esa-sceva/satcom-chunk-collection huggingface dataset:
## Basic Theory
### Bilateral Filter Algorithm  
As a kind of nonlinear spatial filter, BF is able to achieve uniform spatial filtering and reserve the edge information of an image. BF considers both the grey value of the point in the neighborhood scope, and the distance from center of geometric. Thus, the formula of grey value after filtering is presented as follows:  
\[h_{x}(x)=k^{-1}\int\int f(\xi)c(\xi-x)s(f(\xi)-f(x))d\xi, \tag{17}\]  
Figure 2: Fruit fly optimization algorithm.  
where \(k\) is the normalization coefficient, the expression is as follows:  
\[k(x)=\int\!\int\!c(\xi-x)s(f(\xi)-f(x))d\xi, \tag{18}\]  
in which \(h_{x}(\cdot)\) is the corresponding point of the grey value before filtering, and \(x\) is the value after filtering. \(c(\cdot)\) is the space similarity value of the center point with its neighborhood points, \(s(\cdot)\) is the gray similarity value of the center point with its neighborhood points.  
The functions \(c(\cdot)\) and \(s(\cdot)\) can be implemented using Gaussian function, which can be defined as follows:  
\[c(\xi-x)=e^{-\frac{1}{2}\left(\frac{\left\|\xi-x\right\|}{\sigma}\right)^{2}}, \tag{19}\]  
\[s(f(\xi)-f(x))=e^{-\frac{1}{2}\left(\frac{\left\|f(\xi)-f(x)\right\|}{\sigma} \right)^{2}}, \tag{20}\]
## 2 Materials and Methods
### Cognitive Performance
#### 2.5.1 Limited or No Negative Impacts on Cognitive Abilities
The study design was well-controlled, employing a balanced, randomized, and double-blind methodology to ensure accurate results [28]. The findings of the study revealed that there was no evidence to support the notion that EMFs from mobile phone signals had an effect on human cognition. The results did not demonstrate any significant influence of GSM 900 and WCDMA signals on attention or working memory in the participants. There are numerous review articles and surveys available on this topic. To our knowledge, this research represents the first attempt to establish a correlation between the expansion of telecommunication networks, their impact on human cognitive capacities, and the potential for smart environment implementation. Table 1 outlines the focus area of our work in comparison to previous related review articles. \begin{table}
\begin{tabular}{p{142.3 pt} p{227.6 pt}} \hline \hline
**Reference** & **Focus Area** \\ \hline
[29] ([PERSON] et al., 2011) & The review identifies the effects of RF-EMF exposure on cognitive performance measures in humans and highlights the methodological issues in the studies. \\ \hline
[30] ([PERSON] et al., 2017) & This review article focuses on recent neuroimaging and electroencephalography studies to present a more specific analysis of the effects of mobile phone EMF exposure on neurocognitive functions. \\ \hline
[31] ([PERSON] et al., 2022) & This systematic review aims to assess the impact of long-term radiofrequency exposure on cognitive function by analyzing human observational studies. The review categorizes outcomes into three main areas: global cognitive function, domain-specific cognitive function, and clinical diagnoses of cognitive impairment. \\ \hline
[32] ([PERSON] et al., 2020) & The paper reviews previous studies that investigated the relationship between exposure to radiofrequency electromagnetic field (RF-EMF) in the high-frequency band and cognitive function in children and adolescents. \\ \hline This work & This study categorizes RF-EMF exposure levels from UE, MP, and base stations and investigates their potential impact on human cognitive abilities. Additionally, it explores the association between RF-EMF exposure and the concept of smart environments, highlighting both opportunities and challenges. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Previous relevant review works.
# Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
## 1 Introduction
The SS module integrates signals of different spatiotemporal receptive fields and completes the spatiotemporal sensitivity filtering of signals, achieving a physiological signal feature representation with more semantic information. * We designed a multi-level spatiotemporal context fusion (MLSC) module consisting of three spatiotemporal context fusion (SC) modules to better achieve the effective fusion of infrared and visible light multimodal information. The MLSC module can effectively fuse spatiotemporal context information in multiple semantic spaces, effectively compensating for the information loss problem of traditional fusion methods. * We conducted a series of experiments on the VIPL-HR, MR-NIRP, and UBFC-rPPG datasets, demonstrating the superior performance of our method across these three datasets. The organization of the remainder of this paper is outlined as follows: Section 2 provides a review of the relevant literature on methodologies for heart rate estimation. Section 3 elaborates on the specifics of model formulation and architectural design. Section 4 presents the results obtained from the experiments conducted. Lastly, Section 5 concludes the study and considers potential avenues for future research.
## 4 Results and Discussion
#### Receiver Operating Characteristics (ROC) Curve  
To further analyse the results obtained in this study, ROC curve is used. It has been used widely as a tool for analysing overall test performance and for the comparison of the discriminating ability of clinical tests [12; 29]. It is based on the graphical curve plotting the relationship between true positive rate and false positive rate over threshold points of a test. The area under the ROC curve (AUC) summarises the overall performance estimate of the test, where AUC value below 0.5 indicates poor diagnostic test, AUC = 0.5 is considered same as random guesswork by an inexperience clinician, AUC > 0.5 indicates a good diagnostic test, and AUC = 1 stands for perfect diagnostic test.  
Figure 5 shows ROC curves of all the four classification algorithms using VGG16 features. All four classification algorithms produced impressive results, outperforming random guess with SVM achieving an almost perfect diagnostic test. The ROC curve depicted in Figure 6 was generated using VGG19 features, with SVM outperforming all the other classifiers.  
Figure 5: ROC curve and AUC using VGG16 features.  
Figure 6: ROC curve and AUC using VGG19 features.
# Energy Efficient Load-Balancing Mechanism in Integrated IoT-Fog-Cloud Environment
## 1 Introduction  
Numerous smart items and devices, including wearable technology, smartphones, and industrial utility parts, have been equipped with sensors in recent years to monitor and gather real-time physical data from their surroundings. For formulating and managing the sensor nodes, a wireless sensor network provides an efficient framework. Due to the heavy traffic generated by the Internet of Things (IoT) nodes, the network along with servers is overloaded and congested. Thereby, an intermediary layer known as the fog layer is introduced as a bridge amidst the IoT devices and cloud, which aids in achieving reduced response delays and time latency. To support the lower layers' heavy computational workloads, the cloud has introduced the intermediate fog layer to reduce the burden on heavily loaded lower layers.  
Numerous complications, which include heterogeneous types of devices, limited bandwidth, restricted computing resources, a wide range of client requirements, operational costs, etc., influence the efficiency of IoT networks [1]. Thereby, an optimal solution is required to address the above-mentioned issues in the network that will significantlyenhance the system's performance. Scheduling and allocation of resources are crucial for managing data centers that help maintain load balancing, proper utilization of resources, and cutting of carbon emissions [2]. Applications and sensor data are frequently sent and processed by data center computers. Due to the IoT applications' ever-growing resource requirements, it is very difficult to implement them as there is an exponential increase in energy consumption. Subsequently, the performance of the computing node degrades as a result of the massive data transmission and movement of computing devices [3]. Fog computing shifts IoT application computation from cloud platforms to the network's edge. Various applications of the cloud and IoT, such as healthcare, smart transportation, smart cities, smart homes, smart logistics, environmental monitoring, video surveillance, etc., are implemented to automate daily life processes [4]. Owing to the massive amount of data generated with fast velocity, a fog-cloud environment was needed to handle the computing resources efficiently.  
In [5], fog computing is compared with other computing technologies, as represented in Figure 1. Fog computing is a distributed and emerging technology primarily focused on decreasing the latency and data transmission costs to the remote cloud.
## 5 Implementation
### Data  
Data is stored using Google Protocol Buffers [57]. On their website, they are labelled a \"language-neutral, platform-neutral extensible mechanism for serializing structured data\". As IoT devices most commonly provide structured data, i.e., a value, a unit, and additional information, such as boundaries, they are easily encoded as Protocol Buffer elements. An advantage of Protocol Buffers is their platform- and software-independence. Currently, the SBT framework is used for embedded devices that are implemented using C and C++. However, the same data can be used in apps on mobile applications or in desktop tools. Furthermore, Protocol Buffer provide a small fingerprint regarding data size which is especially useful in low-latency communications. An exemplary description of a data point can be found in Listing 1.  
```
messageData{ requiredname=1; optionalunit=2; requiredvalue=3; }
```  
Listing 1: Exemplary Protocol Buffer definition.  
These definitions are created once for every data point of interest that is to be integrated into and used inside the IoT environment. In the context of the SBT project, data point definitions were created to generalise the data embedded devices are working on. These data points are consequently used and stored in the IoT platform. In the course of the project, relevant data points were defined to describe common sensor and actuator values, such as temperature, brightness, or current. These data points were shared between all participating devices as well as the IoT platform.
## 3 Established Connection Phase
### Remark--Protocol Summary and Efficiency
Note that \(2T_{prop}\) is a function of the communication distance and may be very long. For example, in a 700 m link, the waiting time is about 1 s. In such a scenario, the data transmission time is calculated as:  
\[T_{N_{b}}^{(estimation-only)}=\frac{N_{b}}{R}+\frac{N_{b}}{RT_{f}}(T_{s}+2T_{ prop}) \tag{24}\]where the second term accounts for the time slot and propagation delay spent in transmitting the pilot symbols for channel estimation. So, based on Equations (21)-(24), we have that:  
\[\eta^{(estimation-only)}=\frac{1}{1+\frac{T_{s}+2T_{psp}}{T_{f}}} \tag{25}\]  
represents the spectral efficiency for the case of estimation performed at each frame. Specifically, it can be appreciated from Equation (25) that, in this case, spectral efficiency did not scale with \(N_{b}\), thus, meaning that it was independent of the volume of data to be transmitted. Moreover, as expected, \(\eta^{(estimation-only)}\) tended to zero when the propagation delay between the communicating nodes increased. So, we can conclude that the proposed mechanism, based on channel estimation and prediction results, is more convenient, in terms of efficiency, than a conventional approach, based only on the periodic channel estimation. In order to summarize the whole mechanism, the flowchart reported in Figure 2 describes all the functional steps to be performed in the proposed communication framework. It is worth noting that, after a very large number of frames, referred as \(Q_{max}\), where the communication is ongoing, it is reasonable to expect the channel to change significantly. In this case, it would be preferable to perform a complete refresh of interference and channel statistics by re-initializing the transmission with a new CS stage. Otherwise, as shown in Figure 2, a shorter acquisition and estimation during EC is sufficient to drive the Kalman filtering-based channel prediction.
# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
###### Abstract  
Computed tomography scans are among the most used medical imaging modalities. With increased popularity and usage, the need for maintenance also increases. In this work, the problem is tackled using machine learning methods to create a predictive maintenance system for the classification of faulty X-ray tubes. Data for 137 different CT machines were collected, with 128 deemed to fulfil the quality criteria of the study. Of these, 66 have had X-ray tubes subsequently replaced. Afterwards, auto-regressive model coefficients and wavelet coefficients, as standard features in the area, are extracted. For classification, a set of different classical machine learning approaches is used alongside two different architectures of neural networks--1D VGG-style CNN and LSTM RNN. In total, seven different machine learning models are investigated. The best-performing model proved to be an LSTM trained on trimmed and normalised input data, with an accuracy of 87% and a recall of 100% for the faulty class. The developed model has the potential to maximise the uptime of CT machines and help mitigate the adverse effects of machine breakdowns.  
Keywords:LSTM; predictive maintenance; X-ray tube; 1D CNN; CT machine +
Footnote †: journal: Applied sciences
## 2 Potential Application Areas of CR-WSNs
### Military and Public Security Applications  
Conventional WSNs are used in many military and public security applications, such as: (a) chemical biological radiological and nuclear (CBRN) attack detection and investigation; (b) command control; (c) gather the information of battle damage evaluation; (d) battlefield surveillance; (e) intelligence assistant (f) targeting, _etc._ In the battlefield or in disputed regions, an adversary may send jamming signals to disturb radio communication channels [34; 35]. In such situations, because CR-WSs can handoff frequencies over a wide range, CR-WSNs can use different frequency bands, thereby avoiding the frequency band with a jamming signal. In addition, some military applications require a large bandwidth, minimum channel access and communication delays. For such applications, CR-WSNs can be a better option.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 4 Software Implementation
### An Example
#### 4.2.1 Applying the Improved Algorithm Directly to Encryption and Decryption  
Assuming the plaintext \(m=12,345\), the steps are as follows:  
1. [PERSON] selects the key seed \(s_{B}\) to calculate \(n_{B1}=340\) and \(n_{B2}=860\), thus obtaining \(x_{nB}=16,931,337\), and passes \(x_{nB}\) to [PERSON]; Similarly, [PERSON] selects the key seed \(s_{A}\) to calculate \(n_{A1}=267\) and \(n_{A2}=733\), thus obtaining \(x_{nA}=123,490,595\), and passes \(x_{nA}\) to [PERSON];
2. Encryption: \(c=x_{nA+nB}.m=63,457,287\times 12,345=783,380,208,015\). Then, [PERSON] passes \(x_{nA}\) and the ciphertext \(c\) to [PERSON];
3. Decrypt: [PERSON] calculates \(n_{B1}=340\) and \(n_{B2}=860\) based on his private key \(s_{B}\), and computes \(x_{nB+nA}=63,457,287\). Using this value, he recovers the plaintext \(m\) as \(m=c/x_{nB+nA}=783,380,208,015/63,457,287=12,345\).
## 4 Discussion  
The 3 DReshaper software was used to analyze the accuracy of the points between TLS and SfM. Figure 15 shows a comparison between both models analyzed, where 96% of the points presented an error in the range of \(+\)3 mm to \(-\)5 mm (see Figure 15). The other 3.8% of the points varied within a range of \(-\)5 mm and \(-\)17 mm. These results are not a priori relevant and need to be compared with more precise data, such as the GCP taken with a total station.  
Figure 14: SfM model of the selected area with GCPs represented.  
The analysis of the SfM with the GCPs using 3 DReshaper is shown in Figure 16A. The results show that the points had an absolute error greater than 1 mm, and that in no cases did the error reach 2 mm. Considering the features of the total station used to take the control points, where the error range is \(\pm\)2 mm + 2 ppm in the determination of coordinates, it can be concluded that the errors are below those seen in the reference materials.  
By superimposing the GCPs on the high-definition mesh generated from the TLS points, it can be observed in Figure 16B that 88.5% of the points have an error between 5 mm and \(-\)0.7 mm, while only 2.39% are between \(-\)0.7 mm and \(-\)2 mm. In addition, 9.17% of the points were between 5 mm and 13 mm. In this section, it can be observed that these are the points of the marble sink. The cyan and red tones are where the largest range of errors (between 5 mm and 1 cm) have appreciated, and it is there where the biggest differences can be observed, likely because of the materials that compose the sum, such as marble. The green tones of the baseboard are due to the angle of incidence, which in the baseboard is different from the rest of the points of the mosaic.  
Table 4 summarizes the error ranges of each method regarding the control points. It can be observed that the technique that obtains the best results with respect to the GCPs is SfM photogrammetry. This does not mean that the TLS method does not meet the expectations of the GCPs, but that in the range of <2 mm, the percentage of points is higher. In addition, it was found that there is a significant percentage of points that exceed the threshold of \(\geq\)5 mm, in particular 9.17%, because they are points taken on the clean part of the sink. The increase in error in this area is due to the material from which the sink is composed, which is marble. It has been proven in the literature that the laser in materials such as marble, goes through part of the surface and generates erroneous information in the points measured [31]. Specifically, marble, being a porous material on its surface, causes the laser to pass through the surface, generating an error in the altimetry of the points.  
Figure 15: TLS vs. SfM.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 1 Introduction
The quantization of the laser gyroscope and the accelerometer may disturb the error structure of the original information and bring many transient errors for low maneuvers, which may cause difficulty in fault detection. [PERSON] [16] only introduced the low-pass filter concept and combined the generalized likelihood test (GLT) method for fault diagnosis. To date, no researchers have put forward a perfect filtering method and theoretically analyzed the FDI of redundant strapdown inertial measurement unit (RIMU). This paper developed the three-channel filters and conducted their theoretical implementation. The method can be utilized well in micro-grid applications such as faults in solar and wind systems [17; 18; 19; 20; 21]. The paper proposed the constraint conditions of their parameters and analyzed the performance of the weight of different ratios. Thus, this paper is organized as follows: Section 2 provides a theoretical background to the parity space approach, analyzes the existing problems and presents the constraint conditions of parameters of three-channel filters. Section 3 gives simulation results and their analysis. Section 4 presents the conclusions.
## 3 Results and Discussion
### The Effect of Expander and Compressor Efficiencies  
Figure 7 reveals variations in COP and supplied-air temperature with the expansion ratio for open-loop high-pressure and open-loop low-pressure cycles. It is apparent that the  
Figure 6: (**a**) Comparisons of COP and supplied-air temperature for different air refrigeration cycles; (**b**) p-H diagrams for each cycle at the optimal expansion ratio.  
COP of the open-loop high-pressure cycles is larger than that of the open-loop low-pressure cycles, where the COP of the CT-C is the largest when the expansion ratio is greater than 1.8. When the expansion ratio is greater than 1.9 or the supplied-air temperature is lower than 281 K, the COP of the C-CT is the largest in the open low-pressure cycles. As the efficiencies of the compressor and expander increase, the COP of the open-loop cycles increases a lot, and the optimal expansion ratio decreases. The optimal expansion ratios of the TC cycle (85% and 80%), TC-C cycle (85% and 80%) and C-TC cycle (85% and 80%) are 1.96, 2.25 and 2.31, respectively, with maximum COP values of 0.974, 0.997 and 0.979, respectively.  
In the open high-pressure cycles, when the expansion ratio is greater than 1.6 or the supplied temperature is lower than 286 K, the COP of the CT-C cycle is the largest. The optimal expansion ratios of the CT cycle (85% and 80%), C-CT cycle (85% and 80%) and CT-C cycle (85% and 80%) are 2.04, 2.41 and 2.35, respectively, and the corresponding maximum COPs are 0.997, 1.055 and 1.071, respectively. When the supplied-air temperature required is greater than 283.15 K, the CT-C cycle has the maximum COP of 0.969, while the TC cycle has almost the same COP (0.967) but a simpler system arrangement. When the supplied-air temperature is greater than 273.16 K, the CT-C cycle has the maximum COP of 1.069.
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
###### Abstract  
In this study, we estimated drifter tracking over seawater using machine learning and evolutionary search techniques. The parameters used for the prediction are the hourly position of the drifter, the wind velocity, and the flow velocity of each drifter position. Our prediction model was constructed through cross-validation. Trajectories were affected by wind velocity and flow velocity from the starting points of drifters. Mean absolute error (MAE) and normalized cumulative Lagrangian separation (NCLS) were used to evaluate various prediction models. Radial basis function network showed the lowest MAE of 0.0556, an improvement of 35.20% over the numerical model MOHID. Long short-term memory showed the highest NCLS of 0.8762, an improvement of 6.24% over MOHID.  
drafted: 28 September 2020; 10 November 2020; 16 November 2020
## 2 Tissue-Engineered Scaffolds with GBM and Neural Tissues
### Conduits for Nerve Guide

Table 2: Studies related to the nerve guidance conduit development based on graphene-based materials. Keywords: BM-MSCs, bone marrow-derived mesenchymal stem cells; SC, stem cell; NR, nerve regeneration; CNS, central nervous system; PNS, peripheral nerve system; SCI, spinal cord injury; SDR, Sprague-Dawley rat; PNR, peripheral nerve regeneration; CPM, cell proliferation and migration; RGD, arginylglycylzymatic acid; PCL, poly(\(\varepsilon\)-caprolactone), PVDF, polyvinylidene fluoride; PLA, polylactic acid; ApF, Antheraea pernyi silk fibroin; NI, not investigated; \(\downarrow\), lower or decrease, \(\uparrow\), higher or enhance; \(\Rightarrow\), result in; and \(+\), by addition. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multicolumn{1}{c}{**Biomaterial(s)**} & \multicolumn{1}{c}{**GBMs Percentage**} & \multicolumn{1}{c}{**Target and Cell Type**} & \multicolumn{1}{c}{**Animal Model**} & \multicolumn{1}{c}{**Outcomes**} & \multicolumn{1}{c}{**Year, Ref**} \\ \hline \multirow{9}{*}{**Cryogel**} & \multirow{9}{*}{Amino-functionalized G/collagen} & \multirow{9}{*}{0.1\%, 0.5\%, and 1\% \(w/v\)} & \multirow{9}{*}{SCI} & \multirow{9}{*}{Organotypic spinal explant} & \multirow{9}{*}{Outcomes \(\Rightarrow\) SC and NR \(\uparrow\) ATP secretion (spinal cord} & \multirow{9}{*}{2021} \\  & & & from SDR) & & & Optimum \%: 0.5\% \\  & & & & Conductivity: 3.8 \(\times\) & \\