esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation  
In this Section, we provide various simulation experiments to demonstrate the effectiveness and superiority of the DHCO algorithm, in comparison--from multiple perspectives--with state-of-the-art WSN routing algorithms such as LEACH [11], HEED [10], GASONeC [21], and DCFR [15], that consist of the number of sensor nodes and the width of the sensing field. For the sake of acquiring a higher level of confidence, we conducted 600 experiments per each experimental scenario with randomly distributed sensor nodes, and calculated their statistical averages as the results. Evaluating criterions for the WSN longevity were the transmission rounds and percentages of live sensor nodes that had different definitions. The performance aspect of the percentage of live sensor nodes was the case of how many sensor nodes were alive at the transmission round \(t\); the more sensor nodes that were alive, the better the algorithm performed. Another performance aspect was given by the statistics for the transmission rounds of diverse experimental scenarios in WSNs; the more transmission rounds there were, the better the algorithm performed. More details are presented as follows.
# Comparative Analysis of Measurement Tools in the Cognex D900 Vision System
## 2 Background and Literature Review
### Image Recognition Process  
Image recognition in vision systems is a complex process that involves several essential stages to analyze and understand visual data. These stages ensure that the system can accurately identify and classify objects within an image, ultimately leading to informed decisions and actions. The stages include the following [21; 22; 23; 24]:  
* Image acquisition: The process begins with capturing an image using a vision camera. Depending on the application requirements, the camera might be a 2D, 3D, or linear type. This captured image serves as the raw input data for the system. Once the image is captured, it is converted into a digital format that can be processed by the system for further analysis. The quality and type of the camera used, as well as the lighting conditions, play a crucial role in the accuracy of the subsequent steps. * Preprocessing: After acquiring the image, the next step is preprocessing, which involves a series of operations designed to enhance the image quality and prepare it for further analysis. Preprocessing can include a range of techniques such as lighting correction to ensure consistent brightness, noise filtering to remove any unwanted artifacts, contrast enhancement to make important features stand out, and normalization to ensure uniformity across different images. The primary goal of preprocessing is to make feature detection easier and more reliable by refining the image to a more analyzable form. * Segmentation: Segmentation is a critical stage where the image is divided into different segments to isolate objects of interest from the background. This step is crucial for focusing on specific parts of the image that are relevant for recognition. Techniques used in segmentation can vary, including thresholding, which separates objects based on intensity levels; edge detection, which identifies object boundaries; and pixel classification, which categorizes pixels based on their characteristics. Effective segmentation is vital for the accuracy of the following stages. * Feature extraction: Once the image is segmented, the system proceeds to feature extraction, where it identifies and isolates key characteristics necessary for object recognition. These features can include shapes, textures, colors, sizes, or specific markers that are unique to the object being recognized. Feature extraction is a pivotal stage because the accuracy of object recognition heavily depends on how well the system can identify and analyze these features. * Classification: After extracting the relevant features, the system moves on to classification. Here, classification algorithms, often powered by machine learning or artificial intelligence, analyze the extracted features and assign the object to one of the predefined categories. This categorization is based on training data that the system has
## 3 System Architecture
### Channel Coding  
Channel coding is a significant part of numerous digital communication systems. It is also well known as a forward error control (FEC) scheme that can detect and reduce the bit errors in the digital system. Channel coding can be used in both the transmitter and receiver to increase the reliability of the system. Channel coding is used to encode on the transmitter side by adding extra bits to raw data before modulation. Otherwise, channel coding is used to decode on the receiver side. By applying channel coding, errors in the received side can be easily detected and corrected. In our experiment, the suitable channel coding was selected based on the size of the LED matrix.
# A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
## 3 Routing Protocols for UWSNs
### Localization-Free Routing Schemes
#### Eva-Dbr
The routing table update phase is carried out periodically. Sink nodes initiate the update process by broadcasting a routing update phase. This phase is responsible for updating the routing table information of nodes. In the routing phase, transmission power, frequency, and forwarding nodes are selected based on the routing tables, to achieve energy efficient forwarding. RECRP achieves moderate end-to-end delay and packet delivery ratio for a small number of nodes. However, the two parameters improve as the number of nodes increases. A drawback of this protocol is the excessive energy spent during the periodic routing update phase. During this phase, all underwater sensor nodes broadcast with the maximum power, which not only increases energy consumption, but also expands the contention domain, thus increasing delay and incurring further energy consumption. Periodic transmissions with maximum power may quickly drain the network energy. Figure 12: (**a**) Forwarding area in a high density network, (**b**) Forwarding area in a low density network [51].
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 2 Ionosphere Characteristics
### Ordinary and X-Ordinary Waves: Description and Latitude Dependencies  
The ionosphere consists of plasma ionized by the solar activity superposed with the Earth's magnetic field. These two factors make it an anisotropic medium. When an incident radio wave impacts the ionosphere, it splits into ordinary and extraordinary components due to the influence of the magnetic field [8]. These modes are differently affected by the external magnetic field. In this sense, the index of refraction in the ionosphere is defined by the Appleton formula [24], which provides different results for both ordinary and extraordinary waves. In fact, according to this formula, only these two modes can propagate in this medium.  
The ordinary and extraordinary modes propagate on different paths and velocity due to their distinct refractive index. As a consequence, these waves are reflected back at different heights reaching any receiver with different delays. Moreover, the amplitude and the maximum reflected frequency are also different. In fact, the maximum reflected frequency in the F2 layer for the ordinary (\(fof2\)) is usually lower than that for the extraordinary (\(fxf2\)). Ordinary and extraordinary waves have elliptical polarization with an axial ratio that might be different.  
In the particular case of high-latitudes [25], where the magnetic field is almost vertical, the extraordinary waves have horizontal polarization for near grazing incidence. As the extraordinary mode waves are heavily absorbed, a vertical HF antenna, that would receive the ordinary mode should take advantage over a horizontal antenna. Nevertheless, most of the times dipoles are used in that situation for convenience.
###### Abstract  
Aggregation technology can integrate distributed energy resources (DERs) into resource aggregation (RA) to achieve efficient utilization of resources. This paper studies a DERs aggregation model to construct a RA. Firstly, considering the uncertainty of the output of distributed generation (DG), the characteristics of DG are analyzed and the daily eigenvalues are extracted. The contour coefficient is introduced and the improved K-means algorithm is used to cluster the daily eigenvectors to get the multiple probability scenarios in a single season. Then, in order to obtain a RA with lower daily average cost, better power generation characteristics and higher regional aggregation degree, the DERs aggregation model based on multi-scenario and multi-objective is established considering multiple constraints. To obtain a compromise optimal solution, the cellular bat algorithm based on fuzzy membership degree (FMD-CBA) is used to solve the model. Finally, the validity of the multi-scenario and multi-objective model in a single season is verified by an example.  
distributed energy resources; cluster analysis; multi-objective optimization; scenario division; cellular bat algorithm +
Footnote †: journal: Applied Sciences
## 3 Different Objective Functions
### Direct Cost Objective Function  
The second criterion is the direct cost objective function, Equation (2), which is established for the treated structure in the following form:  
\[\text{Minimize }DC=C_{MAT}+C_{POW}+C_{LAB} \tag{2}\] \[=\{C_{MAT}=(C_{M,s}+C_{M,c}+C_{M,r}+C_{M,sc}+C_{M,e}+C_{M,ac,fp, tc}+C_{M,f}+C_{M,c,ng}+C_{M,c,oxy})/(e\cdot L)\}\ +\] \[\{C_{POW}=(C_{P,cgm}+C_{P,av}+C_{P,av})/(e\cdot L)\}\ +\] \[\{C_{LAB}=(C_{L,g}+C_{L,c,oxy-ng}+C_{L,p,a,l}+C_{L,uv}+C_{L,gp}+C_ {L,gpp}+C_{L,f}+C_{L,r}+C_{L,c}+C_{L,v}+C_{L,c})/(e\cdot L)\}\]  
where the objective variable \(DC\) represents the direct production costs per unit of useable surface of the structure. The direct costs are defined as the sum of material costs (\(C_{MAT}\)), power consumption costs (\(C_{POW}\)), and labour costs (\(C_{LAB}\)) required to produce the structural system.  
Material costs (\(C_{MAT}\)) include cost items such as structural steel (\(C_{M,s}\)), concrete (\(C_{M,c}\)), reinforcement (\(C_{M,r}\)), headed shear studs (\(C_{M,sc}\)), electrode consumption (\(C_{M,e}\)), anti-corrosion, fire protection and top coat painting (\(C_{M,ac,fp,tc}\)), floor-slab panels (\(C_{M,f}\)), natural gas consumption (\(C_{M,c,rg}\)), and oxygen consumption (\(C_{M,c,oxy}\)).  
Power consumption costs (\(C_{POW}\)) include edge grinding (\(C_{P,cgm}\)), shielded metal arc welding (\(C_{P,w}\)), stud arc welding (\(C_{P,zu}\)), and concrete consolidation (\(C_{P,p}\)).  
Labour costs (\(C_{LAB}\)) contain edge grinding (\(C_{L,g}\)), steel-sheet cutting (\(C_{L,c,oxy-ng}\)), preparation, assembly and tacking (\(C_{L,p,a,l}\)), shielded metal arc welding (\(C_{L,w}\)), stud arc welding (\(C_{L,sv}\)), surface protection and preparation (\(C_{L,spp}\)), formwork treatment (\(C_{L,f}\)), reinforcing steel treatment (\(C_{L,r}\)), concreting the slab (\(C_{L,c}\)), concrete consolidation (\(C_{L,v}\)), and curing the concrete (\(C_{L,cc}\)).  
For more information on the direct cost items and fabrication times (\(T_{i}\)), see [PERSON] and [PERSON] [4; 5] and [PERSON] et al. [14].
## 6 Limitations and Directions for Future Research  
This study has some limitations that need to be discussed and used as input for further research. First, in this study, we examined three content elements of banner ads pertaining to four layouts, and these banner ads were obtained from the online apparel sector. In further studies, banner ads prepared for different markets could be investigated. It would also be interesting to use online banner ads presented in various forms, as well as to investigate the influence of other content elements on visual attention. In addition, it will be beneficial to use more stimuli to improve the statistical power. Moreover, the size of the banner elements, color and font type used in banner ads are factors that were not examined in this study, which might influence visual behavior. In this manner, a possible direction for future research might be to complement this study with such visual salience factors in order to develop a more in-depth analysis. Additionally, the focus of the present study was the investigation of the effect of the content elements of banner ads on visual attention through the measurement of customers' eye movements and fixation behavior. In the future, this study may be further enhanced in order to evaluate the ultimate effects of the resulting visual attention by employing other measures of advertising effectiveness, such as the click-through rate and saccadic eye movements. Finally, while this study was carried out with an adequate number of participants compared to previous eye-tracking studies, in which small samples are typically widespread [71], it would be worthwhile to replicate it in cultures with right-to-left reading patterns, such as in Arabic countries, in order to ascertain whether the findings are consistent and what can be done to make them more generalizable.  
Conceptualization, S.P.; Data curation, G.G.M.D. and Y.I.; Formal analysis, Y.I.; Methodology, G.G.M.D. and Y.I.; Writing--original draft, S.P., G.G.M.D. and Y.I.; Writing--review & editing, S.P., G.G.M.D. and Y.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Atilim University (E-59394181-604.01.02-4912, 19.08.2020).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
Figure A2: The banner ads numbered 2a (**above**) and 2b (**below**).
## 5 Mechanism
### Disjoining Pressure  
Disjoining pressure (\(\Pi\)Id), defined as the attractive and repulsive forces between two thin layers of fluid surfaces [174], has been widely studied as one of the key mechanisms of nano-EOR [175; 176]. [PERSON] et al. [151] specifically defined disjoining pressure in thin liquid film as the excess pressure in the film relative to that in the bulk solution. Nanofluids have been considered to significantly affect disjoining pressure between two immiscible fluids.  
Figure 4: Contact angle variation of the oil-prime system in different concentrations. Reproduced with permission from reference [153]. Copyright 2013 Elsevier.  
The spreading and adhesion behaviors of nanofluids on a solid surface are quite complex. They are different from simple liquid behavior, since a three-phase contact region exists [177]. In three-phase regions, NPs dispersed in the liquid tend to develop wedge-shaped structures and forces toward the oil-solid contact area, as schematically shown in Figure 5A. Those ordered structures in the wedge film enhance the spreading and wetting strength of the nanofluid on the solid surface [178]. The force created by a single particle is relatively small, but the total forces of a large number of NPs can reach up to \(5\times 10^{4}\) Pa in the vertex region (Figure 5B) [4]. The mechanisms that drive this phenomenon are Brownian motion and electrostatic repulsion [177]. In short, the disjoining force is responsible for the detachment of oil from the solid surface while allowing the nanofluid to spread further.  
Disjoining pressure is affected by several parameters, such as the nanoparticle size, concentration, charge density, temperature, salinity, and surface characteristics [149]. [PERSON] et al. [148] concluded that a higher concentration and smaller size of NPs will lead to increased disjoining pressure. Moreover, the force at the wedge film will increase when smaller sizes of NPs with a higher charge density and electrostatic repulsion are used [4]. However, polydispersity of the NPs needs to be considered since it could significantly affect the magnitude of the disjoining pressure [179].
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 2 Motivation and Contribution  
In this study, we developed a control concept that could mitigate the risk of accidents in case of a degraded or noisy environment. We chose to utilize DSRC communication for our CACC function. The rationale behind the selection of DSRC was that the majority of vehicles currently available on the market that are equipped with V2X capabilities utilize DSRC technology, which renders this a pertinent subject for testing [17]. We created a simulation environment where we can simulate the physical layer of the transmission and we are able to degrade the network performance by manipulating the Signal-to-Noise Ratio (SNR). We compared the proposed control algorithm with a simple CACC and evaluated how the different modulations affect the performance of the system and how the newly developed control concept can respond to the revealed problems.  
Clearly, there is a significant potential to improve safety through the increased automation of transport systems. Automatic intervention in various vehicle systems is made possible by the widespread use of sensors that detect objects in the environment. This characteristic of highly automated systems provides an opportunity to reduce the likelihood of human error and the severity of any errors that do occur. It should be noted, however, that because they are highly dependent on environmental conditions, even modern sensor systems are severely limited in their applicability. With regard to camera or lidar systems, a non-line-of-sight (NLOS) scenario can occur, e.g., in a densely built-up urban environment or in heavy fog. In such cases, V2X can be used as a 360 deg sensing system to help make a safe decision [18]. However, the problem can be exacerbated if an NLOS scenario also occurs for radio communications [19]. In these cases, it is essential that the system makes decisions regarding vehicle control, taking into account risk factors like communication delay, packet loss, and speed.  
With these considerations in mind, this paper presents a concept capable of making vehicle control decisions in NLOS situations of radio-based vehicle functions, taking into account risk factors that affect the safety of the system. The proof of concept presented here is not intended to work as a final control solution that can be applied industrially, but primarily to demonstrate the applicability of the method and the validity of the considerations introduced. These objectives have been achieved through the simulation of the selected scenarios. Investigating the safety implications of the method has provided convincing results.
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 6 Performance Analysis and Comparison
### Performance Metrics and Results
Figure 9: Average energy consumption under different simulation time. collisions in low traffic load. The data packets could reach the root node easily at high packet intervals. The DTS performed better than the TMP and IEEE 802.15.4 standard in terms of PDR by 5% and 17% respectively. It is obvious from the figure that as packet interval increases the average end-to-end delay decreases. The traffic will be less at longer packet interval and at the shorter interval the traffic will be heavy. More packets were injected into the network during high traffic resulting in network congestion and reduced buffer size. Therefore, packets cannot reach the root node easily resulting in increased end-to-end delay. During high packet interval, the traffic load decreased and the packets can reach the root node easily. Hence, the average end-to-end delay decreased. DTS allocated the time slot by considering the available buffer memory in each node. Therefore, the dynamic time slot selection reduced the average end-to-end delay in the network. The percentage of reduction when compared with TMP and IEEE 802.15.4 was 41% and 43% respectively. Figure 12 shows that as the packet interval increased the energy consumption also increases. This is due to the decrease in the traffic load with an increase in the packet interval. An increase in the packet interval increased the idle listening time and the time required to transmit control overheads. This resulted in an increase in energy consumption. If the packet interval was at minimum, the listening time and the control overhead transmission time is also minimum. Hence, the energy consumption was minimum. In addition to this, ER in fuzzy rules and the remaining energy in the link cost function helped control the rise in energy consumption when compared to the other protocols. Figure 11 shows  
Figure 11: Average end-to-end delay under different packet intervals. Figure 10: Packet delivery ratio under different packet intervals. that the DTS scheme had the lowest energy consumption than the existing protocols. The percentage of decrease was 25% and 39% when compared with TMP and IEEE802.15.4 standard. From the two sets of simulation results, it is obvious that there is a considerable improvement in packet delivery ratio with respect to the compared protocols. Hence, the dropping ratio was high, thereby resulting in a better packet delivery ratio. Similarly, the average delay and the energy consumption was also reduced considerably due to the energy efficient link cost function used in the routing layer and the energy ratio considered in the time slot allocation method.
## 5 Experiment III: Effect on Restricting Temporal Modulation Components of TAE
### Results  
The results under the LPF condition are shown in Figure 5, where the horizontal axis indicates the cut-off frequency of the LPF, and the vertical axis shows the urgency scale (positive values indicate a higher degree of urgency). As we can see, the degrees of urgency from lowest to highest are ordered as the same as the results of experiments I and II. The degrees of urgency for stimuli c and d decreased as the cut-off frequency decreased. ANOVA analysis indicated a significant main effect of urgency perception (\(F(24,\infty)=200.1,\ p<0.01\)) and significant differences among all stimuli in c and d, where the cut-off frequencies ranged from 4 to 32 Hz (\(p<0.01\)). With the cut-off frequency of 2 Hz, significant differences were observed among all stimulus pairs except b & d and d & c (\(p<0.01\)).  
The results under the HPF condition are shown in Figure 6, where the horizontal and vertical axes are the same as in Figure 5. We can see here that the degrees of urgency from lowest to highest are ordered and the degrees of urgency for stimuli c and d decreased as the cut-off frequency increased. ANOVA analysis indicated a significant main effect of urgency perception (\(F(24,\infty)=1.517,\ p<0.01\)). There were no significant differences between all stimuli in c and d with regard to the cut-off frequency, although there were significant differences between all stimuli in b, in which the cut-off frequencies were from 2  
Figure 5: Results of experiment III: Urgency perception of noise-vocoded stimuli with the upper temporal modulation component of TAE restricted using LPF.  
to 8 Hz (\(p<0.01\)). With the cut-off frequencies of 8 and 12 Hz, significant differences were observed among all stimulus pairs except c & d (\(p<0.01\)).
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 6 Performance Evaluation and Benchmarking
### Metrics for Evaluating VLC-Based IPS
#### 6.1.4 Computational Complexity  
Computational complexity is one of the most important aspects to consider. In general, the complexity of a VLC-based IPS is due to the channel modeling between transmitters and receivers [119]. Factors such as reflections, diffraction, and interference need to be taken into account [120]. In addition, the complexity of signal processing algorithms used for demodulation, decoding, and localization will contribute to the overall complexity of the system. Furthermore, advanced techniques like multi-user detection and channel estimation can improve localization accuracy but would also lead to higher computational complexity. Thus, the trade-off between accuracy and complexity should be carefully considered.