esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Small GTPases in Plasmodium May Substitute Canonical G-Proteins  
Instead of heterotrimeric G-proteins, a small set of Rab GTPases and a Ras GTPase was identified in _Plasmodium_. Rab GTPases are involved in the regulation of targeting and fusion of transport vesicles within the secretory and endocytic pathways [27] ([PERSON] et al., 2008). Most of the apicomplexan proteins have predicted signal peptides which direct them to the cytosol of the red blood cell (RBC), to the apicoplast and Maurer's Clefs. In contrast, signal peptides of proteins that are directed to secretory organelles like rhoptries, micronemes and dense granules during erythrocyte invasion remain to be identified.  
Rabs are associated with the cytoplasmic surface of a particular membrane compartment in the cell. However, they can also exist as a soluble protein in the cytoplasm like the GDP-bound form of Rab which is soluble in the cytoplasm as a complex with guanine nucleotide dissociation inhibitors (GDI). Rabs become associated with membranes when a GDI displacement factor exposes a preenyl group covalently linked to the C-terminal end of the Rab protein which then inserts into the membrane. Once the GDI is released, a guanine nucleotide exchange factor (GEF) on the membrane activates the Rab by exchanging GDP to GTP. Activated Rabs interact with a range of different partners. In addition to vesicle docking, Rabs are also involved in vesicle formation and movement.  
There are 11 different Rab isoforms in _Plasmodium_[28]. Two small, monomeric GTPases Rab1A proteins i.e., PfRab1A and PfRab1B were further characterized in _Plasmodium_ which regulate vesicular traffic from the endoplasmatic reticulum (ER) to the rhoptries in late-phase schizonts [29]. While PfRab1A is a paralogue of Rab1 found in the chromalveolates, a phylogenetic group within the Apicomplexa, the PfRab1B is more closely related to other organisms.  
A unique myristoylated Rab5B GTPase from three occurring in _Plasmodium_ was identified that was targeted to the food vacuole of the parasite to import nutrients from the RBC for an abundant food supply from the hemoglobin of the host cell [30]. In conclusion, mostly Rab proteins which belong to the Ras superfamily were identified in _Plasmodium_ that regulate the budding and fusion of intracellular vesicles from donor to acceptor membranes.  
A recent screening for G-protein orthologues in _Plasmodium_ resulted in the identification of a non-canonical Ras-like GTPase (acronym PFG) [31]. PfG might represent a multi-enzyme complex (molecular weight 109 kDa) which is localized to the cytosol and has an EngA domain present in bacterial GTPases. PfG is highly divergent from any of the known G\(\alpha_{\mathrm{s}}\) human subunits and still has an unknown function.
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 6 Evaluation
### User Privacy against Other Users
#### 6.2.1. Requester and Agent Compare UIPs  
During the initial phase, a potential Requester and an Agent perform a comparison of their respective interest profiles (UIPs). Recall that the UIP is first separated into groups of entries, and the Agent discloses the group names in descending order based on the number of marked interests he/she has in each group. At this point, the Requester can infer that the Agent has more interests in a high-ranking group than a low-ranked one, but he/she is still completely oblivious to what these interests are or even how many they are. The Agent can assume that the Requester's selection will most likely be from a high-ranking group. Other than this trivial knowledge, the users do not learn any more information about each other's interests, and we can therefore conclude that privacy is preserved.
# Smart Roads for Autonomous Accident Detection and Warnings
## 5 Experiments
### Finding Threshold for the Microphone Sensor Module  
The microphone sensor module is attached to the Arduino Uno board, as shown in the wiring diagram from uploading a sketch (a program) to the Arduino board through the Arduino IDE. The sketch contains the basic pin configuration of the Arduino board and the serial print command for showing the incoming values on the serial monitor or serial plotter. The serial monitor is an output screen that shows data on the monitor and the serial plotter shows the output in the form of a graph which can be easily understood. Figure 9 shows the microphone output of the serial plotter.  
When no sound is produced externally, the microphone senses only background noise. In Figure 9, background noise is shown before and after the accident's sound. When a sound is played through a loudspeaker, the amplitude increases. The normal background sound ranges from 518 to 532 ADC (calculated by taking the ratio of the analog signal's voltage to the reference voltage: ADC = (11.003 * dB) - 83.2073 [68]). However, when an accident sound is produced, the range suddenly changes from 480 to 555 ADC. If the range exceeds 532 ADC or is less than 518 ADC, there is a chance of an accident. To make it more precise, another experiment was completed by playing the sound of a passing vehicle into a microphone through a loudspeaker. Figure 10 shows the waveform for the sound of the passing vehicle on the serial plotter. The range of the output waveform varies from  
Figure 9: Microphone output on the serial plotter.  
512 to 534 ADC. Figure 10 shows that it varies from 519 to 531 ADC against the background sound. It was noted that there are very small differences in the amplitude of the waveform produced when only background noise is presented or from sound due to vehicles passing by, as shown in Figure 11. In one case, the amplitude of the waveform reached 540 ADC when an accident sound is produced. Various accident sounds are produced, and a similar effect provided loudness that is nearly the same. An accident can be detected from the loudness of sound.
# A Super-Resolution Reconstruction Driven Helmet Detection Workflow
## 2 Related Work
### Super-Resolution Reconstruction  
There have been a large number of attempts to improve the performance of super-resolution reconstruction as it is really a long story in the development of computer vision. The most widely used approaches are kinds of interpolation-based methods, such as bilinear interpolation or nearest-neighboring interpolation [12]. Since the process of interpolation always follows a fixed pattern to calculate the new-generated pixel values from existing ones in a low-resolution image, it is hard to adapt to an unknown image degrading protocol.  
Another traditional idea is to treat the SR problem as image reconstruction [13; 14]. Inspired by the learning method, recent super-resolution approaches directly learn the nonlinear relationship from the low-high-resolution images. Based on the learning process, it could be divided into supervised SR and unsupervised SR. The supervised SR requires aligned high- and low-high-resolution image pairs to train the CNN models to fit the mapping between images with different resolutions. [PERSON] et al. propose the super-resolution convolutional neural networks (SRCNN), which effectively improves the effect and speed of image SR reconstruction compared with the traditional image SR algorithms [15]. [PERSON] et al. propose a VDSR network, which increases the number of layers of CNN to 20 [16]. The algorithm combines residual structure and CNN with image SR reconstruction, and the image reconstruction effect has been significantly improved. [PERSON] et al. propose a multi-scale residual network (MSRN), which applies image multi-scale features to the residual structure to further improve the image reconstruction effect [17]. [PERSON] et al. propose a residual channel attention network RCAN, which applies a channel attention mechanism to the image super-resolution problem and achieves a better reconstruction effect than previous algorithms [18]. To apply these methods successfully, we need to prepare a large number of strictly aligned image pairs, which is not a simple task in practice. Thus, currently, simulated image pairs are used in research so as not to decrease its performance in real world applications. Unsupervised SR methods employ the GAN model and its variants to generate high-resolution images with the low-resolution input [19; 20; 21]. However, without paired training data, its accuracy is not as good as that of supervised methods.
# A Power-Line Communication System Governed by Loop Resonance for Photovoltaic Plant Monitoring
## 1 Introduction
However, in this case the DC power current of the string is forced to flow across the resonance coil and depending on its value the coil core can be pushed to saturation and the resonance condition will not be fulfilled. The solution to this core saturation problem will be one of the novelties presented in our work. Additionally, the authors showcased the importance of proper detection firmware, as this heightened tolerance of their PLC system to bit error rates (BER). In [13] the authors propose the integration of the PLC with dc-dc power optimizers (DCPO), where the communication signal is added in the power control loop of the DCPO in such a way that the data are modulated at its output and transmitted via the cascaded power line. AC Voltage sampling circuits are installed in DCPOs for reception. Ref. [14] used FSK as in [6] but with 110 kHz representing a logic one and 90 kHz a logic 0; capacitive coupling was used with an AC blocking coil. Lastly, in [15], PLC transmission was used to facilitate an integrated fault detection algorithm in the communication module which deciphers the specificity of the PV fault occurring at a 10 min interval along an 18 module PV array. In totality, all the studies highlighted the robust nature of PLC systems in their simplicity, efficiency, and cost. As such this paper will explore the implementation of the ring PLC configuration we proposed in [16] which takes advantage of resonance along the entire DC-PV line (loop resonance) for medium to high-frequency communication. With this topology the line essentially acts as a resonant loop antenna with high Q-factor characteristics for maximum power transfer between transmitter and receiver. This paper will evolve with the following sections: Section 2 Materials and Methods, Section 3 Results, and Section 4 Conclusions.
_Article_  
Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques  
[PERSON] 1,2,* 2 [PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2 Clek for  
Received: 3 July 2018; Accepted: 2 August 2018; Published: 4 August 2018  
**Abstract:** In this article, two techniques that are congruous with the principle of control theory are utilized to estimate the state of health (SOH) of real-life plug-in hybrid electric vehicles (PHEVs) accurately, which is of vital importance to battery management systems. The relation between the battery terminal voltage curve properties and the battery state of health is modelled via an adaptive neuron-fuzzy inference system and a group method of data handling. The comparison of the results demonstrates the capability of the proposed techniques for accurate SOH estimation. Moreover, the estimated results are compared with the direct actual measured SOH indicators using standard tests. The results indicate that the adaptive neuron-fuzzy inference system with fifteen rules based on a SOH estimator has better performances over the other technique, with a 1.5% maximum error in comparison to the experimental data.  
**Keywords:** state of health estimation; adaptive neuron-fuzzy inference system (ANFIS); group method of data handling (GMDH); artificial neural network (ANN); electric vehicles (EVs); capacity degradation; lithium-ion battery; time-delay input
## 3 Communication Scheme Based on CMPS
### Communication Scheme for Wireless Body Area Network
CS requires the sender and receiver to be identical, which is difficult in practical applications; especially for long-term device operation. With regard to CMPS, the channel transmitter and receiver can be the same or different, which avoids this problem at a basic level. (4) The transmission signal involves complex operations, such as the multiplication of complex numbers and taking derivatives. The ciphertext signal transmitted by each channel is a combination of five pieces of plain-text information and chaotic signal derivatives. The smartphone samples the sensor every minute. Even if a certain channel is intercepted, it is extremely hard to crack the plain-text in one minute, thereby increasing attack resistance and information security. (5) The dynamic characteristics of the time delay complex Chen system are more complicated than those of the time delay-free complex system, which can increase the secrecy of chaotic communication.
## 5 Simulation Experiment and Analysis
### Path Planning Analysis
#### 5.1.2 Multi-UAV Cooperative Search Trajectories Planning Based on KD-SAC Scenario 2
Figure 6: _Cont._  
Scenario 3:  
The initial information settings of the four UAVs in this scenario are presented in Table 6. Under event attribute \(\mathcal{A}\) and relation condition \(\mathcal{B}\) of Scenario 2, the UAVs execute actions based on the Markov maneuver model and autonomously select templates \(\chi_{1}\) (LiDAR sensor), \(\chi_{2}\) (underwater target detection sensor), and \(\chi_{3}\) (execute search Rule 1). Four distinct prediction points are assigned to UAVs according to Rule 1, resulting in a broader search area compared to Scenario 2. Figure 7 illustrates the path-planning outcomes for all four algorithms employed in this task scenario. It can be seen from Figure 7 that even if multiple UAVs plan trajectories at the same time, the KD-SAC algorithm can plan smoother trajectories, where the trajectory of the UAV4 is particularly smooth. The obstacle avoidance trajectory of UAV1 is smoother compared to Figure 6d, indicating that KD-SAC continuously adjusts the motion state of the UAV to enhance the search rule. This is due to our goal \(g\) that takes into account both the smoothness of the path and the threat posed by obstacles along the path. As the number of iterations increases, based on the deductive inference return visit mechanism, the KD-SAC fully learns and consistently identifies the optimal solution from the action strategy set to optimize the action template in the knowledge base. Figure 6: Comparison of optimal path planning generated by four algorithms in Scenario 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Start Point** &
\begin{tabular}{c} **Initial Relative** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} & **Velocity (km/h)** \\ \hline UAV1 & (100, 350, 100) & 0 & 90 \\ UAV2 & (250, 200, 100) & 0 & 90 \\ UAV3 & (200, 500, 100) & 180 & 90 \\ UAV4 & (400, 100, 100) & 180 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: UAV initial information under Scenario 3. Figure 7: _Cont_.
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 3 Nonlinear Mobility Model  
### Gauss-Markov Mobility Model  
A Gauss-Markov (GM) mobility model is a nonlinear and random model. It was first proposed in [14] and is motivated by the requirement for a closer to reality model in the sense that the UAV will accelerate, deaccelerate, and turn gradually. Thus, the movement (direction and speed) in GM is related to the previous movement through Gaussian equations using average direction and speed of the movement, as well as random Gaussian noise, besides a parameter that controls the degree of the dependency of the previous  
Figure 1: Sample paths under (**a**) random waypoint; (**b**) Gauss–Markov.  
movement. In that sense, GM is also considered as a temporal dependency model, as the time is divided into slots, and the movement at each time slot depends on the movement of the previous time slot. Thus, the movements are correlated [15]. Figure 1b shows a sample of a trajectory generated by the GM mobility model.  
The GM mobility model emulates the real environment for drone communications [16]. The GM was adopted for use in several recent FANET-related studies [16; 17; 18; 19; 20].  
GM works as follows. In the beginning, each drone will have a specific direction and speed. After that, the direction and speed will be changed at each time period. The new direction (\(\theta_{t}\)) and speed (\(v_{t}\)) will depend on the previous direction and speed and will be calculated as follows [21]:  
\[\theta_{t} =\alpha\theta_{t-1}+(1-\alpha)\mu_{\theta}+\sigma_{\theta}\sqrt{1- \alpha^{2}}w_{\theta_{t-1}} \tag{1}\] \[v_{t} =\alpha v_{t-1}+(1-\alpha)\mu_{\varphi}+\sigma_{\varphi}\sqrt{1- \alpha^{2}}w_{v_{t-1}}\]  
where \(t\) is the discrete unit of time; \(\alpha\) is the tuning parameter which adapt the required level of randomness; \(\theta_{t-1}\) and \(v_{t-1}\) are the previous direction and speed at time \(t-1\); \(\mu_{\theta}\) and \(\mu_{\varphi}\) are the mean of the direction and speed, respectively; \(\sigma_{\theta}\) and \(\sigma_{\varphi}\) are the standard deviation of the direction and speed, respectively; \(w_{\theta_{t-1}}\) and \(w_{v_{t-1}}\) are random variables from Gaussian distribution with mean equal 0 and unit variance.  
The \(x\) and \(y\) are then calculated based on the direction and speed as follows:  
\[x_{t} =x_{t-1}+v_{t-1}\cos\theta_{t-1} \tag{2}\] \[y_{t} =y_{t-1}+v_{t-1}\sin\theta_{t-1}\]  
where \((x_{t},y_{t})\) is the \(x\) and \(y\) coordinate for the node at \(t\)th time interval, and \((x_{t-1},y_{t-1})\) is the \(x\) and \(y\) coordinate for the node at \(t-1\) time interval.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 6 Discussion  
The collected data were compared in terms of the efficiency and speed of the described algorithms and their setup. Configurations with different video resolutions, distances, and visibilities were tested, whether due to different lighting, tilt angles, or image focus. It was found that virtually all conditions were better handled by the YOLO [8] algorithm. The algorithm surpassed the competing mask R-CNN [18] algorithm for both short and long-distance detection. Another output from the testing was the most optimal choice of video input resolution to create a so-called blob video, which served as an input to the selected YOLO object detection algorithm. The configuration with 1248 \(\times\) 1248 blob input resolution proved to be highly computationally efficient concerning speed and detection capabilities. The YOLO algorithm is widespread mainly due to its use in low-performance devices and, therefore, despite the loss of speed, it should also be efficient when running on the UAV device.  
Detection outputs proved the high accuracy and robustness of detection under given conditions, i.e., suitable distance, lighting, and video quality. The result yields a preference for a camera with a resolution of at least 1920 \(\times\) 1080 pixels with a horizontal field of view of 90 degrees and a height above the ground of up to 30 m. The algorithm is recommended for performing detection when there are no sudden or rapid changes in camera rotation.  
Testing on the estimations of the geo-positions of pedestrians was performed using data obtained from the campus of the Technical University in Kosice; their positions were mapped to the appropriate geographical coordinates. Data were then stored in the database. Subsequently, it was possible to display the output on the map and perform a cluster analysis. The detection and estimation of their positions were accurate, with a precision of up to 2 m. This confirms the functionality of the algorithm we proposed and implemented to convert the position from the image to the actual geographical position.  
The cluster analysis was available as a part of the experimental software solution. The algorithms exhibited the expected behavior and provided efficient visualization of data points, thermal maps, and map markers.  
Figure 10: Analysis of irregular cluster shapes using DBSCAN (**left**) and K-means (**right**).  
The proposed solution can quickly and robustly detect people from the camera and calculate the geographical locations of pedestrians from the image. The object detection algorithm YOLO [8] is proven to be highly efficient within a height of up to 30 m. The selected clustering algorithms (K-means, DBSCAN, and OPTICS) also performed as expected and were successfully applied to regularly and irregularly shaped data. Further improvements to the system can be made by improving the accuracy and robustness of object detection from higher distances and providing more useful metrics alongside cluster visualization.
# The Effect of Countermeasure Readability on Security Intentions
## 4 Methodology
### Formative Measurement Model Evaluation
#### 4.3.1 Variance Inflation Factor  
Before the significance of outer weights of the measurement model could be analyzed and relationships could be interpreted, the formative measurement model had to be assessed for collinearity issues. The first step in this process was detecting collinearity; that is, looking for high correlations between two formative indicators [61].  
High correlations were not expected, since PMT is well-tested and its constructs and their operationalizations (which output indicators/manifest variables) are distinct and well delineated. Moreover, operationalizations were based on other work, where they were shown to be valid [10, 12, 55]. Manifest variables are the basis of the measurement model and, therefore, SEM analysis as a whole. As a result, high collinearity between them would be problematic.  
To assess collinearity between formative indicators, the variance inflation factor was calculated, as recommended by [PERSON] et al. [68]. [PERSON] et al. [61] state that if VIF \(\geq\) 5, it is likely a collinearity issue is at play. The VIF values for the manifest variables did not surpass 4.671 and averaged at 2.062, safely failing to exceed the recommended threshold.