esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 4 Results and Discussion  
### Experimental Environment and Data Set  
The hardware platform is an Intel (R) Core (TM), i7-8550U CPU s 1.80 HZ, windows 10, 8G memory. The experimental computing environment is Python 3.5, TensorFlow 1.7.0 and Keras framework.  
Taxi trajectory data came from Wuchang District of Wuhan, Hubei Province, from 13 February 2014 to 19 February 2014, 6,705,086 taxi trajectory data were excluded in total by abnormal data cleaning, covering working days, holidays and special holidays (Valentine's Day), which are typically representative and stable [38]. To reflect real-life situations better, we divided the test set and training set into two parts according to date. We divided the total 70-day trajectory data by date. The training set contains 60-day trajectory data, and the test set contains 10-day trajectory data.  
Wuchang District's geographical location is 114\({}^{\circ}\)14\({}^{\prime}\) E-114\({}^{\circ}\)30\({}^{\prime}\) E, 30\({}^{\circ}\)32\({}^{\prime}\) N-30\({}^{\circ}\)37\({}^{\prime}\) N, divided into 84,048 grids according to 50 m \(\times\) 50 m units using ArcGIS10.3 [26]. There are 34,195 POI data in Wuchang. There are 6,705,086 trace data after the meshing of the permeation feature, of which 5,705,086 data are selected as training data, of which 1,000,000 data are used as test data. The test data is used as a sample data input for the trained BP neural network. The system is applied to different temporal periods and functional area taxi forecast, because the mean absolute error (MAE) intuitively reflects the deviation degree between the experimental prediction results and the actual value, so MAE evaluates the experiment results. The evaluation formula is as follows:  
\[MAE=\frac{1}{N}{\sum\limits_{i=1}^{N}}\big{|}y_{i}-y_{i}^{\prime}\big{|} \tag{3}\]  
\(N\) represents the number of experimental results, \(y_{i}\) indicates the actual time a given passenger waits for the first empty car, and \(y_{i}^{\prime}\) indicates the prediction time for that passenger to wait until the first empty car.
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
## 4 Numerical Simulation
### All the Satellites Are Scheduled to Serve for Multiple Targets (ASMT)  
For simplicity, consider the case where multiple users are evenly located over the equator, as shown in Figure 8. In this case, the position of the \(k\)th user can be described as (_Lon,Lat,h_) in the WGS-84 reference system; where \(Lon=360^{\circ}\ k/N\), \(k=0,1,...,N-1\) denotes the longitude, \(Lat=0\) denotes the latitude, \(h\) is the altitude, the main value is \(Lon\in[-180^{\circ},\ 180^{\circ}]\), and the user number \(N\in[2,24]\).  
The problem of determining which satellite will be assigned to which user is called the satellite selection strategy. In this paper, we used a minimum distance criterion that a satellite is prior to establishing an ISL with its nearest visible user. The distance between satellite \(m\) and user \(n\) is  
\[d_{mn}=\sqrt{\left(x_{sm}-x_{un}\right)^{2}+\left(y_{sm}-y_{un}\right)^{2}+ \left(z_{sm}-z_{un}\right)^{2}}\ \,m\in[1,24]\,\ n\in[2,N] \tag{4}\]  
\(Link(m,n)\) represents the link of satellite \(m\) and user \(n\), thus \(Link(m,n)=min(d_{mn})\).  
The availability as a function of altitude and number of users is shown in Figure 9, where the average availability of the worst user during the simulation is based on the minimum distance criterion. As seen in the figure, the number of visible satellites is inversely proportional to the number of users because of the average function of multiple users.  
Figure 8: Satellite selection strategy of multiple users.  
Figure 7: Availability as a function of altitude (1/8): (a) number and (b) probability.  
Figure 10a,b illustrate the probability of at least single-fold availability, and at least four-fold availability, respectively. In general, the availability can be considered strong if the probability is more than 95%. Strong single-fold availability can be achieved based on the conditions that there are fewer than 17 users and the altitudes are lower than 15,000 km, or there are fewer than eight users and the altitudes are lower than 72,000 km. The conditions to achieve four-fold coverage require less than four users or altitudes that are lower than 15,000 km if the users are five.
# Firewall Best Practices for Securing Smart Healthcare Environment: A Review
## 6 Firewall Best Practices for Smart Healthcare Environment  
The interconnectivity of a smart healthcare environment provides a seamless connection across multiple entities where a patient can get engage with doctors and other paramedics to better manage their care. Effective security measures can protect critical data and patient's privacy. Continual change in the healthcare environment influences the use of security equipment, especially the use of firewalls. Moreover, reliable, intelligent, and secure monitoring and management system reduces the impact of threat and risk to the smart healthcare environment. Firewalls not only protect smart healthcare environments from malware and viruses, but also defend against phishing and other cyber-security threats. Network firewalls filter the traffic between the secure network and the internet. Similarly, host-based firewalls run on the server, which control the network traffic. Moreover, host-based firewalls are suitable if patients are connecting through a remote network. Cloud-based firewalls are an important addition to the existing firewalls and provide more benefits in terms of security, more specifically, in securing the cloud-servers [71].  
Smart healthcare environments require special security considerations due to the patient's data and other information. In addition, the various interconnected medical devices are often overlooked where hackers and adversaries can gain unauthorized access.  
\begin{table}
\begin{tabular}{l l} \hline
**Firewall Vulnerability** & **Firewall Types Commonly Impacted** \\ \hline Authorization error & Application firewalls \\ \hline Code execution & Application firewalls \\ \hline Spurious entity & Any \\ \hline Incorrect entity & Any \\ \hline Validation error & Packet filtering \\ \hline Domain error & Packet filtering \\ \hline Target resource access & Packet filtering, Guard, Personal firewall \\ \hline Weak or not correct design error & Any \\ \hline Serialization error & Packet filtering \\ \hline Alias error & Packet filtering \\ \hline Other major logic error & Any \\ \hline Target resource change & Packet filtering, Guard, Personal firewall \\ \hline Misplaced entity & Any \\ \hline Missing entity & Any \\ \hline Boundary value error & Potentially all firewall categories \\ \hline Denial-of-Service (DDoS) & Application firewalls, Packet filtering but \\  & potentially any firewall \\ \hline \end{tabular}
\end{table}
Table 2: List of most known firewall vulnerabilities and targeted firewall type.  
Firewalls are one of the most basic building blocks of securing the smart healthcare environment. Solid and properly configured firewalls repel the attacks and act as a foundation for a smart healthcare environment [72].
# Machine Learning Applications in Surface Transportation Systems: A Literature Review
## 3 Background
### Machine Learning
#### 3.1.2 Neural Networks and Deep Learning Algorithms  
Inspired by the human brain's neural structure, the NN and DL algorithms are based on multi-layered perceptrons. These algorithms contain an input layer, an output layer, and some hidden layers consisting of neurons or perceptrons. These layers are connected through weighted edges, and the training process is based on backpropagation. The input can be any real-world digitized data, such as an image, voice, or text. The input data establish proper edge weights and perceptron activation to improve the output accuracy by matching trained data. Neural network learning algorithms include supervised, unsupervised, reinforcement, clustering, regression, and classification strategies. The only difference between the NN and DL is the number of hidden layers (Figure 4). A DL algorithm can contain several hidden layers, each of which helps to extract a specific feature from the input data.  
Figure 4: Neural network and deep learning model format.  
Recurrent neural networks are special NNs that allow the model to receive a series as input and another as output. This type of NN is suitable for time-series prediction. Feedforward neural networks (FNNs) send the data in one direction from input to output. Additionally, RNNs have an internal circular structure (loop feedback) in which the output data of one layer can be fed into the same layer again as feedback. Therefore, the output at state t depends on the input data at state t and the network status at state t-1. This property makes RNNs suitable for temporal prediction. Convolutional neural networks (CNNs), another category of NNs, can extract features from a multidimensional graph. These networks apply several convolutional and pooling layers to extract the input graph features. This capability makes CNNs suitable for image and video recognition.
## 3 Results
### Impact of MSCs on Expression of Markers Involved in Modulating the Immune Response
It suppresses the production of tumor necrosis factor beta (TNF-\(\beta\)) and other pro-inflammatory factors. In the _Poly (I:C)_ treated group cells, increased IL-10 expression after co-cultivation was observed, compared to CMSCs that were not activated. The overall results of the CMSCs phenotype after co-cultivation with lymphocytes are as follows. _Poly (I:C)_ CMSCs stimulated had positive expression of CD90, CD44 and CD54, less positive IL-10 and negative of TLR4, and TLR3. Non-stimulated CMSCs had positive expression of CD90, less positive of CD44 and negative of CD54, TLR4, TLR3, and IL-10. The difference in the sample of activated _Poly (I:C)_ CMSCs was mainly the expression in the markers CD54 and IL-10. The expression of TLR3 and TLR4 receptors, which are associated with the pathogens-associated molecular pattern (PAMP) and damage-associated molecular pattern (DAMP) was monitored. Upon TLR activation, CMSCs polarize and trigger several intracellular cascades that lead to the production and release of multiple cytokines. The change in expression in activated CMSCs was observed after the fourth passage, after _Poly (I:C)_ stimulation and co-culture with active PHA lymphocytes. The phenotype and expression of the markers in the CMSCs sample was determined. The expression of the marker CD90 was also monitored. CD90 is a hallmark of MSCs and is involved in cell adhesion and migration. We also monitored the expression dynamics of the marker CD44, a receptor for hyaluronic acid, that also participates in intercellular interactions and interactions with collagen molecules. The dynamics of CD54 expression was monitored after co-culture with lymphocytes. CD54 is an immunoglobulin group adhesive molecule. It is a transmembrane protein with an extracellular domain. CD54 is a ligand for immune-associated ligands. CD54 is a binding site for antigen associated with leukocyte function (LFA-1). The expression of IL-10 was monitored. It acts as an anti-inflammatory inhibitor of pro-inflammatory cytokines such as TNF-alpha (see Figure 8a,b). Figure 8: **(a)** Flow cytometry (FACS) analysis chorionic mesenchymal stem cells activated with polyinosine: polycytidylic acid CMSCs _Poly (I:C)_ co-culture with lymphocyte and (b) FACS analysis CMSCs non-activated CMSCs co-culture with lymphocyte. Representative flow cytometry analysis. In comparison, activated non-activated CMSCs by paired t-test showed statistically significant differences in the groups CD54 (%) (\(p\) = 0.0000), CD44 (%) (\(p\) = 0.0006) and IL-10 (%) (\(p\) = 0.0000); differences were not confirmed in the CD90 (%) (\(p\) = 0.8662), TLR3 (%) (\(p\) = 0.3752) and TLR4 (%) (\(p\) = 0.0925) groups (see Figure 9 and Table 1).
# Compact UHF Circularly Polarized Multi-Band Quadrifilar Antenna for CubeSat
## 6 Conclusions  
A multi-band right-hand circularly polarized antenna integrated into a three-unit CubeSat has been designed, simulated, and measured. A dual substrate solution based on FR4 material has been manufactured to validate the simulation model. After integration into a 3U CubeSat structure, the antenna provides an RHCP configuration with aperture beamwidths of 134 \({}^{\circ}\)C and 144 \({}^{\circ}\)C at 870 MHz and 920 MHz, respectively. The proposed antenna has been successfully tested in flight with a communication range greater than 1200 km from an LEO CubeSat to a low-power terrestrial receiver.  
Conceptualization, F.F.; Methodology, M.T.N.; Software, L.-H.T.; Validation, M.T.N.; Investigation, L.-H.T.; Data curation, M.T.N.; Writing--original draft, M.T.N.; Writing--review & editing, L.-H.T. and F.F.; Supervision, L.-H.T.; Project administration, F.F.; Funding acquisition, L.-H.T. and F.F. All authors have read and agreed to the published version of the manuscript.  
This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number C2023-26-01.  
Not applicable.  
Lacuna Space is acknowledged for the strong support and authorization to share in-flight results. We are extremely grateful to [PERSON], [PERSON], and [PERSON] for their guidance and advice during the design, measurement, and in-flight experiments.  
The authors declare no conflict of interest.
## Abstract  
Longitudinal-connected air suspension has been proven to have desirable dynamic load-sharing performances for multi-axle heavy vehicles. However, optimization approaches towards the improvement of comprehensive vehicle performance through the geometric design of longitudinal-connected air suspension have been considerably lacking. To address this, based on a 5-degrees-of-freedom nonlinear model of a three-axle semi-trailer with longitudinal air suspension, taking the changes of driving conditions (road roughness, speed, and load) into account, a height control strategy of the longitudinal-connected air suspension was proposed. Then, in view of the height of the air spring under various driving conditions, the support vector regression method was employed to fit the relationship models between the performance indices and the driving conditions, as well as the suspension geometric parameters (inside diameters of the air line and the connectors). Finally, to tackle the uncertainties of driving conditions in the optimization of suspension geometric parameters, a double-loop multi-objective particle swarm optimization algorithm (DL-MOPSO) was put forward based on the interval uncertainty theory. The simulation results indicate that compared with the longitudinal-connected air suspension using two traditional geometric parameters, the optimization ratios for dynamic load sharing coefficient and root-mean-square acceleration at various spring heights are between \(-1.04\%\) and \(20.75\%\), and \(1.44\%\) and \(35.1\%\), respectively. Therefore, based on the signals measured from the suspension height sensors, through integrated control of inflation/deflation valves of air suspensions, as well as the valves' inside connectors and air lines, the proposed DL-MOPSO algorithm can improve the comprehensive driving performance of the longitudinal-connected three-axle semi-trailer effectively, and in response to changes in driving conditions.  
longitudinal-connected air suspension; design parameters; interval uncertainty; double-loop multi-objective particle swarm optimization +
Footnote †: journal: Applied Soft
# An Effective Motion-Tracking Scheme for Machine-Learning Applications in Noisy Videos
## 5 Conclusions  
Finding an object of interest in an image and making an important judgment based on it has emerged as a very important research topic in recent years. Given this situation, we proposed a technique for tracking a moving object of interest in a noisy video to devise a technique that can build training data for machine learning. To track an object without a separate data set in such an environment, we designed an object detection and tracking algorithm based on a modified simplest color balance technique. The proposed method unifies the image information corresponding to the object to be detected through the SCB algorithm. In this paper, we adopted the idea of collectively minimizing values in a certain low range in the SCB algorithm. After applying the modified SCB algorithm to the image based on the idea, a binarization algorithm was applied to determine the exact object to be found. Finally, in order to track the object in the subsequent video, the label was displayed on the white bundle that was determined as the object in the frame unit. As a result, the detection accuracy of ambiguous objects was over 95%. The approach also reduced the tracking loss rate to 10%.  
Our study can be used to distinguish and track objects that users want, excluding various unnecessary objects and very small objects present in micro-fields, and this study can be used in various fields related to cell research.  
As a future work, we are planning to enhance the clarity of the input video itself by eliminating the unnecessary noise object. This might help improve the performance of object recognition in noisy videos.  
Methodology, H.-W.L.; validation, O.B.; investigation, J.L.; writing--original draft preparation, H.K.; supervision, C.-P.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MIST (Ministry of Science, ICT), Korea, under the National Program for Excellence in SW, and was supervised by the IITP (Institute of Information and Communications Technology Planning and Evaluation) in 2022 (2019-0-01834).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Characteristics of Nanoparticles
Figure 16 shows the comparison of Nu for the 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) and 0.1 wt% Fe\({}_{3}\)O\({}_{4}\)-MWCNT nanofluids. Similar to the convective heat transfer coefficient, the Nu was higher in the latter than in the former. As Re increased, Nu was also increased. The increase in Nu for the Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluid was due to the increase in the Brownian motion of MWCNT with high thermal conductivities, thermal and physical properties, and large surface area of the nanoparticles. In addition, it was confirmed that the heat transferability of the Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluid increased due to the effect of the magnetic field. In particular, when the magnetic field strengths were 0, 250, 500, and 750 G at Re = 1400, the Nu values of the 0.1 wt% Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluid were 12.4, 12.6, 12.7, and 12.8, respectively, and the Nu values of the 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) nanofluid were 12.3, 12.4, 12.5, and 12.7, respectively. The Nu of the 0.1 wt% Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluid increased by 1.20% on average compared with that of the 0.2 wt% Fe\({}_{3}\)O\({}_{4}\) nanofluid. Similar to the convective heat transfer coefficient changes, the Nu is not increased significantly because the convective heat transfer coefficient increased due to the increase in Re at the same thermal conductivity. Few studies have compared the convective heat transfer characteristics of Fe\({}_{3}\)O\({}_{4}\) nanofluids and Fe\({}_{3}\)O\({}_{4}\)-MWCNT hybrid nanofluids. However, similar trends have been reported in previous studies [31; 32]. Due to the slight improvement in the convective heat transfer coefficient, Nu also exhibited a slight improvement rate. Figure 7:5 Nusselt number comparison of different nanofluids.
## 4 Simulation Results and Discussion
### Superior Optimization Method
#### 4.2.1 System Power and Profit  
Figures 8 and 9 show the power curve and daily profit for different energies, respectively. The optimal scheduling of the EH has been carried out by satisfying the operation and hub constraints using the IFLA algorithm. According to Figure 8, the active power of all EH equipment is not positive during the early hours of the program; i.e., 1:00 to 7:00. These conditions have been created because EVs are charging and energy is stored due to the low price of electricity in this period. On the other hand, the demand for hub equipment has been answered by CHP due to the low price of gas in the hours of 1:00 to 4:00. On the other hand, Figure 8 shows that due to the high price of electricity during the hours of 8:00 to 24:00, the electricity required by the networks is supplied by CHP equipment, renewable energy sources, storage systems, and electric vehicles during these hours. On the other hand, due to the higher price of heating energy compared to gas, the maximum allowed heating power has been delivered to the heating network during the full day. Therefore, from 1:00 to 4:00, power received in the hub equipment from the upstream network exceeds that of CHP, photovoltaic, and wind energy sources, and the power of the hub equipment is negative during these hours. Furthermore, in the hours of 8:00 to 24:00, the power of the hub equipment is positive. Therefore, according to the mentioned cases, energy management has been carried out to maximize the profit of the system.  
The daily changes in the profit hub are presented in Figure 9. The results show that the profit of the system in the day-ahead market of different networks is affected by the price of energy. According to Figure 9, the benefit from heating is variable due to the variable price of heating energy in different periods. On the other hand, based on Figure 9, it is observed that the profit of the electricity market is negative in the hours 1:00 to 7:00 due to energy storage by the EVs, and it is positive in the rest of the hours. Furthermore, the profit earned in the gas market is not positive because of receiving gas energy from CHP and steam boilers, as well as the higher profit in the heating market compared to the gas market at all hours. The profit from the forward market includes the set of profits of all hubs in different forward markets in such a way that the negative profit means the positive cost.