esa-sceva/satcom-chunk-collection huggingface dataset:
# Things2 People Interaction toward Energy Savings in Shared Spaces Using BIM
## 4 Personalized Context Data Prediction
### Modelling
#### 4.2.3 Long Short-Term Memory Neural Network (LSTM)  
The long short-term memory neural network (LSTM) is a type of recurrent neural network. This neural network is differentiated from the deep neural networks in terms of its usage, in that a deep neural network generates predictions considering its current input and discriminating previous input. For time-series data, this type of neural network would eliminate the dependencies between inputs that are observed for time series problems.  
The application of recurrent neural networks helps mitigate this loss on dependency problem by memorizing the valuable input throughout sequences. Although being able to hold information through the various sequences, it is still unable to hold that same information for extended sequences since the traditional recurrent neural networks utilise backpropagation in model training.  
The LSTM was developed considering this loss, allowing the information to be held in longer sequences. This is achieved by using the logic of maintaining only information that is important in a sequence and forgetting the irrelevant information through internal mechanisms called gates.  
The LSTM model can be explained by Equation (5)--LSTM Model Equation.  
\[\begin{split} i_{t}=\ \sigma(W_{xi}x_{[t]}+b_{xi}+W_{hi}h_{[t-1]}+b_{hi })\\ f_{t}=\ \sigma(W_{xf}x_{[t]}+b_{xf}+W_{hf}h_{[t-1]}+b_{hf})\\ g_{t}=\ tanh(W_{xg}x_{[t]}+b_{xg}+W_{hg}h_{[t-1]}+b_{hg})\\ o_{t}=\ (W_{x0}x_{[t]}+b_{x0}+W_{ho}h_{[t-1]}+b_{ho})\\ C_{[t]}=\ f_{t}\odot c_{[t-1]}+i_{t}\odot g_{t}\\ h_{[t]}=\ o_{t}\odot tanh(c_{[t-1]})\end{split} \tag{5}\]  
where \(i\) indicates the input, \(f\) the forgotten information, \(g\) the update step, \(o\) the output, \(c\) the cell memory state, and \(h\) the hidden state, [34].
## 3 Theory Analysis
### Kurtosis Criterion  
Kurtosis is a measure of the steepness or sharpness of the data distribution, as shown in (18). Since the WGN and narrowband interference signal intervals obey the normal distribution, and the PD signal intervals obviously deviate from the normal distribution, the kurtosis value of each IMF obtained by the decomposition of MVMD is suitable for the diagnosis of PD defect signals.  
In order to study the application of the kurtosis value in accurately distinguishing PD feature information and noise interference components in noisy PD signals, paper [14] calculated the kurtosis mean of sinusoidal and double exponential decay signals under different signal-to-noise ratios (the number of calculations under each signal-to-noise ratio is 30 times). As a result, the kurtosis mean of sinusoidal signal at 0 dB is about 2.90, while the kurtosis mean of double exponential decay signal y2 at 0 dB is about 3.20. In order to filter out as much WGN as possible, in this paper, an IMF\({}_{n}\) component with a kurtosis value greater than 3.2 is defined as the PD dominant component, and an IMF\({}_{n}\) component with a kurtosis value less than or equal to 3.2 is defined as the noise dominant component.  
\[k_{n}=\frac{\sum\limits_{n=1}^{N}\left[\text{IMF}_{n}-\mu\right]^{4}}{(N-1) \sigma^{4}} \tag{19}\]  
In (19), \(\mu\) represents the mean of IMF\({}_{n}\), \(\sigma\) represents the standard deviation of IMF\({}_{n}\) and \(N\) is the number of sampling points.  
Figure 4: AEE of different SNRs.
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 2 Wildfire Management Needs Assessment
### Summary of Wildfire Manager Needs
#### 2.2.3 Wildfire Behavior and Threat Estimates  
The proximity and threat to interface zones was identified as critical intelligence in the survey (Table 2). Proximity to these zones is achievable through accurate detection and mapping in conjunction with national interface maps [54].  
Wildfire behavior observations were considered to have both tactical and strategic value, particularly in terms of estimating the potential threat of a wildfire. Information relating to the rate and direction of wildfire spread (ROS (m s\({}^{-1}\)) and DIR (deg); [56; 57]) as well as the Fire Intensity (FI, (kW m\({}^{-1}\))), are essential to characterizing the behavior of actively spreading wildfires. [PERSON] et al. (2017) [58] demonstrated that FI can be estimated directly from IR measurements of FRP. Wildfire behavior is of particular interest during the late afternoon \"peak burn\" period (Figure 1). This information should ideally be paired with the spatial context (e.g., adjacent fuels, topography, and proximity to areas of concern; [59]) to provide estimates of proximity and threat to these areas.
## 1 Introduction
In addition, the authors ascribed the grain refining to dynamic recrystallization. [PERSON] et al. [28] applied the AFSD process to deposit AA2011-O and AA2011-T6 aluminum alloys on an AA5083 substrate alloy. They found that the behavior of the materials during deposition was affected by the temper conditions of the starting rod materials. To achieve a suitable deposition state with the \"T6\" temper condition (hard rod material), a high rotation speed of 1200 rpm and deposition rates of 3, 6, and 9 mm/min were necessary. In contrast, in the case of the \"O\" temper condition (soft rod material), the alloy required a low rotation speed of 200 rpm and deposition rates of 1, 2 and 3 mm/min to achieve the deposition process. [PERSON] et al. [38] succeeded indepositing AA5083/TiO\({}_{2}\) composite material in discontinues multilayers (layer by layer) under the processing conditions of a 20 mm diameter consumable rod rotation speed of 800 rpm, a downward force of 7 kN, and a step time of 25 s for each layer. Joining of the AA7075 and AA2024 aluminum alloys is widely utilized in the automotive and aerospace industries to manufacture structural components. The AA2024 is applied as a large sheet for the body structures as a skin, while, because of its greater strength, the AA7075 alloy is employed as a stringer in a T-joint arrangement, which prevents the skin from buckling. Some studies [4; 39; 40] have reported that the friction stir welding process could be used to join these alloys in T-joints at different welding parameters, concluding that the main governing factor for joint efficiency and metallurgical properties is the introduced heat input. Based on these concepts, AFSD as a solid-state process can be applied in the industry to achieve such goals at a certain scale. Informed by published data, the innovative contribution of the current study comes from the fact that this is the first attempt to quantify the integral process-microstructure-property relationships of as-deposited AA7075-T6 on an AA2024-T4 substrate using the AFSD process. Thus, the aims of the current work are to explore the possibility of applying AFSD to build continuously multilayered AA7075-T6 parts on a substrate of AA2024 and to optimize the process parameters affecting the heat input. To achieve these aims, AFSD was employed at a wide range of rod rotating speeds (200 to 1200 rpm) and (1, 2, 3, 4, and 5 mm/min) deposition feeding rates. The downward force and the thermal cycles to achieve the deposited parts were recorded. The materials produced were evaluated visually and at the micro-scale. Cross-sections of the deposited materials were prepared for microstructure investigation and hardness measurement. A compression test was also applied to the AFSD products to determine their load-carrying capacity. Finally, the XRD technique was used to examine the presence phases before and after the deposition of the AA7075-T6 rod alloy.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 3 Results
### Results of Study Area 1
#### 3.2.2.  
Analyzation of Features  
All the features discussed in Section 2.1.2 compose a vector representing each superpixel. LWR was calculated for all superpixels, and the histogram result is shown in Figure 6. Figure 6 shows obvious double peaks, and that closer to 0 belongs to the water body.  
For the redundancy analysis of features, in this part, their correlations were calculated, as shown in Figure 7a. The texture value is generally independent of other features. The LWR value is related to the mid-value in the HV mode and the mean and standard deviation values in the HH mode separately; furthermore, the mid of the HV mode is related to the standard deviation and mean in the HH mode. Generally, not every polarization mode shows the same trend, and the correlation between different polarization modes is not strong, which shows that different polarization modes are of different importance to water extractors; the LWR is independent of the texture value and scattering matrix and shows a certain correlation with statistical characteristics, as it is calculated from those features. On the other hand, we already know the strong correlation between LWR and water bodies, so the features with a strong correlation with LWR should also be conducive to water extraction; in other words, HH is more conducive to water extraction than HV polarization.  
Figure 4: False color composite SAR images of two study areas. **(a1)** Study area 1, Sentinel-1 acquired on 6 July 2020; **(a2)** study area 1, Sentinel-1 acquired on 18 July 2020; **(b)** study area 2, Gaofen-3.  
Figure 5: Over-segmentation results of study area 1. (**a**) General view; (**b–d**) are enlarged views of A, B, and C in (**a**), respectively.  
Figure 6: Histogram of LWR results.  
To analyze whether the design features are useful, the importance of the features is evaluated by the tree model, as shown in Figure 7b. According to the results, the most important features are texture information, then the LWR features and the scatter matrix. In terms of the statistical features, the most useful are the standard deviation, min, and mean in the HH mode, which further shows that the HH polarization mode is more suitable for water extraction.  
In general, the above analysis shows that the features are effective, and the importance of texture and the HH polarization mode is demonstrated. Considering that a neural network can resist the risk caused by feature redundancy, different models have different weights for different features; all the above are selected as the features of nodes.
# Fault-Tolerant Model Predictive Control Applied to a Sewer Network
## 5 Results and Discussion
### Fault Detection and Diagnosis Tests  
As exposed in Section 4.3, the faults under study will be:  
* Faults in level sensors: sensor gain is reduced to 10% of its nominal value.  
Figure 10: Tank levels generated by the neural network (scenario 3).  
Figure 9: Tank levels (scenario 3).  
Faults in actuators: gate is blocked at 20% of its total opening.  
These faults will be provoked on the second, fifth or eighth day of a 10-day simulation interval, considering both scenarios 2 and 3.  
Previously, it has been verified that in the absence of faults, the detection system does not detect any type of fault, although it presents an alarm rate for scenarios 2 and 3 of 9.57% and 11.13%, respectively, but since 20 consecutive alarms are necessary to consider a fault situation, none are detected.  
The following graphics show the calculated \(Q\) threshold and the \(Q\) statistic in the absence of faults for scenarios 2 and 3:  
Both Figures 11 and 12 show that the \(Q\) statistic sometimes exceeds the calculated threshold, but no fault is detected because 20 consecutive alarms are necessary.  
The results obtained in different fault situations are shown in Tables 3 and 4 for scenarios 2 and 3. The tables show the detection results (detection instant) and the diagnosis  
Figure 11: \(Q\) statistic and threshold calculated in a normal operating situation (scenario 2).  
Figure 12: \(Q\) statistic and threshold calculated in a normal operating situation (scenario 3).  
for the type of fault considered (fault variable: \(h_{i}\), is the tank \(i\) level; \(u_{i}\) is the tank \(i\) output flow rate). The correct diagnosis is highlighted in green:  
In terms of fault detection, both scenarios show that all faults are detected relatively quickly (almost all before the next day). It must be considered, as already mentioned, that these are large faults. In the tests performed with less significant faults, detection was considerably delayed with respect to the time of fault generation, and there were some cases in which the fault was not detected at all.  
Regarding fault classification, success is variable depending on the proximity of the disturbances to the moment of generation of the fault, as well as their magnitude and their frequency. As these characteristics are highly variable, success in detecting the fault is also variable. Further investigations must be improved by fault classification.
## 6 Performance and Discussion
### Experimental Result  
The classification performance of our proposed method was tested based on three parameters: accuracy, specificity, and sensitivity. Figure 10 displays the comparative classification performance results using the ADNI dataset trained model on the left part and the same parameter with the GARD trained dataset on the right part. Accuracy for ADNI trained dataset drops from 97% to 80% in the GARD test set in the case of ROI_CL based features, whereas the accuracy drop is not so severe in ROI_AAL based features. Similarly, accuracy from the GARD-trained dataset drops from 96% to 91% in the ADNI test set using ROI_CL features and from 97% to 88% in ROI_AAL features. Here all the results are from ADNI-trained BR-NN models; the first four results are for 30% test sets of ADNI, whereas the last four are for 100% of the GARD dataset used as the test set. To test the validation of the proposed method, some ablation experiments were also performed to conclude the need for smoothing and the importance of all features. Besides, this experiment compares the efficiency via time consumption of each experiment. The results of all performed experiments are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Region from ROI\_CL** & **\# of Voxels** & **Mean T** & **\# of Cluster** &
\begin{tabular}{c} **Selected Region for ROI** \\ _AAL (Alphabetically) \\ \end{tabular} \\ \hline  & **Thalamus\_R** & 124 & 4.0965 & 1 & \\ \cline{2-5}  & **Hippocampus\_R** & 79 & 4.7669 & 1 & \\ \cline{2-5} WM based Region (AD vs. CN) & **Thalamus\_L** & 75 & 3.8945 & 1 & \\ \cline{2-5} (AD vs. CN) & **Pallidum\_R** & 27 & 4.1347 & 1 & \\ \cline{2-5}  & **Caudate\_R** & 26 & 3.6705 & 4 & \\ \cline{2-5}  & **Putamen\_R** & 24 & 3.7313 & 4 & \\ \hline  & **Cingulum\_Mid\_R** & 64 & 2.7408 & 1 & \\ \cline{2-5}  & **Insua\_L** & 75 & 2.7419 & 2 & \\ \cline{2-5} GM based Region (AD vs. MCI) & **Olfactory\_R** & 61 & 2.6409 & 4 & \\ \cline{2-5} (AD vs. MCI) & **Temporal\_Sup\_R** & 42 & 2.5427 & 5 & \\ \cline{2-5}  & **Lingual\_R** & 79 & 2.5406 & 5 & \\ \cline{2-5}  & **Insua\_L** & 114 & 2.4783 & 6 & \\ \cline{2-5}  & **Hippocampus\_L** & 66 & 2.5187 & 10 & \\ \hline  & **Temporal\_Sup\_R** & 1475 & 4.6274 & 1 & \\ \cline{2-5}  & **Temporal\_Mid\_R** & 1310 & 4.5555 & 1 & \\ \cline{2-5}  & **Hippocampus\_R** & 873 & 5.9036 & 1 & \\ \cline{2-5}  & **ParaHippocampal\_R** & 730 & 4.6921 & 1 & \\ \cline{2-5} (CN vs. MCI) & **Temporal\_Mid\_L** & 1337 & 4.6585 & 1 & \\ \cline{2-5}  & **Precuneus\_L** & 888 & 4.8104 & 2 & \\ \cline{2-5}  & **Fusiform\_L** & 852 & 4.498 & 2 & \\ \cline{2-5}  & **Hippocampus\_L** & 667 & 5.6445 & 2 & \\ \cline{2-5}  & **Temporal\_Inf\_L** & 505 & 4.6409 & 2 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Min-Max Algorithm  
The min-max method is used as a positioning technique in the range-based localization. The idea of the min-max algorithm is to make a box area or square for each anchor node using its location and calculated distance. Then, the overlap of these squares is determined. The location of the node is put in the centre of the overlap box. That is to say, each anchor node determines the RSSI rate from the object node and computes its distance to an object node using the RSSI rate based on the radio propagation model. Then, a square with two times the measured distance is sketched over the anchor node [162]. The object node is situated within the intersecting area of the squares sketched around all anchor nodes. The min-max algorithm is focused on 3D and 2D indoor localization [166; 167]. The method can be easily implemented because it essentially consists of a small number of additions, subtractions and logical comparisons [168]. Min-max supports a coarse location approximation, and can give a high location error in some situations, such as when a bounding box is used instead of a circle, providing a broader region measured from each anchor [162]. Moreover, the weighted centroid localization estimation has the same manner as the min-max algorithm [169]. Therefore, an extended min-max method was proposed to increase the precision of the min-max method containing more operations [168]. The min-max method for a node with distance estimates to three anchors are illustrated in Figure 10.  
The bounding box of anchor \(a\) is created by adding and subtracting the estimated distance \(d_{a}\) from the anchor position \((x_{a},y_{a})\):  
\[[x_{a}-d_{a},y_{a}-d_{a}]\times[x_{a}+d_{a},y_{a}+d_{a}]. \tag{5}\]  
The intersection of the bounding boxes is computed by taking the maximum of all coordinate minimums and the minimum of all maximums:  
\[\begin{array}{l}[\max(x_{i}-d_{i}),\max(y_{i}-d_{i})]\\ \times[\min(x_{i}+d_{i}),\min(y_{i}+d_{i})].\end{array} \tag{6}\]  
The final position is set to the average of both corner coordinates [162; 170].
## 5 Limitation and Future Works  
The main objective of the proposed EMLWDF algorithm is to enhance the QoS of cell-edge users along with maintaining the average user throughput. However, the performance of the proposed algorithm decreases when there are a higher number of users in the cell due to the lack of available resources and high congestion. As a future work, the above problem can be solved by introducing the queue length of each user to the scheduler for different types of data. Queue length for real-time and non-real-time data will help the scheduler to allocate the RBs to the users according to their requirements more efficiently. Moreover, implementing improved and higher multiple input multiple output (MIMO) techniques can increase the capacity of the cell and thus can serve more users.
# Review of Indoor Positioning: Radio Wave Technology
## 2 Parameter Based Positioning
### Doa  
DOA-based measurements use the angle information of the received signal to estimate its position [74]. The DOA approach, also called AOA, is simpler than time-based measurements because of the estimation of the 2D position with only two angle measurements. The DOA-based positioning system is the evaluation of the signal AOA. The accuracy of the DOA-based localization system is highly impactful with regard to multipath effects. However, this technique depends on accurate angle measurements. The DOA estimation can be done by using an antenna array or direction. In addition, DOA-based systems have proposed and applied for a localization system integrating with different measurement  
Figure 6: AOA-based triangulation.  
techniques such as RSS, TOF, TDOA, and RTOF. There are several different antenna implementations such as the narrowband system, switch beam, phase antenna array, and UWB-based system estimate localization based on DOA algorithms [75]. The DOA-based localization systems need a suitable antenna with different requirements.  
DOA-based techniques are divided into the offline and online technique based on the applications [74]. In offline method, this computes multiple times, and the average value label as the fingerprints. By using these fingerprints, the triangulation method estimates the location. The offline systems have larger complexity and can be utilized for offline applications. In online method, the angles are determined from the received signals and the triangulation method estimates the position. These methods have smaller complexity and utilize real time applications. The DOA techniques have been presented for an indoor localization system to estimate channel characteristics and focus on the multipath propagation interference problem [76; 77]. Moreover, a hybrid joint direction and time difference of arrival (JDTDOA) approach has introduced the precision of the system performance [78].
## 5 Classification of Smart Grid Cyberattack
### Data Secrecy Attacks
#### 5.2.2 Spoofing  
Spoofing is a method used by cybercriminals to falsely act as a legitimate or known source. Spoofing can take many different forms, including IP spoofing, email spoofing, DNS spoofing, GPS spoofing, and website spoofing. Additionally, spoofing may also involve software exploitation assaults, message replays, and man-in-the-middle (MITM) attacks [92]. The integrity and confidentiality of an energy smart system can be compromised by spoofing attacks. For the smart grid to operate reliably and efficiently, data accuracy is essential. Data from GPS timestamps, currents, voltages, or frequencies can all be breached. The loss of integrity and availability can result from spoofing attacks such as identity or data spoofing. Such attacks can seriously impair the functioning, stability, security, and reliability of a smart grid. Furthermore, GPS location data can be subjected to cyberattacks in a variety of ways. One such method is global position system (GPS) spoofing, in which the attacker creates fake GPS signals to provide false location data. Some of the most effective defenses against spoofing attacks are as follows: the use of several devices to monitor the power communication line, cooperation between GPS services, a single data feed, and the synchronization of measurements through the NTP (network timing protocol) at various locations in real time [93; 94].
## 4 Proposed Framework  
Statistical model checking concentrates on performing the right number of simulations to obtain statistically reliable estimations, such as the probability of success in reaching a goal state. On the other hand, process mining focuses on constructing and/or optimising a system model using logs of its traces. Drawing on the strengths of these two methodologies, as shown in Figures 3 and 4, we propose a framework that integrates SMC and PM. This fusion aids in identifying anomalies and areas of concern within models discovered through PM. Notably, it addresses complex business processes, which are often challenging to model comprehensively. Figure 5 illustrates the proposed framework.  
SMC, with its capacity to manage the state-space explosion problem and its ability to handle complex process behaviours, is a powerful tool for process mining. The application of SMC could greatly aid in understanding, optimising, and controlling complex business processes, thus offering significant value for organisations striving for efficiency and reliability. Furthermore, it could recommend improvements based on the statistically robust results estimated through SMC. The proposed framework is divided into three major parts, as shown below:  
1. Process-mining model.
2. Modelling and verification.
3. Analysis.