esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Thermal Analysis
### Temperature Profile Results  
Figures 7 and 8 show the temperature profiles in a 0.4 m regolith wall after several lunar day-night cycles for two different solar heat flux corresponding to two different locations on the surface of the Moon, close to the equator (1300 W/m\({}^{2}\)) and closer to the poles (600 W/m\({}^{2}\)). The temperature inside the habitat (\(x=0\)) is maintained constant at 20 \({}^{\circ}\)C. The profiles show clearly that the maximum external temperature depends on the solar flux (120 \({}^{\circ}\)C for a 1300 W/m\({}^{2}\) flux against 50 \({}^{\circ}\)C for a 600 W/m\({}^{2}\) flux). More interestingly, there is a significant difference in the constant temperature thickness close to the habitat between the non-compressed regolith wall with nominal density of 1.6 g/cm\({}^{3}\) and the compressed regolith wall with a density of 4.0 g/cm\({}^{3}\). In the latter case, more than half of the wall does not undergo the large external temperature shifts between the lunar days and nights. Similar insulating property is observed for non-compressed regolith, however, there is a beneficial gain of about 10 cm of isothermal behaviour in the compressed case. It provides another reason to compress regolith for Moon base construction on top of the conclusions from radioprotection studies.  
To keep 20 \({}^{\circ}\)C inside, a source would be required to compensate for the heat escaping through the wall. The tangents to the night 5 profiles at \(x=0\) m give temperature gradients which are used in the Fourier's law to calculate the escape fluxes in the cases of 600 and 1300 W/m\({}^{2}\) of solar heating. To estimate the required heating, a cylinder with a 5-m diameter and 5-m height is taken for reference. The results are summarised in Table 6, which indicates how little heating will be required thanks to the insulating properties of highlands regolith.  
This study points to the remarkable insulation property of highlands regolith (specifically when it is compressed) and to the fact that only the outmost part of compressed regolith needs to be upsized against harsh thermal cycling. After the effect of the initial conditions wears off (here, -70 \({}^{\circ}\)C after two cycles), approximately half the wall keeps its temperatures with small fluctuations between the night and the day.  
Figure 8: Temperature profiles through non-compressed (1.6 g/cm\({}^{3}\), image **a**) and compressed (4.0 g/cm\({}^{3}\), image **b**) highlands regolith walls with the solar heat flux of 1300 W/m\({}^{2}\).  
Figure 7: Temperature profiles through non-compressed (1.6 g/cm\({}^{3}\), image **a**) and compressed (4.0 g/cm\({}^{3}\), image **b**) highlands regolith walls with the solar heat flux of 600 W/m\({}^{2}\).
# From Waste to Strength: Applying Wastepaper, Fungi and Bacteria for Soil Stabilization
###### Abstract  
Bioccementation is a promising soil stabilization technology that relies on microbiologically induced calcite precipitation (MICP). The addition of wastewater was found to enhance the mechanical strength of bioecmented soil. This study examined the effects of incorporating wastewater into bioecmented soil, focusing on the use of the ureolytic bacterium _Bacillus licheniformis_ DSMZ 8782 and the yeast-like fungus _Scrystallization candidium candidium_ SC for soil stabilization. The optimal wastewater content was determined to be 2%, as it did not disrupt the uniform distribution of CaCO\({}_{3}\) and contributed to improved soil strength. The combination of bacteria and fungi significantly increased the unconfined compression strength of samples containing 2% wastewaterpaper (161.1 kPa) compared to untreated soil (61 kPa) and bacteria-only treatments (66.5 kPa), showing improvements of 2.6 and 2.4 times, respectively. Furthermore, we demonstrated that adding fungal biomass without wastepaper significantly improved the compressive strength, achieving a value of 236.6 kPa--nine times higher than untreated soil (26.4 kPa) and four times higher than soil treated with bacteria alone (60.6 kPa). This study identifies the optimal wastepaper content and highlights the potential of combining fungal and bacterial biomass for bioacementation in soil stabilization.  
MICP; wastewaterpaper; calcium carbonate; _Bacillus licheniformis; Scrystallization candidium candidium_
## 4 Implementation of the Proposed Algorithm
### Procedure for Extracting Watermark  
The watermark extracting diagram of this proposed algorithm is showed in Figure 3. The detailed extracting steps are described as follows:  
1. [label=Step 1]
2. Segment the watermarked audio signal \(A^{\prime}\) into \(M\) audio fragments \(A^{\prime}_{l}\) with the length of \(N\).
3. Perform \(r\)-level DWT on \(A^{\prime}_{l}\) to obtain the wavelet coefficients \(De^{\prime}(r)\).
4. Divide \(De^{\prime}(r)\) into \(De^{\prime}_{1}(r,\mathrm{j})\) and \(De^{\prime}_{2}(r,\mathrm{j})\) with the length of \(N/2^{r+1}\).
5. Perform DCT on \(De^{\prime}_{1}(r,\mathrm{j})\) and \(De^{\prime}_{2}(r,\mathrm{j})\) to obtain \(C^{\prime}_{1}(r,\mathrm{j})\) and \(C^{\prime}_{2}(r,\mathrm{j})\) respectively.
6. Calculate the average amplitudes of \(|C_{1}(r,\mathrm{j})|\) and \(|C_{2}(r,\mathrm{j})|\) to obtain \(M^{\prime}_{c1}\) and \(M^{\prime}_{\epsilon^{2}}\) according to Formulas (6) and (7).
7. If \(M^{\prime}_{c1}>M^{\prime}_{c2}\), the extracted binary information is '1', otherwise, it is '0'.
8. Repeat Step 2 to Step 6 until all binary watermarks are extracted.
9. Convert the extracted binary stream into binary image watermark.  
Figure 3: The watermark extracting diagram of this proposed algorithm.
# Relationship between Fault Level and System Strength in Future Renewable-Rich Power Grids
## 5 Results and Discussion  
This section analyses the aforementioned methods for fault level calculations based on the Thevenin impedance estimation with those obtained from both the steady-state fault simulation (i.e., IEC 60909 standards) and dynamic simulations (i.e., RMS simulation) at different penetration of PE-based RESs. The outcome of this comparison would contribute to a better understanding of the impact of the increased penetration of RESs on the validity of using the fault level as a scanning tool reflecting the steady-state system strength represented by the Thevenin impedance. In other words, we intend to identify the extent to which the fault level results of the measure-based Thevenin impedance methods match with those obtained from the simulations. If they match, the results would indicate that the fault level at high penetration of RESs can still be employed as a reliable tool to scan the system strength, otherwise, the fault level-based approach may fail to do so. The adjusted IEEE nine-bus test system [33] was modelled in DIgSILENT PowerFactory. To consider multiple scenarios of penetration of RESs, a capacity-based metric for representing the percentage of the installed RESs in the system is defined, as given in Equation (8):  
\[p^{\%}\ =\ \frac{RESs}{SGs+RESs}\times 100\% \tag{8}\]  
where \(p\%\) is the penetration level percentage. SGs and RESs are the MVA ratings of the installed SGs and RESs, respectively.  
Note that any RES penetration here is associated with SGs displacements (i.e., SGs decommissioning), hence the total installed generation capacity in the system is kept unchanged. The fault level is monitored at all load nodes (bus five, six, and eight), which represent three levels of system strength and Thevenin impedance seen from those nodes. Figure 4 shows the adjusted IEEE nine-bus test system which is modelled considering three different scenarios as follows:  
1. Scenario 1: base scenario with a total of 0% penetration of RESs.
2. Scenario 2: low penetration scenario with a total of 22% penetration of RESs (displacing SG-3 with PV system).
3. Scenario 3: high penetration scenario with a total of 56% penetration of RESs (displacing SGs-2 with type-four wind and SG-3 with PV system).  
Note that the total installed generation capacity equals to 567.5 MVA. The biggest generation unit at the G1, at slack bus one, is 247.5 MVA. The rest of the generation capacity  
Figure 4: Modified IEEE nine-bus test system with RESs: (**a**) 22% RES penetration scenario (**b**) 56% RES penetration scenario.  
is divided into two values of 192 MVA and 128 MVA for G2 and G3, respectively. On the other hand, the system has three loads with a total of 315 MW and 115 MVAr located at a transmission network of 230 kV, as shown in Table 1.
## 5 Conclusions  
This paper proposes an effective anomaly detection mechanism based on the deep neural network for the IoT network architecture that efficiently learns valuable complex patterns from the IoT network flows to classify traffic as benign and anomalous. The proposed methodology is tested on the newly available IoT-Botnet 2020 dataset. The experimental results demonstrated the proposed model superiority compared to other DL methods by exhibiting a detection accuracy of 99.01% with the false alarm rate of 3.9%, showing an improvement of 0.57-2.6% in terms of the model's accuracy, while at the same time reducing the FAR by 0.23-7.98%. It was also observed that the model showed a detection rate of 99.9% to detect anomalies and recorded a decrease in the detection rate by 3.8% for detecting the benign traffic, probably due to the imbalanced nature of the dataset. Results also show that the best numerical features in the range of 16-32 calculated using the MI will be the reasonable choice to reduce the model complexity with an almost negligible effect on its performance. In addition, the inclusion of the categorical features further improves detection accuracy by only utilizing the top five features.  
For future research, we will extend this work by implementing our solution for the multiclass classification scenarios to find out the exact nature of the detected anomaly. In future work, we will test the proposed model's effectiveness in a real-time IoT environment.  
Conceptualization, Z.A., A.S.K., J.J.P.C.R. and K.N.; methodology, Z.A., A.S.K., K.N., I.H. and R.H.; validation, Z.A., A.S.K., K.N., I.H. and R.H.; writing--original draft preparation, M.R.H., K.N. and S.T.; writing--review and editing, Z.A., M.R.H., K.N. and S.T.; visualization, M.R.H., K.N. and S.T.; supervision, Z.A., A.S.K., J.J.P.C.R. and K.N.; funding acquisition, Z.A., A.S.K., K.N., I.H. and R.H. All authors have read and agreed to the published version of the manuscript.  
This research is fully funded by Universiti Malaysia Sarawak, Kota Samarahan, Malaysia under the grant number F08/PGRG/1908/2019. A.S.K. received the grant. The sponsors' website: [[https://www.riec.unimas.my](https://www.riec.unimas.my)]([https://www.riec.unimas.my](https://www.riec.unimas.my)) (accessed on 10 July 2021).  
**Acknowledgments:** The authors would like to thank the editors of _Applied Sciences_ and anonymous reviewers for their time and review of this manuscript and the valuable comments and suggestions on improving the paper from [PERSON] (IEEE Life member and former Director IEEE Region 10).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results  
### Metabolic Activity  
To assess cell viability, the resazurin reduction assay was chosen. The decrease in resazurin reduction is proportional to the decrease of the cell metabolism or cell number. After the 24 h exposure of the BJ cells to Les-4368, a 24.60%, 66.40%, and 72.34% decrease in the metabolic activity was noted at the three highest uM concentrations (from 10 to 100 uM), compared to the control (Figure 3A). In turn, after the 48 h exposure of the BJ cells to Les-4368, the 1 uM, 10 uM, 50 uM, and 100 uM concentrations caused a decrease in the metabolic activity by 17.98%, 43.07%, 73.35%, and 75.77%, respectively, compared to the control (Figure 3A).  
Figure 3: Effects of the increasing concentrations (1 nM–100 uM) of Les-4368 (**A**,**C**) and Les-4370 (**B**,**D**) on metabolic activity in the BJ cell line (**A**,**B**) and the CACO-2 cell line (**C**,**D**) after treatment for 24 h and 48 h. Data are expressed as means (\(n=6\)) with standard deviation (SD); data marked with *, **, and *** are statistically different at \(p<0.05\), \(p<0.01\), and \(p<0.001\), respectively, compared to the control.  
In the BJ cell line, after the 24 h exposure to Les-4370, only the 100 uM concentration caused a 17.73% decrease in the metabolic activity, compared to the control (Figure 3B). In turn, after the 48 h treatment of the tested cells with Les-4370, a similar decrease in the metabolic activity was observed in the concentration range of 50-100 uM (by 16.95 and 17.11%, in comparison to the control) (Figure 3B).  
After the 24 h exposure of the CACO-2 cells to Les-4368, the metabolic activity was reduced at the concentrations of 10 uM, 50 uM, and 100 uM by 23.39%, 41.94%, and 45.80%, respectively, compared to the control (Figure 3C). In turn, after the 48 h treatment of the cells with 10 uM, 50 uM, and 100 uM of Les-4368, the metabolic activity was decreased by 44.51%, 59.35%, and 58.38%, respectively, compared to the control (Figure 3C).  
The treatment of the CACO-2 cells with Les-4370 at the concentrations of 10 uM, 50 uM, and 100 uM for 24 h resulted in a decrease in the metabolic activity by 10.93%, 11.04%, and 13.84%, respectively, compared to the control (Figure 3D). After the 48 h exposure of the CACO-2 cells to Les-4370, an 11.36% decrease in this parameter was found in the 100 uM concentration variant, compared to the control (Figure 3D).  
Based on results of the resazurin reduction test, half of the maximal inhibitory concentration (IC\({}_{50}\)) was calculated (Table 1).
# Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
## 1 Introduction  
Traffic breakdown occurs when the speed of traffic rapidly decreases from free-flow to low speeds. When a breakdown occurs, vehicles are forced to rapidly decelerate, leading to delays and safety hazards [1]. This speed reduction can extend to kilometers [2]. It is a critical issue in urban areas where high volumes of vehicles often lead to breakdowns, cause traffic delays, and increase emissions. Thus, it is crucial for transportation engineers and traffic system operators to understand the causes of traffic breakdown and develop strategies to manage and mitigate it.  
Researchers have conducted studies and analysis on traffic breakdown events. By analyzing the traffic flow before breakdown events, researchers have constructed probabilistic models linking the traffic flow and the probability of the occurrence of a breakdown event. As the available traffic data becomes more abundant, researchers have discovered that traffic breakdown occurrences are related not only to traffic flow rate but also to other factors. However, previous probabilistic models usually considered only one variable. With the advancements in machine learning techniques in recent years, researchers have started using neural networks to predict traffic breakdown events and have achieved high accuracy in predicting their occurrences. Given the advantages of neural networks, the model can incorporate multiple variables considering temporal and spatially correlated data. Currently, no research considers the impact of traffic flow from ramps on breakdown occurrences using neural networks.
## 2 Materials and Methods
### Ankle Joint Position Sense Assessment  
Ankle proprioception was assessed using a digital inclinometer. The inter-rater reliability demonstrated excellent agreement, with ICC values ranging from 0.87 to 0.91. The standard error of measurement (SEM) for these tests was found to be between 0.06 and 0.07. The individuals were instructed to sit on a couch with their feet in a relaxed dangling posture, knees flexed to an angle of 90 degrees, and hips also flexed to the same degree. The primary inclinometer of the dual inclinometer was attached to the lateral aspect of the foot, while the secondary inclinometer was attached to the lateral aspect of the tibia at the shaft level and secured with a Velcro strap (Figure 1).  
The examiner performed the necessary calibration on the inclinometer in order to locate the neutral position (0 degrees of dorsiflexion and 0 degrees of plantar flexion). During the measurements of the ankle JPS, the subjects were told to close their eyes so that any potential influence from visual input could be eliminated. The examiner manipulated the participant's foot into a position of dorsiflexion or plantar flexion (at angles of 10 degrees and 15 degrees), starting from the neutral position. This position was then held for a period of five seconds. It was requested of the participants that they recall their place. After that, the examiner guided the foot back to its starting position (neutral position--0 degrees of dorsiflexion and 0 degrees of plantar flexion), and participants were given instructions to actively repeat the target angle based on their memories with as much precision as they could muster. The participants indicated when they reached the desired location by saying \"YES\", and the angular displacement was determined as the difference in degrees between the replicated angle and the target angle. The results of these trials were averaged for further examination, and the results of three consecutive trials were used for each of the directions that were investigated. The examiner provided instructions to the participants that were standardized and did not emphasize anything in particular to assist them in simply obtaining the target angle.  
Figure 1: Ankle joint position sense evaluation using a dual digital inclinometer.
# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 2 Proposed EMD-BiGRU Architecture  
### Empirical Mode Decomposition  
Empirical mode decomposition, as a data processing with adaptive capabilities, can decompose the signal based on the time-scale characteristics of the data itself [28]. Because the EMD does not need any pre-defined basis functions, it is the fundamental difference between EMD decomposition and wavelet decomposition building on a priori harmonic basis functions and wavelet basis functions [29]. In addition, EMD is well suited to deal with non-linear, non-smooth time series due to handling data series or signals. Thus, it can decompose a complex signal into a linear combination that includes the limited number of intrinsic mode functions (IMF) with frequencies ranging from high to low, where every IMF represents local features of the original signal at different time scales. Therefore, monitoring methods based on EMD acquire many mode information from monitoring data, which can improve the accuracy of predicting missing data. The flow of EMD algorithm is shown as follows:  
Step 1: All maximum and minimum points are found from the given monitoring data \(X(t)\). Then, by using cubic spline interpolation, the maximum values \(e_{max}(t)\) are formed into the upper envelope curve, and the minimum \(e_{min}(t)\) is formed into the lower envelope curve. Thus, the mean value is calculated from the upper and lower envelopes \(m(t)=\left(e_{max}(t)+e_{min}(t)\right)/2\).  
Step 2: The intermediate signal is calculated via obtained original signal and the polar envelope \(h(t)=X(t)-m(t)\).  
Step 3: To determine whether \(h(t)\) satisfies the two conditions of the IMF. If satisfied, \(h(t)\) is to be the first IMF, namely \(imf_{1}(t)\), else \(h(t)\) is as the original signal and return to Step 1.  
Step 4: Separating \(imf_{1}(t)\) from the original signal \(r(t)=X(t)-imf_{1}(t)\). When \(r(t)\) is a monotonic function and \(res(t)\) is a residual function, the iteration finish training.  
Finally, the original signal \(X(t)\) can be represented as several IMFs and a residual.  
\[X(t)=\sum_{i=1}^{n}imf_{i}(t)+res(t) \tag{1}\]  
where \(res(t)\) denotes the residual, \(n\) is the number of IMFs, \(X(t)\) is the original signal, and \(imf_{i}(t)\) denotes the \(i\)-th IMF.  
Since each component of the EMD decomposition contains only a portion of the features of the original sequence, IMF looks simpler than the original sequence. Figure 1 shows the original time series and different components with different sequence features. In addition, the original time series is selected from structure state #14 of a three-story building structure. The collected system provides a band-limited random excitation in the range of 20-150 Hz acting on the three-story structure. A detailed description of a three-story frame can be seen in Section 3.
## 2 Materials and Methods
### Experimental Setup  
For the acquisition of the vibration signals, a piezoelectric vibration transducer is used, which has a linear frequency range from 0.13 Hz to 22 kHz and a sensitivity of 100 mV/g. The sensor is an accelerometer. A signal acquisition at the bearings is not possible. Therefore, the sensor signal acquisition is carried out at the brake carrier of the wheel drive. The brake carrier is located at the first stage as shown in Figure 1. The sensor is mono-axial and orientated in x-direction. The IPEE signal of the sensor is logged through a data-acquisition system with a sampling rate of 100 kHz and a resolution of 24 bit.  
The data acquisition is carried out with a load-free test rig. In total, seven measurement campaigns are executed. In the first campaign, an undamaged test gearbox is used. Afterwards, the damaged components listed in Table 3 are separately mounted to the test gearbox. For each campaign, multiple constant rotational speeds \(n\) are applied, namely 700, 1200, 1700, 2100, 2700, 3400, 4200 rpm. Each rotational speed is measured five times with a duration of 20 s, respectively.
# Design and Implementation of a Smart Traffic Signal Control System for Smart City Applications
## 2 Related Works on ITS Applications
### Road Side Unit (RSU)  
[PERSON] et al. proposed a new traffic control system for communication policy management, communication management, remote management, and developed communication protocols among RSU, OBU, and the cloud [9]. [PERSON] et al. [10] proposed a scalable enhanced roadside unit, the main components of which are the speed adaptive traffic control system, the pollution adaptive traffic control system (PATC), weather information system, and master control center (MCC). It aims to improve traffic flow by opening or closing specific routes through PATC and use MCC to monitor weather, coordinate road maintenance services, and drive and maintain in bad weather.  
[PERSON] et al. designed smart traffic cones to communicate with vehicles by using RFID, they combined with image compression analysis algorithms to develop a new traffic cone vehicle accident detection and recognition system [11]. This paper uses wavelet transform image compression with vector quantization. [PERSON] et al. proposed a new mechanism by selecting the vehicles performing V2I communications based on the concept of road sectorization [12]. This mechanism can effectively reduce transmission overhead. In practice, the vehicle sends a message packet in a specific area. The message packet may be lost due to environmental factors, and the traffic signal controller does not receive the information of the vehicle. [PERSON] et al. [13] proposed an e-Notify system architecture system that uses sensors (such as airbag status, throttle status, brake status) and algorithms to determine if an accident has occurred. When the system detects a vehicle has a traffic accident, it broadcasts the message to the RSU. RSU receives traffic accident information and uploads information to the cloud platform via mobile communication.