esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measures to Overcome the Challenges
### Dust Accumulation on BIPV
The robots will be provided with sensors to sense the level of dust and moisture on the surface. The robot will move in an upward/downward direction on the rails provided on the side of the envelopes (Figure 22). The implementation of DCR as shown in Figure 23 can rectify the dust accumulation issue on the BIPV system on the roof. The practical implementation of such technology on building facades was not reported. The combination of cleaning robots over the BIPV system and aerial robots for dust prediction on modules can play a significant role in resolving the issue on a large-scale basis. As discussed earlier, the dusting of the BIPV panels is challenging in terms of safety and vertical height. The current advancement in sensing technology and communication boost the applicability of drone and robotics-powered automatic system for the identification and cleaning of dust on BIPV panels. Figure 21: A new CMS uses a radiometer sensor embedded in a UAV to detect dust on PV panels [45]. Figure 22: Adjustment of DCR on BIPV facade [62]. Figure 24 presents an amalgamation of multiple technologies and a LoRa-based network establishment for an automatic dust cleaning system. Drone-based mode, PV panel mode, robotic wiper mote, and drone yard are the components of the proposed architecture that are connected to the cloud server through a LoRa-based gateway. In this, the authorities initiate the instructions to the drones located in the drone yard to proceed with the inspection of the dust status on BIPV panels through the LoRa-based gateway. As the LoRa-based gateway is based on LoRa and Wi-Fi, so it can receive the instructions from the cloud server through the internet and communicate the instructions to the drones through LoRa. Figure 23: Adjustment of DCR on BIPV roof [62]. Figure 24: Proposed architecture for the robotic and drone-based automatic wiper system. Drone-based more embedded in the drones enables to detect the dust based on visuals obtained through the camera module. The obtained images are computed on the AI-based computing unit with co-processor to identify the dust in real-time. If the dust is identified, then the drones instruct the PV panel note to send the GPS location to the robotics wiper mode through the LoRa communication. Here the PV panel note is embedded in every panel so that every panel will be connected with the drone-based mote and robotic wiper mode through LoRa communication. Based on the request from the particular PV panel mote, the robotic wiper mode enables the robotic wiper system to locate and clean the dust on the BIPV panels. Simultaneously, it receives the request from the other PV panel mote, then it moves to that location and performs a cleaning activity. Along with building structure and area, the complete system can be customized.
## 5 IoT Service Provisioning via NDN in the Smart Campus
### Service Provisioning
#### 5.3.2 Forwarding towards the Cloud  
This forwarding stage is performed only if the service has not been allocated in the path between CAN and PAN, i.e., the service provisioning time offered by the cloud is shorter than the one offered by on-path NDN-fog nodes.  
Specifically, the NACK packet is forwarded hop-by-hop back to the CAN, which starts the forwarding process towards the cloud. It modifies the service interest by adding a well-known prefix, e.g., _/cloud_, to the existing service name and resends the request. Thanks to the newly added name prefix, the request is now forwarded towards the cloud and, therefore, it will reach the root node. Each NDN-fog node receiving the service interest applies the previously discussed processing and evaluates the service provisioning time.  
The procedure continues until the interest reaches the root node, which acts in a way slightly similar to the PAN: (i) if no node is available for the execution, the service interest is finally forwarded towards the cloud; (ii) otherwise, the node with the lowest service provisioning time is selected as the executor and a Data ACK is sent back to notify the decision.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework
### Analysis of Haze and Rain Models for Creating Synthesized Training Dataset
#### 2.1.2 Rain Model (Rain Streak Model)  
In rainy road environments, the input image is usually affected by rain streak. Thus, the rain streak model is appropriate for representing rainy images. The rain model used to synthesize the shape of the rain streaks, which has been widely used in previous studies, is as follows:  
\[O(x)=B(x)+S(x), \tag{3}\]  
where \(O\) is the observed image degraded by the raindrop. \(B\) indicates the clean background image, and \(S\) is the rain streak image. There are many studies on removing rain using this model [20; 21]. They focused on separating background \(B\) and rain \(S\) through observed \(O\). However, this simple addition model is insufficient to represent the rain phenomenon. For this reason, [PERSON] et al. [22] suggested using a nonlinear composite model with a screen blending method rather than using the existing linear addition synthesis model. The screen blend model can model some visual properties of real-world rain images, such as the effectiveness of internal reflections, to generate visually more true rain images. The combination of rain and background layers can more naturally express signal-defendant properties using screen blending techniques. Therefore, the screen blend model is expected to be more effective in training the network by expressing the combination of background and background more naturally. The model proposed by [PERSON] [22] is as follows:  
\[O(x)=B(x)+S(x)-B(x)\circ S(x), \tag{4}\]  
where \(\circ\) operator indicates point-wise multiplication. This nonlinear synthesis models was used for the training dataset.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 3 Preliminaries
### Elliptic Curve Signcryption
#### 3.3.3 Un-Signcryption Phase  
In the unsigncryption phase, decryption and signature verification are performed at the receiving side. Upon receiving the signcrypted message \(\{c,\ t,\ w\}\) from [PERSON], [PERSON] performs the computations as given below.  
* Compute \(w=sv_{y}\ mod\ q\)
* If Scheme 1 is used then it regenerates the key \(K\) as: \[K=H(wP_{x}+wtG)\] (11)
* If Scheme 2 is used then it regenerates the key \(K\) as: \[K=H(wG+wtP_{x})\] (12)
* Decrypt the ciphertext as: \[M=D_{k}(c)\] (13)
* Accept the message \(M\) if: \[KH_{k^{\prime}}\Big{(}M,\ Blind_{Info}\Big{)}=t\] (14) where Scheme-1 is the Shortened Elliptic Curve Digital Signature Scheme 1 and Scheme-2 is the Shortened Elliptic Curve Digital Signature Scheme 2. In this way, the functionality of the digital signature and encryption is achieved in a single logical phase.
# Sequence-Information Recognition Method Based on Integrated mDTW
## 3 Methodology
### Multidimensional DTW
Since the information on location is contained in displacement data, much of the deep-learning-based handwriting research only relies on displacement. However, the cluster model in this paper does not have a parameter optimizing process to learn the information of location from displacement, so we added \(ld\) in Equation (3) to enhance the matching performance. \(\beta\) is the weight of writing states. Considering one character has almost 100 points and 5-10 strokes means that the rate of end stroke is about 10% and character end is 1%, \(\beta\) is set as \([1,10,100]\) to balance the infect of different writing states. The dimension of a computed matrix \(Dm\) is \(n\times m\), and each element represents \(dsum\) distance of two points. The second step of mDTW is computing a matrix \(G\) for final point matching through an iterative process. This process is computed point-by-point by Equation (6)  
\[G(i,j)=\min\left\{ \begin{array}{c}G(i-1,j)+Dm(i,j)\\ G(i-1,j-1)+\lambda\cdot Dm(i,j)\\ G(i,j-1)+Dm(i,j)\end{array} \right. \tag{6}\]  
\(\lambda\) is the weight of temporal similarity, which is configured as a constant 2 in traditional DTW algorithms. In this paper, we made it flexible to fit the multi-dim point matching task. \(G\) is computed from bottom to the top, form left to right. As shown in Figure 2, DTW has following steps in summery:  
* Step 1: compute \(Dm\) Equations (2)-(5)
* Step 2: initialize \(G\) as a zero matrix
* Step 3: compute the first row (\(G_{1}\)) of \(G\) by Equation (6) form \(G_{11}\) to \(G_{1n}\) (green values in Figure 2)
* Step 4: repeat Step 3 \(m\) times until all elements in \(G\) are updated
* Step 5: start from \(G_{mn}\), jump to a temporal location via \(min\left\{G_{i,j-1},G_{i-1,j-1},G_{i-1,j}\right\}\). * Step 6: Repeat Step5 until terminal \(G_{11}\) (red arrows in Figure 2)  
Completing the above steps will return the matrix \(G\), where \(G_{mn}\) could be recognized as the final distance of two sequences \(DTWdist(seq_{i},seq_{j})\). Meanwhile, the computing process has a very high time cost in most of the corresponding tasks especially when dealing with complex sequence problems. Therefore, the algorithm is accelerated by parallel computing for practical application. The method of acceleration uses GPU to do high-dimension matrix multiplications. In the case of ensuring that the GPU memory limit is not exceeded, we could set a batch of sequences to do DTW computation parallel. This method further reduces the calculation time cost of the system.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 3 The Proposed CSS Method
### Training Methodology
The binary cross-entropy loss function is used as the loss function for BSWSS-Net and can be expressed as  
\[L_{WSS}=-\frac{1}{N_{c}N_{WSS}}\sum\limits_{n_{WSS}=1}^{N_{WSS}}\sum\limits_{ n_{c}=1}^{N_{c}}\Big{(}s_{n_{c}}^{(n_{WSS})}\log\Big{(}\hat{s}_{n_{c}}^{(n_{ WSS})}\Big{)}+\Big{(}1-s_{n_{c}}^{(n_{WSS})}\Big{)}\Big{(}\log\Big{(}1-\hat{s}_{n_{c}}^{( n_{WSS})}\Big{)}\Big{)}\Big{)}\Big{)}. \tag{18}\]  
When training BSWSS-Net, we use the Adam optimizer with a maximum of 200 epochs and stop the training process when an increase in the loss value on the test set is detected. The learning rate is initially set to 0.001 for the first 30 epochs and then reduced to 0.0001. To prevent overfitting, we add dropout layers between the fully connected layers, with a dropout rate set to 0.05.
# Perceptual Characteristics of Voice Identification in Noisy Environments
## 5 Conclusions  
We designed and implemented voice identity perception experiments under different noise environments, obtained the perception rules, and established perception models. Furthermore, the influence of the frequency distribution on voice identity perception was determined. The experimental results provide a reference for evaluating courtroom voice quality or controlling the recording environment. The main conclusions are as follows:  
1. In all three types of noise environments, the perceptional accuracy increases first and then becomes stable as the SNRs increase. Equations can be used to estimate the perceptual result.
2. When the SNR is greater than 0 dB, there is no significant difference among the three types of noise. However, the noise type will affect voice identity perception when the SNR is less than 0 dB. The influence of speech noise on voice identity perception will become more obvious than white noise and pink noise, while the perception difference between white noise and pink noise is not significant.
3. Different frequency distributions of speech noise affect voice identity perception differently. The speech noise in the low-frequency and medium-frequency bands significantly impacts voice identity perception, while the high-frequency speech noise has less interference. However, the contribution of acoustic information to identity perception at high frequencies becomes obvious with the reduction in the SNR.  
Conceptualization, Y.Z., Y.L. and H.N.; methodology, Y.Z., Y.L. and H.N.; software, Y.Z. and H.N.; validation, Y.Z.; formal analysis, Y.Z.; investigation, Y.Z., Y.L. and H.N.; resources, Y.Z.; data curation, Y.Z. and Y.L.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z. and Y.L.; visualization, Y.Z.; supervision, Y.L.; project administration, Y.Z.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities under Grant No. CUC2019B030.  
The study was approved by the Research Ethics Committee of Communication University of China.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the reviewers and editors for their reviews of this research.  
The authors declare no conflict of interest.
# Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis
## 3 Results and Discussion
The applied method's advantage is that it automatically learns discriminating characteristics through the training process of the network without requiring manual feature selection. The results of the two cases studied demonstrated the advantages of the developed method [58]. [PERSON] et al. presented a novel approach for condition monitoring and fault diagnosis in wind turbines based on structural break detection in SCADA data. The technique was employed for the DC of a wind turbine with 2 MW rated power using SCADA data related to the temperature. To detect structural changes, they used a method based on control charts, where the sequences of the calculated probability (p-values) were plotted together with the critical line and defined by the significance level. The method was validated using two known fault events on the WTs, and the results demonstrated the effectiveness of the method in monitoring the WT and confidently detecting abnormal faults [59]. [PERSON] et al. studied multiple combinations of bearing defects, including both dual- and multiple-defect conditions. They selected two predominant methods of fault diagnosis: vibration monitoring utilizing time-frequency scalograms extracted using a Continuous Waveform Transect (CWT) and a Non-Invasive Infrared Thermography (IRT). The group found adequate accuracy in both the dual- and multiple-fault conditions for vibration-based fault diagnosis, with a range of 99.39% to 99.97%. Meanwhile, for the IRT-based failure diagnosis, a 100% classification accuracy was reached for double and multiple failures under all conditions [60]. Table 2 presents some articles in the scientific literature that approach the use of the prospected technologies. Figure 2 shows the main applicants (a) and inventors (b) of the prospected patent documents. The Vestas Wind Systems A/S (Aarhus N, Arhus, Denmark) appears in first place with 26 documents. The company is a Danish company and the global leader in sustainable energy solutions, with more than 40 years of experience in wind energy, and currently has more than 160 GW of wind turbines installed in 88 countries, preventing 1.5 billion tonnes3 of CO2 from being emitted into the atmosphere. The group is specialized in the manufacturing, installation, and servicing of wind turbines and is the market leader
# Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking
## 5 Simulations and Results
### Spacecraft Target Tracking Simulation
#### 5.2.3 Simulation Result and Analysis  
Figure 2a shows the spacecraft ground simulator on which the algorithm of High-Precision Orbit Propagation (HPOP) is run, which takes into account the perturbation effects of the Earth's high-order non-spherical gravity, atmospheric drag, solar light pressure, three-body gravity, and tides. Among them, the 21-layer Earth model, atmospheric drag coefficient \(c_{d}=2.2\), and spacecraft surface mass ratio 0.02 m\({}^{2}\)/kg are considered; the atmospheric density model adopts the Jacchia-Roberts model; and the solar luminous pressure coefficient \(c_{r}=1\). The orbital epoch of the spacecraft considered in the experiment is 16:00:00 (UTC) on 1 September 2016, and the six orbital roots are:  
\[a=7028.14\text{ km},\ e=0,\ i=80.7^{\circ},\ \Omega=63.8^{\circ},\ \omega=0^{\circ},\ f=107.1^{\circ}\]As shown in Figure 2b, the lowest measured elevation angle of the radar is 10\({}^{\circ}\). It is assumed that the radar ranging error is 60 m and the angle error is 0.02\({}^{\circ}\).  
The algorithm proposed in this paper is compared with the SCKF and OSCKF, and the root mean square error (RMSE) is used to evaluate the spacecraft tracking accuracy. The Monte-Carlo simulation is run 200 times, and the results are shown in Figure 3. The mean positioning RMSE and velocity RMSE are listed in Table 2.  
Figure 3: The spacecraft tracking RMSE of the various filters. **(a)** Position RMSE. **(b)** Velocity RMSE.  
Figure 2: Schematic diagram of spacecraft tracking. **(a)** Spacecraft simulator. **(b)** Radar spacecraft tracking.  
It can be seen that the spacecraft tracking accuracy of the OSCKF is higher than that of the SCKF; specifically, the position tracking accuracy is increased by 2.21%, and the velocity tracking accuracy is increased by 1.94%, which indicates that the orthogonal transformation can improve the tracking accuracy. The spacecraft tracking accuracy of the IOSCKF is higher than that of the OSCKF; specifically, the position tracking accuracy is increased by 2.71%, and the velocity tracking accuracy is increased by 2.97%, which indicates that the tracking accuracy can be effectively improved by introducing iterative calculation into the measurement equation, thus verifying the effectiveness of the method presented in this paper.
## 5 Experimental Results  
### Dataset Description  
This study employs multiple datasets to enhance the robustness and applicability of the proposed fall-detection system. Below are descriptions of the datasets used:  
* NTU RGB+D Dataset (NTU): This dataset [45] is renowned for its extensive collection of human activities captured through high-resolution videos. Researchers selected a subset of activities specifically relevant to medical conditions, focusing on critical actions for assessing health-related incidents. This subset includes activities such as sneezing/coughing, staggering, falling, headache, chest pain, back pain, neck pain, nausea/vomiting, and fan self, providing a rich resource for analyzing fall-related events.
* Multiple Cameras Fall Dataset (MCFD): [PERSON] et al. [46] developed the MCFD, which consists of 192 video recordings captured from 24 different scenarios using eight synchronized cameras with a resolution of 720 \(\times\) 480. This dataset uniquely includes a wide variety of fall scenarios (22) and activities of daily living (2), such as moving boxes, dressing, and room cleaning, recorded from multiple viewpoints. This diversity aids in simulating more realistic fall situations.
* UR Fall Dataset (URFD): The Computational Modeling Discipline Centre at the University of Rzeszow [47] created the UR fall-detection dataset (URFD), which includes 70 videos--30 depicting falls and 40 showing non-fall activities such as walking, sitting, and squatting. The videos capture performers exhibiting a range of fall-related behaviors, including backward leaning and sudden descents to the ground. The videos are recorded in RGB format with a resolution of 640 \(\times\) 480.  
Each dataset provides unique insights and challenges, offering a comprehensive platform for testing and improving the proposed fall-detection algorithms.
# A Feature Selection Algorithm Performance Metric for Comparative Analysis
## 4 Baseline Fitness Improvement  
The baseline solution of a problem is defined herein as the solution with all features selected, i.e., \(s^{*}\). The baseline fitness, \(f(s^{*})\), is the fitness of the baseline solution. The baseline fitness improvement (BFI) is a measure of the benefit gained by applying feature selection, relative to the baseline fitness of a problem. The BFI of a solution is calculated as  
\[BFI(s)=f(s)-f(s^{*}) \tag{4}\]  
The BFI is interpreted as the potential gain of performing feature selection. The BFI measure also bears a resemblance to the AIC [16] for model selection. The fundamental difference in behaviour of the BFI and AIC lies in the interpretation of the individual metrics. The AIC establishes the quality of a model with respect to other models, whereas the BFI establishes the relative quality of a feature selection solution with respect to the baseline solution for a feature selection problem. A measure of algorithm performance, \(f_{c}(s)\), alone is not representative of feature selection algorithm performance, but rather of the underlying classifier performance. Table 1 contains fictional algorithm performance, baseline fitness and the respective calculated BFI values to illustrate the differences in interpretation.  
Table 1 shows that a simple comparison of algorithm A for the two datasets using \(f_{c}(s)\) would lead one to believe that feature selection algorithm A performed better on  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(f_{c}(s)\) & \(f_{c}(s^{*})\) & \(BFI(s)\) \\ \hline Dataset 1 & & & \\ \hline A & 0.9 & 0.5 & 0.4 \\ B & 0.4 & 0.5 & \(-\)0.1 \\ C & 0.7 & 0.5 & 0.2 \\ \hline Dataset 2 & & & \\ \hline A & 0.7 & 0.3 & 0.4 \\ B & 0.5 & 0.3 & 0.2 \\ C & 0.9 & 0.3 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Artificial baseline fitness improvement (BFI) and performance.  
Figure 2: Penalty function.  
dataset 1 than on dataset 2. Whilst it may be true that the underlying classifier performed better on dataset 1 than on dataset 2 with regard to classifier accuracy, it is not a true representation of the effectiveness of the feature selection algorithm. The BFI values show that algorithm A achieved a 0.4 improvement over the baseline fitness for both datasets. Therefore, the feature selection algorithm actually managed to improve the classification performance equally for the two datasets. In the absence of knowledge of the global optima of the problem, the baseline fitness acts as a universal reference point for real world feature selection problems. Therefore, the BFI can be used to assess the performance of a feature selection algorithm across datasets.  
The range of the BFI measure is dependant on the objective function that is used. The robustness of the proposed fitness function in Equation (1), and BFI measure in Equation (4), is evaluated in Section 5.