By looking at Figure 3, let us observe that initially, the graphs on the left-hand side (_llhs_) and right-hand side (_rhs_) appear to differ at edge nodes \(1\) and \(3\). It is important to note that the _lhs_ graph includes a node indexed as one, which is absent in the _rhs_ graph. On the other hand, the _rhs_ graph introduces a new node indexed as three, and consequently, there are new edges connecting it to the other nodes in the _rhs_ graph. Figure 3: Left and right sides of production P1. The transformation is performed through the application of two productions: P1 and P2. First, we apply the production P1, presented in Figure 3, on the part of the IoT graph presented in Figure 1 (in [] are the node's IDs in Figure 3):  
1. We designate the identity morphism h from \(lhs\) to part of G. Node 1 is represented by node 12 and, accordingly, nodes \(2\to 11\). The identity morphism preserves the structure of the mapped graphs (i.e., no nodes or edges are glued, and the mapped nodes and edges have the same labeling). 2. Node 1 in \(lhs\) does not belong to \(rhs\), and thus h(1) is removed from \(G\). Edge (1,2) is removed too. 3. Node 11, which represents the nodes belonging to \(h(V_{lhs})\cap h(V_{rhs})\), remains in \(G\) and is glued with a new part of \(rhs\) (i.e., new node 3 and edge (3,2)), which creates a final graph on \(rhs\). After the creation of a new virtual humidity sensor (VHS) node by the application of production P1, we have to designate the neighboring sensors for VHS node 22 (in [] are the node's IDs in Figure 3). We have here two possible matches for the P2 \(lhs\) (Figure 4) against the graph presented in Figure 3:  
1. \(1\to 22,2\to 11,3\to 10,4\to 13\);
2. \(1\to 22,2\to 11,3\to 16,4\to 19\). Production P2 is applied in both cases, creating edges (22,13) and (22,16), which means that the VHS can use the values of the sensors represented by nodes 13 and 16 for interpolation of their values. Finally, the IoT graph after application of this production will appear as shown in Figure 5.  
Figure 4: Left and right sides of production P2.
## 2 Methods
### Kinematic Positioning Performance Assessment Methods
\begin{split}\mathbf{V}&=\mathbf{B}\delta\mathbf{x}- \mathbf{L}\\ \mathbf{B}&=\left[\mathbf{e}_{i}^{1\mathbf{T}},\ \mathbf{e}_{i}^{2\mathbf{T}},\ \cdots\ \mathbf{e}_{i}^{n\ \mathbf{T}}\right]^{\mathbf{T}}\\ \mathbf{L}&=\left[l_{i}^{1}-L_{i}^{1}(\mathbf{x}_{0} ),\ \ l_{i}^{2}-L_{i}^{2}(\mathbf{x}_{0}),\ \cdots\ \ l_{i}^{n}-L_{i}^{n}(\mathbf{x}_{0})\right]^{\mathbf{T}}\end{split}  \tag{3}\]  
where \(\delta\mathbf{x}\) is the parameter to be estimated, \(\mathbf{B}\) is the design matrix, and \(n\) is the available station number. For user single-epoch kinematic positioning, we can evaluate the positioning accuracy \(\sigma_{\mathbf{X}}\)  
\[ \begin{split}\sigma_{\mathbf{X}}&=\sqrt{\text{tr} \left[\left(\mathbf{B}^{\mathbf{T}}\mathbf{P}\mathbf{B}\right)^{-1}\right]}\ \sigma_{0}=\sqrt{\text{tr}\left(\mathbf{N}^{-1}\right)}\ \sigma_{0}\\ P&=\sigma_{0}^{2}/\left(\sigma_{ranging\ error}^{2}+ \left(L(\mathbf{x}_{0})/1500\right)^{2}\sigma_{sound\ \ speed\ error}^{2}\right)\end{split}  \tag{4}\]  
where \(\mathbf{N}\) is the normal matrix, \(\mathbf{P}\) is the weight matrix, which considers the ranging error and the constant sound speed error, and \(\sigma_{0}^{2}\) is the variance of unit weight [26; 36]. The a posteriori variance of the unit weight is assumed to equal to the a priori variance of the unit weight in the adjustment model. The positioning accuracy \(\sigma_{\mathbf{X}}\) is related to the network geometric structure formed by the user and network stations, which can be evaluated by the position dilution of precision PDOP [26]  
\[\text{PDOP}=\sqrt{\text{tr}\left(\mathbf{N}^{-1}\right)} \tag{5}\]  
We analyzed the PDOP in the underwater space using Equations (6)-(8), where \(m\) is the number of all grid points. The maximum PDOP determines the lower bound of the positioning accuracy. Average PDOP:  
\[\text{PDOP}_{\text{ave}}=\frac{1}{m}\sum\limits_{i=1}^{m}\text{PDOP}_{i} \tag{6}\]Standard deviation of PDOP:  
\[\text{PDOP}_{\text{std}}=\sqrt{\frac{1}{m-1}{\sum_{i=1}^{m}}{\left(\text{PDOP}_{i }-\text{PDOP}_{\text{mean}}\right)^{2}}} \tag{7}\]  
Maximum PDOP:  
\[\text{PDOP}_{\text{max}}=\text{Max}(\text{PDOP}_{1},\text{PDOP}_{2},\dots \text{PDOP}_{m}) \tag{8}\]
## 2 Materials and Methods  
### Theoretical Basis  
#### 2.1.1 NMR Theory  
According to the NMR relaxation mechanism, transverse relaxation consists of three parts: bulk relaxation, surface relaxation, and diffusion relaxation [21]:  
\[\frac{1}{T_{2}}=\frac{1}{T_{2B}}+\frac{1}{T_{2S}}+\frac{1}{T_{2D}} \tag{1}\]\[T_{2D}=\frac{C_{md}(\gamma GT_{E})}{12} \tag{2}\]  
where \(T_{2}\) is the transverse relaxation time, \(T_{2B}\) is the transverse bulk relaxation time, \(T_{2S}\) is the transverse surface relaxation time, \(T_{2D}\) is the transverse diffusion relaxation time, \(C_{md}\) is the molecular diffusion coefficient, \(G\) is the gyromagnetic ratio of a proton, and \(T_{E}\) is the inter-echo spacing. When the magnetic field is uniform (\(G\) is very small) and \(T_{E}\) is short enough, the \(T_{2D}\) can be ignored. For porous media saturated with distilled water, the \(T_{2B}\) is much bigger than \(T_{2S}\), and the 1/\(T_{2B}\) is also usually negligible. Equation (1) can be simplified as follows [13; 42; 43]:  
\[\frac{1}{T_{2}}=\frac{1}{T_{2S}}\approx C_{r}\bigg{(}\frac{S}{V}\bigg{)}=F_{s} \frac{C_{r}}{r_{c}} \tag{3}\]  
where \(C_{r}\) is the relaxation coefficient which is mainly affected by the mineral composition and surface properties of clay mineral, \(S\) is the surface area of pores, \(V\) is the volume of pores, \(r_{c}\) is the diameter of pores, and \(F_{s}\) is the geometric shape factor (3 for spherical pores and 2 for columnar pores) [44].
# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
###### Abstract  
Studies have demonstrated that changes in the climate affect wind power forecasting under different weather conditions. Theoretically, accurate prediction of both wind power output and weather changes using statistics-based prediction models is difficult. In practice, traditional machine learning models can perform long-term wind power forecasting with a mean absolute percentage error (MAPE) of 10% to 17%, which does not meet the engineering requirements for our renewable energy project. Deep learning networks (DLNs) have been employed to obtain the correlations between meteorological features and power generation using a multilayer neural convolutional architecture with gradient descent algorithms to minimize estimation errors. This has wide applicability to the field of wind power forecasting. Therefore, this study aimed at the long-term (24-72-h ahead) prediction of wind power with an MAPE of less than 10% by using the Temporal Convolutional Network (TCN) algorithm of DLNs. In our experiment, we performed TCN model pretraining using historical weather data and the power generation outputs of a wind turbine from a Scada wind power plant in Turkey. The experimental results indicated an MAPE of 5.13% for 72-h wind power prediction, which is adequate within the constraints of our project. Finally, we compared the performance of four DLN-based prediction models for power forecasting, namely, the TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models. We validated that the TCN outperforms the other three models for wind power prediction in terms of data input volume, stability of error reduction, and forecast accuracy.  
c 2021  
31 October 2021  
Subject: 3 November 2021  
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# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
### Contributions  
In this study, we propose a hybrid scheme of a MAC layer and a collaborative disaster-monitoring system for disaster-monitoring applications. The key contributions of this study are as follows.  
* A hybrid superior node token ring MAC (HT-MAC) scheme is proposed, which is based on the virtual token ring of ordinary nodes, the polling of all superior nodes in one period, and alert transmissions with a low-power listening (LPL) and shortened preamble approach during the sleep state. A low-power clustering method for heterogeneous WSNs is proposed in order to increase the lifetime of disaster-monitoring systems;
* Based on embedded Markov chains, a model of the HT-MAC scheme was developed, and the mean queue length, mean cycle time, and mean upper bound of the frame delay were obtained;
* Using the HT-MAC, WirelessHART, and DRX simulation models, simulations were performed under various conditions, and the delay specifications for the three types of data were explored. The theoretical results were verified through simulations. Based on the simulation results, this HT-MAC scheme satisfies the delay and throughput requirements for the three types of data in disaster-monitoring applications, and can enhance the effective lifetime of heterogeneous networks with the proposed clustering algorithm;
* An architecture for a collaborative disaster-monitoring system is proposed, and a method for obtaining seismic data using a highly energy-efficient approach is presented.
# Advanced Progress of Optical Wireless Technologies for Power Industry: An Overview
## 2 Classification and Progress of Power Industry Optical Wireless Communications  
### Channel Characteristics between Power Line and Indoor Wireless Optical Communications  
In recent years, researchers have carried out a series of theoretical modeling and measurement activities for the channel characteristics of the power line and IWOC system in [18; 19; 20; 21; 22]. The block diagram for an integrated system is illustrated in Figure 3. In this system, the information signal is influenced by the inherent noise of PLC and IWOC channel successively. The module after pre-compensation and signal amplification, and then the data signal to the LED on the DC bias to drive LED light, which is as light transmitter coverage indoor areas. From an integration and commercial point of view, the module is very simple to install on the LED light and could be transformed into a communication device immediately. The light illuminated by the LED is received by the avalanche  
Figure 1: The application scenarios of power industry optical wireless systems: (**a**) indoor scenario; (**b**) tunnel scenario; (**c**) outdoor scenario; (**d**) hospital scenario.  
Figure 2: Block diagram of the power industry optical wireless technologies.  
photodiode (APD), and then the APD amplifies and demodulates the signal according to the received light signal level. Taking into account the structure of the entire system, IWOC is used as the downlink. Consider an infrared adapter or PLC uplink, which will be very suitable for multimedia data services [19].  
In [19], the signal is first modulated into an analog signal by the PLC modulator. The modulated signal is transmitted to the power line through the coupler. The system uses \"PLC to IWOC conversion\" module, as is shown in Figure 4, which is in the LED lamp to receive the data signal from the power cord.
## Results  
Paired t-tests resulted in significant differences between patients and matched controls with SPL (skew), \(f_{0}\) (standard deviation/ SD, 95 th percentile, middle 90% range, kurtosis), CPP (skew), H1-H2 (mean, SD, interquartile range, 95 th percentile, middle 90% range, kurtosis), and phonatory/silent segments (phonatory segments between 0.1-0.316 s and silent segments between 1000-3160 s). A step-wise logistic regression reduced these significant features to 4 final statistics: \(f_{0}\) middle 90% range, SPL skew, the cumulative duration of silent segments between 1000-3160 s, and H1-H2 kurtosis. The overall classification from this final group was 79%, and the area under the ROC curve was 0.88 (95% confidence interval = 0.82-0.95).
## 2 Materials and Methods
### Proposed Feature Selection Method
IG is widely used to find feature selection and the decision tree classifier [37]. In the proposed algorithm, IG was used to find important feature sets. In general, if the IG of any feature is higher than 0.05, then the feature is considered an important feature [38]. Therefore, the IG value of 0.05 was used to find the fitted features spectra set \(F_{r}\). This way, the final important spectra were selected from \(F_{bot}\). These potentially important spectra were made with an important spectrum set and considered as important spectra as \(F_{n}\) = \(F_{top}\) + \(F_{r}\). The above process iteratively selects a predefined number of significant spectral features. In online applications, the spectra set should be small, with optimal speed and accuracy. However, quality control applications need high accuracy with optimal speed and computational cost. Therefore, in the in-line application, the \(F_{\ell}\) spectra set was selected and for the quality control application, the \(F_{T\ell}\) spectra set was selected (\(F_{T\ell}\) > \(F_{f\ell}\)). To select the \(F_{T\ell}\) spectra set for quality control application, the iterative process continued until the \(T_{\ell}\) spectral band. However, to select \(F_{f\ell}\) spectra, \(F_{T\ell}\) top-ranked spectra were selected as mentioned above, and then an exhaustive search algorithm was performed. The exhaustive search algorithm finds all possible combinations of\(f_{x}\) spectrum features among \(T_{\varepsilon}\) spectra. The SVM classification algorithm was used as a performance evaluation model and the top performing spectra set \(F_{f\ell}\) was selected as the most important spectra for in-line aflatoxin B1 detection.