esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
### Conclusions  
Failures in the critical subsystems of CNC machines are bound to happen during their operational lifetime, thus making maintenance a necessary activity to ensure nominal operation. The linear axis being one of these critical subsystems when it fails can lead to significant downtime and maintenance costs, especially when the failure cannot be easily detected or localized, and troubleshooting must be done to determine the root cause. Misalignment is one of these root-cause FFs that over time will lead to the complete failure of the linear axis if not promptly addressed. From the reviewed literature, there are a lack of studies that focus on utilizing signal segmentation processing techniques to localize misalignment. Additionally, the studies conducted on misalignment analyzed the full stroke to detect the fault, but did not focus on localizing the area where it was present. Finally, there are limited comparative studies that investigate what occurs after a repair is conducted on a faulty system and how it differentiates from the originally established baseline. Therefore, the presented paper aimed at filling these gaps by introducing and applying a novel vibration signal segmentation technique to localize a misalignment based on the change of evaluated time domain features. The three major conclusions from the performed studies include the following:  
* The usability of RMS and standard deviation features was increased by applying the segmentation technique. Since at a full stroke analysis these features exhibited less than 10% error when compared to the baseline state. However, when the segmentation technique is applied the features' percentage error increased above 20% at the quarter segment level, and above 40% at the eighth segment level.
* The presented technique localized the area where the misalignment was introduced by relating the distance traveled to the segment number. Furthermore, the localization was further confirmed when comparing both forward and reverse strokes due to their anomalous segments being identified on opposite sides.
* The repair state analysis demonstrated a 15% error of the RMS and standard deviation features in the forward direction where the misalignment was introduced, when compared to the baseline condition. However, these changes can only be observed when the signal is segmented to the quarter and eighth segment levels. Comparatively, the reverse direction showed that both of these features stabilized within the baseline limits. This is important, since segmentation can be utilized as a verification tool after a repair and to update operational thresholds based on segmented analysis to avoid false alarms.
# Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform
## 4 Elliptical Hough Transform
### Algorithm Design
Using apriori information to partition the parameter search space, we obtain a total of \(n_{a}\times n_{b}\times n_{\theta}\) quantized parameters. Assuming that the total number of measurements is \(m\) during N-frame scan, then in theory, the total of \(n_{a}\times n_{b}\times n_{\theta}\times m\) cumulative calculations will be performed. In fact, since \(a,b,\theta\) have the constraint relationship Equation (37), \(b\) does not need to be quantified, and the total computation is \(n_{a}\times n_{\theta}\times m\). It follows that the more the number of targets, the denser the clutter, the longer the accumulation time, and the higher the accuracy of parameter quantization, the more time consuming the algorithm is. Furthermore, in the above steps, we have used a three-dimensional search parameter space \(a-b-\theta\) instead of two-dimensional space \(a-b\) indicated by Equations (13) and (14). Since Equations (13) and (14) are relatively computationally sophisticated, we only use them as a constraint to reduce the number of search points. That is to say, when implementing the step of discretization Equation (39), we select only the points that satisfy Equations (13) or (14) to implement the mapping. Using Equation (14) as an example, the selection is judged as  
\[\left|b(i,k,l)-\hat{b}(i,k)\right|\leq\Delta b \tag{45}\]  
where \(b(i,k,l)\) is the measurement-related computed value of \(b\), \(\hat{b}(i,k)\) is the theoretical computed value of \(b\) according to Equation (14), and \(\Delta b\) is introduced to account for all types of measurement errors. With the steps of preselection, unlike pairs of \(a(i)-b(j)-\theta(k)\) can be greatly reduced.
## 7 System Performance and Evaluation
### Outlier Detection and Event Identification
(**a**) All data with five clusters depend on the number of parameters involved; (**b**) Clusters for temperature data outside the mine, the midway point of the mine and at the mine face during the specified period of monitoring; (**c**) Clusters of humidity data monitored during specified periods outside the mine, at the midway point of the mine and at the mine face; (**d**) Clusters of CH\({}_{4}\) monitored data from the midway point of the mine and at mine face 1; (**e**) Clusters of CO\({}_{2}\) monitored at the midway point of the mine and at the mine face; (**f**) Clusters of CO monitored at the midway point and mine face.
## 4 Measurements Validation  
For measurement validation, three prototypes of a 1 \(\times\) 4 patch antenna array with integrated P-CRLH phase shifters are manufactured and are shown in Figure 13c. The substrate used has a thickness of 1.27 mm, \(\varepsilon_{r}=2.2\) and \(tan\delta\) = 0.002. The dimensions of the patches are shown in Figure 8 (Section 3) operating at 5.5 GHz. The fabricated proposed unit cell P-CRLH phase shifter with dimensions discussed in Section 2 is given in Figure 13b. For size comparison, a photograph of the recently published P-CRLH phase shifter [29] is also shown in Figure 13a. The reflection coefficient, insertion loss and phase response of the fabricated unit cell P-CRLH phase shifter are measured with the Agilent Network Analyzer in Figure 13d. The tiny magnets beneath the ground plane are used to activate the MRSs. The measured results are shown in Figure 14. In Figure 14a, the reflection coefficient is less than 10 dB for both the ports, indicating good impedance matching characteristics, the maximum insertion loss measured is \(-2\) dB, showing good propagation behavior. In Figure 14b, the phase response at design frequency of 5.5 GHz is around \(-86^{\circ}\), which is in close agreement with the simulated phase of \(-92^{\circ}\) in Figure 7. There are three main advantages of the proposed P-CRLH phase shifter and the proposed P-CRLH phase shifters-excited phased array as compared to similar work in [29]. (1) There is a 60% size reduction in the proposed P-CRLH phase shifter. (2) There is a 73.68% reduction on the requirements of the printed microstrip transmission lines on both ends of the proposed P-CRLH phase shifter. (3) The proposed P-CRLH excited phased array in Figure 13c is fabricated on a single PCB as compared to phased array in [29], where the antenna array and phase shifters are on separate PCBs.  
Figure 13: Photographs of the fabricated (**a**) unit cell P-CRLH phase shifter reported in [29]; (**b**) proposed unit cell P-CRLH phase shifter; (**c**) a 1 \(\times\) 4 linear phased array with integrated proposed P-CRLH phase shifters in an anechoic chamber and (**d**) unit cell P-CRLH phase shifter measurements.  
Next, the measured results for three prototypes of the broadside scan, the 15-degree scan and the 30-degree scan phased array are discussed.
## 4 Simulation and Results
### Learning ML Algorithm  
An intersection on a two-level four-lane highway in Changzhou, China, near Zheng Zhou Shi Park (Figure 4) was selected as a learning and testing service area to evaluate the system's sustainability. The V2I transceiver was assumed to be deployed on a frame road structure. The dataset contained a total of 12,680 examples, where 20% was allocated for a test set.  
Figure 5 shows the accuracy of the ML algorithm represented by the error matrix. Following the results, the percentage of correctly identified classes from the total number of examples was 94% on the test set. Considering the complex multipath environment in the  
Figure 4: Image of the selected service area (**top**), its digital model in SUMO (**bottom**), and beam-steering sectors (**top**, colour-marked; the sector’s index corresponds to the index of the serving PAA).  
service area due to the building density, one can conclude that the system's sustainability increased the compared results, where the proportion of correctly identified classes did not exceed 75%.
## 5 Applications of PCMs in Buildings
### Gypsum Boards  
Another constructional element that can be used for TES are gypsum boards. The implementation of PCM technology into gypsum boards has been studied since 1990 because gypsum boards are low-cost and they are used widely in plenty of constructional applications. Srinivasaraonaik et al. [89] incorporated microencapsulated PCMs into gypsum mixture for gypsum boards. The addition of PCMs caused reduction in the compressive and flexural strength, while the porosity of the gypsum composites increased. As long as it concerns the thermal properties, thermal conductivity of the gypsum composites decreased in proportion with the amount of PCM incorporation. Similar results were obtained from the integration of graphene oxide microencapsulated PCM composites [73]. Additionally, in another study, Juan Pablo Bravo et al. [90] modified gypsum boards with a paraffinic microencapsulated PCM (20 wt%) and implemented a two-month enclosure test. The results were analyzed in a computer simulation program and they showed that microencapsulated PCMs with phase change temperature from 26.5 \({}^{\circ}\)C to 29.2 \({}^{\circ}\)C allow the charge and discharge of the material and, thus, the provision of indoor thermal comfort.
_Applied_  
_sciences_  
_IoT and Deep Learning Based Approach for Rapid Screening_  
and Face Mask Detection for Infection Spread Control_  
**[PERSON] 1,2, [PERSON] 1,3, [PERSON] 2, [PERSON] 4, [PERSON] 5, [PERSON] 1 and [PERSON] 1,*  
1 School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China; [EMAIL_ADDRESS] (S.H.); [EMAIL_ADDRESS] (Y.Y.); [EMAIL_ADDRESS] (J.A.W.)  
2 Department of Computer Science, NCBA&E, Rahim Kar Khan 64200, Pakistan; [EMAIL_ADDRESS]  
3 Distributed Systems Group, University of Unibsurg-Essen, 40750 Unibsurg, Germany  
4 Department of Data Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan; [EMAIL_ADDRESS]  
5 Department of Zoology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS]  
**Abstract** The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.  
**Keywords:** COVID-19 rapid screening; face mask detection; IoT; deep learning
## 3 The Proposed GEER-DLAD Model
### ELC Based Data Reduction Technique  
Initially, the IoT devices execute the data collection process to gather the data regarding the target environment. A lightweight model of compression named ELC is projected in [22]. It is a robust error-bounded lossy compression technique for High Performance Computing (HPC) domains. The actual SZ model compresses the incoming data files in binary form; it is composed of data structure and data types. In this literature, the SZ approach has been adopted for IoT tools by assuming the floating data type and eliminate alternate types that reduce the code size and simple execution. Hence, the strategy behind selecting SZ for IoT domains are listed below:  
* SZ enables compression of multivariate time series with distinct characteristics and varying scales.
* SZ activates the data loss management with the help of error bound mechanism.
* SZ results in maximum compression ratio when compared with multidimensional transform domains.  
The gathered data are M \(\times\) N array, where M and N define the reading and feature count. For instance, assume a sensor movement gathered from gyroscope and accelerometer metrics for 3 coordinate axes over a period P. At this point, M is allocated as 128, and N is 6. Initially, 2-D array is changed into 1-D array. Next, a flattened array undergoes compression with the help of lossy SZ method. At last, final binary array has been sent to the edge.  
The SZ compressor is started by reducing 1-D array under the application of adaptive curve-fitting methods [23]. The optimal step applies 3 prediction technologies namely, Preceding Neighbor Fitting (PNF), Linear-Curve Fitting (LCF), and Quadratic-Curve Fitting (QCF). The variations among the 3 schemes of precursor data points has to fit the actual value. Moreover, the nearest approximation has been provided as a result. It is apparent that the fitted data is converted as integer quantization and subjected to the encoding phase with the help of the Huffman tree. In case of error bound, absolute error bound is applied where compression and decompression errors are restricted within absolute error.
## 5 Results of Testing the Omega-k Algorithm
### Simulated LFM-CW SAR Rave Data  
In Figure 18, the LFM-CW SAR system is represented by the black rhombus at a certain height, which moves in the azimuth direction and the chirp signals are transmitted and received in the slant range direction. The antenna footprint of the LFM-CW SAR system is represented by the green triangle and the green asterisk represents the isolated and isotropic point target; the blue quadrilateral is the scene scanned with the radar.  
On the other hand, Figure 18 shows blue chirp signals, this observed chirp signal \(s(t)\) is continuous, which is called a continuous wave linear frequency modulated chirp. The LFM-CW SAR system is used to generate a continuous wave chirp signal with linear frequency modulation shown in Figure 18 using the main parameters of Table 4 and Equation (21); the magnitude and phase of the LFM-CW SAR raw data is shown in Figure 19, which has range and azimuth directions, and in the magnitude of the SAR raw data a yellow rectangle is observed that has no meaning and has no direct application.  
Figure 16: SAR image obtained with the modified Omega-k algorithm using the SAR raw data of Figure 14a.  
Figure 17: (**a**) Sample of a SAR image obtained with the range-Doppler algorithm (SAR Training Processor v1.1) [24]. (**b**) Sample of a SAR image obtained with the Omega-k algorithm [19; 24]. (**c**) Sample of a SAR image obtained with the proposed Omega-k algorithm.  
The LFM-CW SAR image in Figure 20 of an point target is obtained from the LFM-CW SAR raw data shown in Figure 19 using the modified Omega-k algorithm.  
The quality parameters shown in Table 5 are approximate because they depend on the dimensions of the scene. These parameters are obtained using Table 4 and performing a cut in azimuth and range at the most intense point or pixel of the SAR image of a point target (isolated and isotropic). On the other hand, \(PSLR_{R}\), \(PSLR_{A}\), \(ISLR_{R}\), and \(ISLR_{A}\) are the peak side lobe ratio (PSLR) and integrated side lobe ratio (ISLR) in the range and azimuth, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Parameters** & **Symbol** & **Values** \\ \hline Carrier frequency & \(f_{0}\) & 5.4287 GHz\({}_{z}\) \\ Bandwidth & \(B\) & 170 MHz\({}_{z}\) \\ Pulse repetition rate & \(PRF\) & 307.292 Hz\({}_{z}\) \\ Horizontal beam width & \(\theta_{H}\) & \(11^{\circ}\) \\ SAR platform speed & \(V_{s}\) & 30.1938 m/s \\ Speed of light & \(c\) & \(3\times 10^{8}\) m/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the linear frequency modulation continuous wave SAR system.  
Figure 19: This Figure shows: (**a**) LFM-CW SAR raw data phase. (**b**) LFM-CW SAR raw data magnitude.  
Figure 18: LFM-CW SAR System.
## 1 Introduction
The literature [9] proposed an intelligent multi-model prediction method based on output characteristic clustering and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) for photovoltaic (PV) power generation interval prediction. The combination of the CNN and LSTM effectively improves the modeling accuracy of the intelligent prediction model. The literature [10] proposed an LSTM-Informer model based on the Improved Stacked Ensemble Algorithm (ISt-LSTM-Informer). By integrating the advantages of the two underlying models, the ISt-LSTM-Informer achieves accurate short- and medium-term PV power prediction. The literature [11] proposed an integrated multivariate model based on VMD, a CNN, and a bidirectional gated recurrent unit (BiGRU), which effectively improves the accuracy of prediction. However, none of the above literature considered the optimization of the hyperparameters of neural networks, and the settings of the hyperparameters were selected based on experience, which could not be adaptively adjusted according to the actual scene. In order to realize accurate PV power generation prediction, this paper proposes a combined network prediction model, which integrates NRBO, VMD, and Transformer-BiLSTM. The Transformer decoder partially fuses the BiLSTM network and retains the encoder to obtain the main body of the prediction model, and at the same time, the NRBO algorithm is applied to optimize the relevant hyperparameters of the network model. The NRBO algorithm is used to optimize the hyperparameters of the network model. The VMD of historical PV data is performed to remove the noise and redundant signals, and the GWO algorithm is applied to optimize the key parameters of the VMD. The characteristic parameters related to PV output are selected as inputs to the model, and the predicted values of each modal component are superimposed to obtain the final PV output power.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
###### Abstract  
In this paper, we present our novel approach for the crowdsourced dynamic vertical mapping of buildings. For achieving this, we use the barometric sensor of smartphones to estimate altitude differences and the moment of the outdoor to indoor transition to extract reference pressure. We have identified the outdoor-indoor transition (OITransition) via the fusion of four different sensors. Our approach has been evaluated extensively over a period of 6 months in different humidity, temperature, and cloud-coverage situations, as well as over different hours of the day, and it is found that it can always predict the correct number of floors, while it can approximate the altitude with an average error of 0.5 m.  
indoor mapping; outdoor-indoor transition; CityGML; dynamic mapping; vertical mapping +
Footnote †: journal: Sensors
# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 1 Introduction
### Motivation and Contributions  
This paper introduces a novel system involving multiple UAV clusters assisted by RIS, where the UAVs are scheduled periodically to receive signals from the RIS mounted on a UAV. Our focus is on mitigating interference among the UAV clusters by optimizing the allocation of UAV scheduling time slots while taking into account different UAV scheduling cycles and the constraint of limited RIS arrays. The contributions of this article are outlined below.  
1. We present a UAV cluster system that leverages RIS mounted on UAVs to establish LoS channels between BS and UAVs, and enhance wireless connectivity in obstructed environments. Additionally, we propose designing the RIS coefficients based on the position information of UAVs to minimize the channel estimation overhead.
2. This study demonstrates that by designing RIS with appropriate coefficients, it is possible to estimate the interference between UAV clusters based on the positions of the UAVs. We introduce a UAV interference matrix to describe the interference  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Existing Works** & **Cellular-Connected UAV** & **UAV-Mounted RIS** & **Interference Avoidance** & **Scheduling Optimization** & **Periodic Scheduling** \\ \hline [9] & ✓ & & & ✓ & \\ \hline [13; 14; 15] & & ✓ & & & \\ \hline [16; 17; 18] & & ✓ & & & ✓ & \\ \hline [19] & ✓ & & & ✓ & ✓ & \\ \hline [20; 21] & ✓ & & ✓ & & \\ \hline [22; 23; 24] & & & ✓ & ✓ & & \\ \hline This Paper & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related literature.  
relationship between UAV clusters. In the context of periodical scheduling, we derive the degree of interference between UAV clusters by analyzing the scheduling period and initial scheduling time slot of each UAV. Subsequently, we formulate the UAV scheduling problem as an integer optimization problem.
3. The time slot scheduling problem for UAVs is approached as a multi-stage decision problem, which can be equivalently modeled as an optimal path search problem through the establishment of a UAV scheduling graph. To tackle this issue, we introduce a novel local interference minimization (LIM) scheme that aims to minimize interference arising from newly scheduled UAVs. Additionally, we propose a roll-out interference minimization (RIM) algorithm that builds upon the LIM scheme by utilizing its calculation results to determine optimal decisions for subsequent nodes.
4. The performance of the proposed LIM and RIM schemes is evaluated through simulations and compared to that of the basic sequential scheduling scheme. The results show that the proposed schemes effectively reduce the interference level between UAV clusters while satisfying the constraints of given scheduling periods and a limited number of RISs.