esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 3 Fuzzy Approaches for Energy Performance of Buildings Forecasting
### Fuzzy Models Identification
#### 3.3.1 ANFIS Models  
In order to obtain ANFIS models it is necessary to define two sets of parameters: one related to the discretization process of the input variables, which has been explained before, and the parameters related to the training process. The parameters needed to perform the training process are: the type of the output function (i.e., constant or linear), the optimization method to train the fuzzy inference system, and the number of training epochs.  
ANFIS uses the eight input variables to predict each output, HL and CL, and does not perform any kind of feature selection. In this experiment we use a constant output function, two different numbers of training epochs, and a hybrid optimization method (default option). A constant output has been chosen instead of a linear one because the number of parameters that ANFIS needs to optimize when a linear output function is used causes Matlab to run out of memory. Notice there are 8 input variables, 4 discretized into 3 classes and 4 into 2 classes; therefore, each ANFIS model is composed of a rule set of \(3^{4}\times 2^{4}=1296\) rules. If the output of each rule is a constant output function then the number of output parameters to be optimized is 1296. However, if each rule has as output a first-order linear function the number of output parameters to optimize becomes higher: 11,664 (= \(1296\times 9\)). Two different values, i.e., 50 and 300, are studied to set the number of epochs. The time needed to run a 10-fold CV using an Intel Core2 Duo (3 GHz, 4 GB of RAM) during 50 epochs is more than 7 h and during 300 epochs is more than 41 h. However, the errors obtained in both cases are virtually identical, for instance, in the case of the HL the RMSE errors obtained are 0.5205 (50 epochs) versus 0.5203 (300 epochs). Therefore, in this case, it does not make sense to increase more than 50 the number of epochs. With respect to the optimization method, the hybrid approach, i.e., a combination of backpropagation and least square estimation (see Section 2.2), has been chosen because it is the original one designed to optimize ANFIS models, and from our point of view is better suited for this task.
## 4 Discussion
The initiation of real-time tracking after receiving a birth alert may improve accuracy, applications, and benefit future research in this area. It is recommended that real-time tracking of cows after birth should record a geolocation every 15-30 min to maximize the likelihood of finding the travelling cow and calf. Therefore, improvements to the VIT signal strength could help with VIT retrieval and confirming VIT expulsion site, but this could be restricted by the feasible size and weight of the VIT. Both birth alert systems had advantages and limitations in relation to the ease of use and ability to track calves at birth. An advantage of both systems was the provision of a geolocation for the birth site, which made initial VHF tracking more efficient by providing a starting point. For Brand 2, the calving location was only accessible via their software, which slowed the process, whereas Brand 1 provided a geolocation in the email and GSM message. However, the geolocation provided by both systems was for the GNSS collar and not the VIT; therefore, the VIT had to be tracked with the handheld VHF antenna. Nevertheless, VHF was not accurate nor practical to find the cows or the VIT and therefore alternative technologies are required. The accuracy of both birth alert systems trialed was low and therefore, these are not suitable for use in extensive systems or animals free roaming in large areas. The main reasons for this poor performance were: early expulsion and lack of suitability for long-term deployments (\(\geq\)3 months), time delay between VIT expelling and receiving the alert message, low range of communication of the VIT with the collar using UHF, difficult localization of the cows after calving using the VHF tracker, the size of the paddock and difficult terrain, the behavior of cows at calving time, keeping on the move after calving and abandoning the calving site quickly. In addition, cows calving within the seven-day calibration period after VIT insertion for Brand 1 also resulted in missed birth alerts. These issues must be addressed before the technology becomes a viable solution for research and perhaps commercial applications in extensive cattle operations. Potential solutions may range from changing the thresholds for the birth alert criteria for temperature and activity to improving the strength of the VHF and UHF communications of the VIT, improving the communication protocol to send the alerts to the cloud, or initiating real time GPS tracking once a birth alert was set off. Initiation of real time tracking at birth would provide regular ongoing geolocation updates on calving animals to enable more efficient access to newborn calves, although this would increase satellite data costs.
# mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm
###### Abstract  
Smart Cities are called to change the daily life of human beings. This concept permits improving the efficiency of our cities in several areas such as the use of water, energy consumption, waste treatment, and mobility both for people as well as vehicles throughout the city. This represents an interconnected scenario in which thousands of embedded devices need to work in a collaborative way both for sensing and modifying the environment properly. Under this scenario, the majority of devices will use wireless protocols for communicating among them, representing a challenge for optimizing the use of the electromagnetic spectrum. When the density of deployed nodes increases, the competition for using the physical medium becomes harder and, in consequence, traffic collisions will be higher, affecting data-rates in the communication process. This work presents _mDARAL_, a multi-radio routing algorithm based on the Dynamic and Adaptive Radio Algorithm (_DARAL_), which has the capability of isolating groups of nodes into sub-networks. The nodes of each sub-network will communicate among them using a dedicated radio frequency, thus isolating the use of the radio channel to a reduced number of nodes. Each sub-network will have a master node with two physical radios, one for communicating with its neighbours and the other for being the contact point among its group and other sub-networks. The communication among sub-networks is done through master nodes in a dedicated radio frequency. The algorithm works to maximize the overall performance of the network through the distribution of the traffic messages into unoccupied frequencies. The obtained results show that _mDARAL_ achieves great improvement in terms of the number of control messages necessary to connect a node to the network, convergence time and energy consumption during the connection phase compared to _DARAL_.  
multi-radio; network routing algorithm; WSN; DARAL; smart city; IEEE 802.15.4
## 4 AI-Enabled Intelligent 6G Network Applications
### Remote Surgery  
Sixth-generation networks will enable remote surgery, allowing doctors to operate on patients while they are apart. This might be very beneficial in situations when it is not practical for the patient and medical professional to be in the same location, such as in remote or underserved areas. With the aid of 6G networks, surgeons will be able to operate robotic surgical equipment and view the surgery site in real time [29; 30].  
Future smart healthcare will feature freely accessible, locally independent, and unrestricted healthcare services. Dynamic personal health monitoring, tele-diagnosis and pathological inference, holographic medicine, patient recovery training, and telesurgery are just a few of the unique use cases that will be made possible by the introduction of new sensors and AI capabilities in the 6G mobile network [25; 26].
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation
### Estimation Accuracy: Cramer-Rao Lower Bound  
Estimation theory provides performance metrics for techniques by comparing their covariance estimation error against a theoretical lower bound. The Cramer-Rao lower bound (CRLB) is a generally employed bound for the estimation of covariance matrix error [16], [31].  
For the scenarios considered in this paper, the error covariance of unbiased estimate \(\hat{\Omega}\), i.e., \(E\left\{\hat{\mathbf{\Lambda}}\right\}=\mathbf{\Omega}\), is bound by  
\[E\left\{\left(\Omega-\hat{\Omega}\right)\left(\Omega-\hat{\Omega}\right)^{T} \right\}=\left[-E\left\{\frac{\partial^{2}\log\left(p\left(\widetilde{A}_{N}/ \Omega\right)\right)}{\partial\Omega\partial\Omega^{T}}\right\}\right]^{-1} \tag{57}\]  
For compactness purposes, \(\mathbf{\Omega}\) and \(k\) are excluded from the notation in the following. The above provides the stochastic CRLB by substituting the SML probability density function into Equation (44):  
\[\left\{CRLB_{sto}\right\}_{ij}=\frac{\widetilde{\sigma}_{a}^{2}}{2}\left[ \Re\left\{Tr\left\{\mathbf{Y}_{j}P\widetilde{\mathbf{Y}}_{i}^{H}\widetilde{ R}_{ss}\mathbf{Y}\widetilde{\mathbf{R}}_{aa}{}^{-1}\mathbf{Y}^{H}\widetilde{R}_{ss} \right\}\right\}\right]^{-1} \tag{58}\]  
where \(\mathbf{Y}_{i}\) is the partial derivative of \(\mathbf{Y}\) with respect to the \(i\)th element in \(\mathbf{\Omega}\) and defined as  
\[\mathbf{Y}_{i}=\frac{\partial\mathbf{Y}}{\partial\mathbf{\Omega}_{i}} \tag{59}\]  
\(\mathbf{Y}_{i}\) elements are the SH partial derivatives with respect to elevation angle \(\theta\) and azimuth angle \(\phi\) and expressed as  
\[\begin{array}{ll}\frac{\partial Y_{n}^{m}(\Omega)}{\partial\theta}=&-\sqrt{ \frac{(2n+1)(n-m)!}{4\pi(n+m)!}}e^{-im\phi}\csc(\theta)\\ &\times\left[(n+1)\cos(\theta)P_{n}^{m}(\cos(\theta))+(m-n-1)P_{n+1}^{m}(\cos (\theta))\right]\end{array} \tag{60}\]  
and  
\[\frac{\partial Y_{n}^{m}(\Omega)}{\partial\phi}=imY_{n}^{m}(\Omega), \tag{61}\]  
respectively, and \(\csc(\cdot)\) denotes the cosecant trigonometric function. All of the partial derivatives are presented in the literature (e.g., MATHEMATICA). The CRLB for a single stochastic source in the spherical harmonic domain is given in [35]. The DML probability density function in Equation (52) can be substituted into Equation (55) to provide the deterministic CRLB [25]:  
\[\left\{CRLB_{det}\right\}_{ij}=\frac{\widetilde{\sigma}_{a}^{2}}{2}\left[\Re \left\{Tr\left\{\mathbf{Y}_{j}P\widetilde{\mathbf{Y}}_{i}^{H}\widetilde{R}_{ ss}\right\}\right\}\right]^{-1} \tag{62}\]
## 2 Maximum Bubble Pressure Method
### Introduction of Measurement Correction  
A closer analysis of the problem shows that the above assumption (for \(P_{max}\), the bubble has a hemispheric shape with radius \(r\)) introduces a specific error of method, which is the more significant as the value of the quotient \(\rho/\sigma\) becomes higher. The more critical its value, the more flattened the hemisphere is, taking on a shape like a rotational ellipsoid [14; 15]. Under these conditions, the maximum pressure occurs in a bubble with a larger volume than that of a hemisphere with radius \(r\). Mathematical analysis of the dependence of the bubble geometry on the value of this quotient allowed the formula for the correction of the surface tension value to be derived as follows (10):  
\[\sigma_{k}=\frac{r\Delta P_{max}}{2}[1-\frac{2}{3}gr^{2}w] \tag{10}\]  
where:  
* \(w=\rho/\sigma\)--The value of surface tension determined from (9).
* \(\sigma_{k}\)--The adjusted value.  
[PERSON] formulated a modified version of Equation (9), which was used for the studied systems (11) [11; 15].  
\[\sigma_{k}=\frac{r\Delta P_{max}}{2}[1-\frac{2}{3}gr^{2}w-\frac{(r^{2}gw)^{2} }{12}]. \tag{11}\]  
The diagram below (Figure 3) shows examples of the values of the correction factor \(\epsilon=\sigma/\sigma_{k}\) as a function of the quotient \(\rho/\sigma\) calculated from the relationships in (9) (the red waveform in Figure 3) and (10) (the blue waveform in Figure 3) for a capillary with a radius of \(r=1\) [mm]. As can be seen, for the value of \(\rho/\sigma<2\cdot 10^{4}\) there is complete agreement of the correction calculated using Formulas (9) and (10), while significant differences appear for \(\rho/\sigma>3\cdot 10^{4}\). It should be emphasised that the smaller the mentioned error of the method, the smaller the radius of the capillary \(r\).
## 4 Results and Discussion
### Influence of Different Parameters on the Support Structure
In summary, it can be seen that the shallow borehole diameter has the most obvious effect on the deformation amount of the roadway and anchor support, to ensure the strength of the support structure, the optimal shallow borehole diameter is 120 mm; the deep borehole diameter has the most obvious effect on the stress transfer of the roadway surrounding rock, to improve the overall pressure relief effect, the optimal deep borehole diameter of 200 mm\(\sim\)300 mm; the position of the variable borehole diameter has the most obvious effect on the original stress peak, it can be seen that the stress peak can be controlled by the variable diameter of the borehole, to improve the overall pressure relief effect. To control the peak stress at the bottom of the variable diameter borehole, the optimal variable diameter borehole is located at 4 m\(\sim\)6 m from the roadway side. The spacing of the borehole has a significant effect on the transfer of stress to the surrounding rock, the deformation of the roadway, and the impact of the anchor support. To achieve reasonable control of the support structure strength and the deformation of the surrounding rock, the optimal variable diameter borehole spacing is in the range of 1.0 m\(\sim\)2.0 m.
# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 4 Discussion
The best CCI, MCC values, and F1 scores (%) among Segments III, IV, and V for the cross-comparisons of VAD space are given in Table 3 for the unbalanced data and in Table 4 for the balanced data. It can be easily seen that Segment V gave mostly the best percentages for all CCI, MCC values, and F1 scores among Segments III, IV, and V. Besides these results, the RSS classifier gave the best percentages by a considerable amount with the unbalanced data, while the OF classifier gave similar results with the balanced data among the mentioned segments. The best accuracies of the cross-comparisons for each class separation level can be easily seen in both tables. The SMOTE method considerably increases the percentages of the performance metrics, which can be seen in Table 4. As the class separations move away \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sep. Level**} & \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**CCI (\%)**} & \multicolumn{4}{c}{**F1 Score (\%)**} & \multicolumn{4}{c}{**MCC (\%)**} \\ \cline{3-11}  & & **Value** & **Classifier \({}^{*}\)** & **Segment** & **Value** & **Classifier** & **Segment** & **Value** & **Classifier** & **Segment** \\ \hline \multirow{4}{*}{(5)} & A\&G & 88.62 & RF & V & 88.6 & RF & IV, V & 77.4 & RF & V \\  & B\&H & 82.60 & OF & V & 82.6 & OF & V & 65.2 & OF & V \\  & D\&F & 89.94 & OF & V & 89.9 & RF, OF & V & 80.2 & OF & V \\  & C\&E & 90.31 & OF & V & 90.3 & OF & V & 80.7 & OF & V \\ \hline \multirow{4}{*}{(4-6)} & A\&G & 91.45 & RF & V & 91.4 & RF & V & 83.1 & RF & V \\  & B\&H & 93.37 & OF & V & 93.4 & OF & V & 90.8 & FURIA & III \\  & D\&F & 94.76 & IBk, RC, SMO & III, IV, V & 94.8 & IBk, RC, SMO & III, IV, V & 90.0 & IBk & IV \\  & C\&E & 97.92 & RC & III & 97.9 & RC & III & 95.9 & RC & III \\ \hline \multirow{4}{*}{(3–7)} & A\&G & 95.58 & SMO & IV & 95.6 & SMO & IV & 91.4 & SMO & IV \\  & B\&H & 100.00 & RF, OF & III & 100.0 & RF, OF & III & 100.0 & RF, OF & III \\  & D\&F & 95.05 & SMO & V & 95.0 & SMO & V & 90.3 & SMO & V \\ \hline \multirow{4}{*}{(4.5–5.5)} & A\&G & 97.92 & IBk & III & 97.9 & IBk & III & 95.9 & IBk & III \\ \hline \hline \end{tabular}  
* FURA: Fuzzy Unordered Rule Induction Algorithm; IBk: Instance-Based Learning (it is a K-Nearest Neighbor classifier); OF: Optimized Forest; RC: Random Committee; RF: Random Forest; RSS: Random Subspace; SMO: Sequential Minimum Optimization.  
\end{table} 
Table 4: Best CCI, MCC values, and F1 scores (%) among Segments III, IV, and V for the cross-comparisons of VAD space (balanced data with SMOTE).
## 2 Data, Methods and Techniques
### Experiments  
Tap water, produced by the Cracow Waterworks, with natural organic substances (at a temperature of 293 K) was dosed into the filter at the rates of 0.8, 1.0, 1.5 and 2.2 mL/s; water was stored in the 200 dm\({}^{3}\) tank. The organic content in water samples was measured as the absorbance \(A\) at a wavelength of 254 nm in a 5 cm long quartz cuvette using a SPECORD 210 PLUS spectrophotometer, Analytik Jena, Jena, Germany.  
Ultraviolet absorbance is proportional to the concentration of organic substances \(C\) in water [14; 15; 16; 17]. Since the concentration \(C\) can be linked to absorbance \(A\), the units g/m\({}^{3}\) can be replaced with 1/m in the authors' mathematical models. Depending on the units adopted, model parameters and changes in concentration \(C\) or absorbance \(A\) over time may have different dimensions. The transition from one dimension to another requires substituting g/m\({}^{3}\) for 1/m, or vice versa. UV\({}_{254}\) absorbance characterizes the content of natural matter containing mainly aromatic groups [30]. These are substances that are precursors of trihalomethanes (THM). Removing these substances from water is important in limiting the formation of THMs that are hazardous to health in the water purification process.  
The PAC doses into the anthracite layer were as follows: 1, 2, 3 and 4 g. The apparent PAC density per unit volume of the anthracite layer changed following the current depth of the anthracite layer; also, the depth changed slightly due to the amount of dosed sorbent and arrangement of anthracite particles.  
Once the adsorption experiments were completed, a tracer (1 M NaCl) was pulsed into the filter column. The average longitudinal dispersion coefficient for the entire column was calculated from a specific conductivity \(S\) in the filter effluent. At low electrolyte concentrations, conductivity is proportional to electrolyte concentrations.  
Finally, the filter bed was backwashed (fluidized) to remove the sorbent as well as other undesirable substances and the unit was ready for the next experiment.
## 3 Results and Discussion
### Performance Evaluation Measures  
The measures used for evaluating the proposed model are as follows: (i) Precision (P) is the ratio of the number of correctly identified root-rot-diseased plants to the total number of identified root-rot-diseased plants. It is given in Equation (8). (ii) Recall (R) is the ratio of the number of correctly identified root-rot-diseased plants to the total number of root-rot-diseased plants. It is given in Equation (9). (iii) The \(F_{1}\) score is the weighted average of P and R. It is given in Equation (10). (iv) Accuracy (Acc) is the proportion of correctly identified plants and is given by Equation (11).  
\[Precision(P)=\frac{TP}{TP+FP} \tag{8}\]  
\[Recall(R)=\frac{TP}{TP+FN} \tag{9}\]  
\[F_{1}\,score=\frac{2\times P\times R}{P+R} \tag{10}\]  
\[Accuracy=\frac{TP+FN}{TP+FN+FP+TN} \tag{11}\]  
Here, \(TP\) is True Positive, which denotes the number of root-rot-diseased plants correctly identified; \(FP\) is False Positive, which denotes the number of plants falsely identified as root rot diseased; \(FN\) is False Negative, which denotes the number of root-rot-diseased plants that were not identified as such; and \(TN\) denotes the number of healthy plants correctly identified.
## 3 Establishment and Simulation of the Error Model
### Matrix Description of Relative Motion
\begin{array}{cccc}0&1&0&-P_{y}^{4}\\ 0&0&1&-P_{z}^{4}\end{array} \right]\left[ \begin{array}{cccc}x^{4}\\ y^{4}\\ z^{4}\\ 1\end{array} \right]=\left[ \begin{array}{cccc}0\\ 0\end{array} \right] \tag{5}\]  
The original point Q of CS5 is at the center of the front surface of autocollimator AC2, and the Q point in CS0 is \(\left( \begin{array}{cccc}Q_{x}&Q_{y}&Q_{z}&1\end{array} \right)^{T}\). The angle installation errors between CS5 and CS0 are expressed as \(\varepsilon_{xm}\), \(\varepsilon_{ym}\), and \(\varepsilon_{zm}\). The position of the exit light \(Q^{5}\) is \(\left( \begin{array}{cccc}Q_{x}^{5}&0&Q_{z}^{5}&1\end{array} \right)^{T}\), and the light vector is \(\left[ \begin{array}{cccc}0&y&0&0\end{array} \right]^{T}\). Similarly, the position coordinate of the original point K of CS6 in CS0 is \(\left( \begin{array}{cccc}K_{x}&K_{y}&K_{z}&1\end{array} \right)^{T}\), and the angular installation errors relative to CS0 are expressed as \(\varepsilon_{xm}\), \(\varepsilon_{ym}\), and \(\varepsilon_{zm}\). The position of the exit light \(K^{6}\) is \(\left( \begin{array}{cccc}0&K_{y}^{6}&K_{z}^{6}&1\end{array} \right)^{T}\), and the light vector is \(\left[ \begin{array}{cccc}x&0&0&0\end{array} \right]^{T}\). The position coordinate of the original point U of CS7 is \(\left( \begin{array}{cccc}U_{x}&U_{y}&U_{z}&1\end{array} \right)^{T}\), and the angular installation errors are expressed as \(\varepsilon_{xm}\), \(\varepsilon_{ym}\), and \(\varepsilon_{zm}\). The position of the exit light \(U^{7}\) of the light is \(\left( \begin{array}{cccc}U_{x}^{7}&0&U_{z}^{7}&1\end{array} \right)^{T}\), and the light vector is \(\left[ \begin{array}{cccc}0&y&0&0\end{array} \right]^{T}\). The position coordinate of the original point V of CS8 is \(\left( \begin{array}{cccc}\text{V}_{x}&\text{V}_{y}&\text{V}_{z}&1\end{array} \right)^{T}\), and the angular installation errors are expressed as \(\varepsilon_{xv}\), \(\varepsilon_{yv}\), and \(\varepsilon_{zv}\). The position of the exit light \(V^{8}\) of the light is \(\left( \begin{array}{ccccc}&V^{8}_{x}&V^{8}_{y}&0&1\\ \end{array} \right)^{T}\), and the light vector is \(\left[ \begin{array}{ccccc}&0&0&z&0\\ \end{array} \right]^{T}\). According to the above definition, the corresponding equations of light rays \(L^{5}\), \(L^{6}\), \(L^{7}\), and \(L^{8}\) and coordinate transformation matrix \(T^{5}_{0}\), \(T^{5}_{0}\), \(T^{7}_{0}\), and \(T^{8}_{0}\) can be obtained, respectively. The above homogeneous coordinate transformation matrix describes the relative motion relationship between each component, which not only includes the six DOF motion errors of the target rotary axis, but also includes the installation errors of each component. Through ray tracing, the six DOF geometric motion errors of the rotary axis can be obtained by using the spatial coordinate change of the returned ray.
# Towards a Hybrid Security Framework for Phishing Awareness Education and Defense
## 12 Architecture Performance Comparisons  
Six different neural network architectural models were developed and compared: Feedforward Neural Network (FNN), Bi-directional RNN (Bi-RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). These models were chosen based on previous studies, which have shown that these models have demonstrated promising performance in a variety of phishing detection-related applications [22; 23; 24; 25; 26].  
The training and testing workflow for the models are as follows:  
1. Preprocessed dataset discussed in Sections 8 and 9 is used;
2. Dataset is split into a training set and an evaluation set at an appropriate ratio;
3. Training dataset is used to develop the six deep-learning models;
4. Evaluation dataset is used to test the efficiency of the models;
5. Matrices specified in Section 11 are used for the evaluation of the models;
6. Execution time is measured for each model to evaluate the performance speed.  
For instance, Figure 15 is the code representation for FNN implementation. Performance comparisons among the different deep learning architectures are illustrated in Table 4.