esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 11 Discussion on Private Campus Networks
### Mobile Evolution  
Generally, in the mobile industry, the promises of generation \"N\" are fulfilled by the \"N+1\" generation in practice. The main use-case for \"1G\" phones was the transmission of the human voicewith full mobility of users. The service providers only offered services with large size User Equipment (UE) where cell-to-cell handovers were not fully seamless. Thanks to the growing interest in this technology and the rapidly growing subscriber numbers, standardization committees have developed a system that provides a globally available voice transmission service, Global System for Mobile Communications (GSM; as later known as \"2G\"). Using the GSM standard, the UEs were able to move between cellular towers without interrupting duplex voice traffic; also, the devices could be used in other countries as well. However, data transfer was only added later, so the data transmission rate was quite low. The 384 kbps throughput and \(\sim\)200 ms one-way latency available in Enhanced Data for GSM Evolution (EDGE) provided a basic data network service, but could not at all compete with the current Digital Subscriber Line (DSL) or Cable Television (CaTV) data transmission.  
As service providers have seen that mobile data could have a bright future, it became the primary focus for their research and development. The voice transmission on the 2G network was almost perfect, but the growing number of users needed better spectral utilization. The 3G standard is already partly designed for data transmission along with the voice. The transfer rate was up to 42 Mbps. However, at the time of writing the standard, the main use-cases were known as web browsing and huge file downloads only. At that time, \"killer applications\" such as Facebook and Youtube were not particularly known, although they required much more data and continuous data transfer from the network site. It is clear that loading a web page has different demands on the network side than a continuous chat, even with Rich Communication Services (RCS) features, including eventual video messaging. To cover these diverse requirements, the 4G network was standardized to provide dynamic bandwidth allocation and serve the need of many users at the same time. The targeted peak data rate has ended up being 1 Gbps--with major question marks whether this has been reached by 4G in practice.  
The major plans for 5G systems around 2020 contain even higher data transfer rates (10 Gbps), more interactive services (1 ms latency), and more users (0.5M users/km2). The network is slowly becoming live and fulfilling the criteria described, but for now, the actual major use-cases, \"killer applications\" are highly awaited for 5G. As an example, the 1 Gbps data throughput cannot be leveraged by today's smartphones compared to 4G's 750 Mbps, so the user experience is not significantly better. Therefore, the extra infrastructure investment would not pay off in the short-term for this use-case.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 3 Proposed Methodology
### GAN Architecture  
In this work, we employ Wasserstein GAN (WGAN), Conditional WGAN (CWGAN), and WGAN with gradient penalty (WGAN-GP) as the underlying generative models due to their superior stability and effectiveness in data augmentation. WGAN improves upon standard GANs by replacing the Jensen-Shannon divergence with the Wasserstein distance, which leads to more stable training and mitigates mode collapse. CWGAN further enhances WGAN by conditioning both the generator and the critic on class labels, enabling the generation of targeted attack samples. WGAN-GP builds on WGAN by introducing a gradient penalty to enforce the Lipschitz constraint, offering improved convergence without relying on weight clipping.  
Despite these architectural advancements, WGAN, CWGAN, and WGAN-GP all lack explicit mechanisms to enforce feature-level alignment between real and synthetic data. This limitation motivates the incorporation of Soft Nearest Neighbor Loss (SNNL), which refines the feature representations of synthetic samples and ensures better alignment with the distribution of real network traffic.
## 4 Results and Discussion
### Water Balance Evaluation between 2020 and 2029
#### 4.7.4 Monthly Water Balance with the Consideration of Ecological Water Requirements
In summary, the annual water balance in Phuket Island without the consideration of ecological water requirements between 2020 and 2029 reveals a water surplus in all years under the dry and wet year scenarios in the normal and new normal conditions. Under the dry year scenario in the same period, the monthly water balance in Phuket Island without the consideration of ecological water requirements reveals a water deficit for six months (December-May) in the normal and new normal conditions. Under the wet year scenario, the monthly water balance in Phuket Island without the consideration of ecological water requirements indicates a water deficit for three months (February-April) in the normal and new normal conditions (see Figure 15). On the contrary, when considering ecological water requirements, the annual water balance in Phuket Island between 2020 and 2029 shows a water deficit in all years under the dry year scenario in the normal and new normal conditions. However, under the wet year scenario for the same period, the annual water balance in Phuket Island indicates a water surplus in all years in the normal and new normal conditions. Under the dry year scenario, the monthly water balance in Phuket Island in the same period reveals a water deficit for seven months (November-May) in the normal and new normal conditions. Under the wet year scenario, the monthly water balance in Phuket Island shows a water deficit for four months (January-April) in the normal and new normal conditions (see Figure 16). Accordingly, these findings indicate the effect of rainfall on the water balance in terms of deficit and surplus and imply the possibility of water scarcity in the future.
## 3 Results
### Removal of Bacterial Biofilm  
Concerning the removal of a 21-day bacterial biofilm, all three irrigation procedures significantly reduced the metabolic activity of the established biofilm, regardless of the apical preparation size, compared to the control group without irrigation (Figure 3a, \(p<0.05\)). Additionally, ultrasonic and sonic activation induced a significant reduction compared to manual irrigation, for both apical sizes (\(p<0.05\)). For an apical size of 35/.06, no significant differences were observed between ultrasonic and sonic groups. Conversely, for 50/.06, the ultrasonic activation induced a significantly greater reduction compared to sonic activation (\(p<0.05\)). Furthermore, a significantly higher reduction in the metabolic activity of the biofilm was achieved in the apical size 50/.06, over the 35/.06, for both ultrasonic and sonic systems (\(p<0.05\)). For manual irrigation, the reduction was similar for both apical sizes. Representative images of the biofilm remaining on the root canal surface  
Figure 1: (**a**) Frequency of irrigant extrusion under different root canal irrigation procedures and apical preparation sizes. (**b**) Extension of irrigant extrusion in positive samples, and representative images of irrigant extrusion (inset, scale bar 0.5 cm). (**c**) Mass of apically extruded debris under different root canal irrigation procedures and apical preparation sizes. * Significantly different between apical sizes, \(p<0.05\).  
after each procedure, focusing on teeth instrumented to an apical size 50/.06, are presented in Figure 3b.  
Figure 3: (**a**) Metabolic activity of the 21-day _E. faecalis_ biofilm after irrigation procedure, quantified by the resazurin assay. * Significantly different from the Manual, for the same apical size; **+** Significantly different to the control (C), for the same apical size; **+** Significant differences between Ultrasonic 50/.06 and Sonic 50/.06 groups; § Significant differences between the same irrigation procedure at different apical sizes; for all \(p\) < 0.05. (**b**) Representative SEM images of the root canal walls after irrigation, in an apical size of 50/.06 (bars 5 \(\upmu\)m).  
Figure 2: Representative SEM images of the root canal walls at (**a**) lower magnification for debris evaluation (bars 100 \(\upmu\)m); and at (**b**) higher magnification for smear layer evaluation (bars 10 \(\upmu\)m). Number of samples indexed for each score in evaluating (**c**) residual debris and (**d**) smear layer, after selected irrigation procedure, in apical sizes of 35/.06 and 50/.06.
# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 3 Materials and Methods
### Data Preprocessing
We estimated the transformation parameter by minimizing the weighted squared distance between the empirical characteristic function of transformed data and the characteristic function of the normal distribution [79]. This method provides two types of transformations: Yeo-Johnson transformation and the Box-Cox transformation. The Box-Cox conversion can only be used for strictly positive data; therefore, the first variant should predict temperatures that may be negative or zero. The Yeo-Johnson transformation is defined by the Formula (1):  
\[x_{i}{}^{(\lambda)}\left\{ \begin{array}{ll}[(x_{i}+1)^{\lambda}-1]/\lambda& \text{if }\lambda\
eq 0,\ x_{i}\geq 0,\\ ln(x_{i})+1&\text{if }\lambda=0,\ x_{i}\geq 0\\ -[(-x_{i}+1)^{2-\lambda}-1]/(2-\lambda)&\text{if }\lambda\
eq 2,\ x_{i}<0,\\ -ln(-x_{i}+1)&\text{if }\lambda=2,\ x_{i}<0,\end{array} \right. \tag{1}\]  
while the Box-Cox transformation is described by Formula (2):  
\[x_{i}{}^{(\lambda)}\left\{ \begin{array}{ll}\frac{x_{i}^{\lambda}-1}{\lambda }&\text{if }\lambda\
eq 0,\\ ln(x_{i})&\text{if }\lambda=0.\end{array} \right. \tag{2}\]  
Standardization of a dataset is a common requirement for many machine learning estimators. Typically, this is done by removing the mean and scaling to the unit variance. However, outliers can often influence the sample mean/variance negatively. In such cases, the median and the interquartile range often provide better results. The interquartile range (IQR) is robust to the outliers that can occur with weather data. In this method, the median is removed and then the data are scaled according to the range of quantiles between the first quartile (25 th quantile) and the third quartile (75 th quantile) [80]. Due to the nature of the dataset studied, we decided that it was necessary to compare the performance of the prediction model for the two methods described.
# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 4 Experiments and Results
### Ablation Experiments  
#### 4.5.1 Ablation Study of Each Module in SARFNet  
We conducted ablation experiments to verify the effect of different modules in this architecture. Table 3 shows that, when AAM is used for UNet, the results are 89.62, 81.79, 80.98, and 84.13 for the WT, TC, ET, and average Dice, respectively. The results of UNet with AAM are better than baseline, which shows that the AAM module is effective at brain tumor segmentation tasks because of its ability to use axial attention to extract global information and retain the characteristics of ordinary convolution and multiscale receptive fields. After the SL\({}_{\text{i}}\)RF module is added, the results are 90.42, 84.00, 80.66, and 85.03 for the WT, TC, ET, and average Dice, respectively, which indicates that it has been further improved on the basis of the previous module. The module plays a role in further feature extraction during the downsampling stage.  
After CAM was added, the TC, ET, and average Dice increased, with values of 90.13, 84.19, 81.14, and 85.15 for the WT, TC, ET, and average Dice, respectively. The results of the ablation study of deep supervision show that the results are improved at WT, TC, ET, and average Dice. We fused the above modules to the network; the results are 90.40, 85.54, 82.15, and 86.03 for WT, TC, ET, and average Dice, respectively. The best results show that our network and all the modules can be effectively applied to brain tumor segmentation tasks.  
Figure 7: Visualization results of medical cases. From left to right: T1, T1 ce, T2, FLAIR, the result segmented by SARFNet, and the ground truth. Green, yellow, and red represent the whole tumor, tumor core, and enhancing tumor, respectively.
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 1 Introduction
Figure 1b shows a pie chart with the break-up into the eight fields. The most prominent field was ophthalmology including roughly a third of all publications, followed by the 'technical' publications, reports on dental applications, PS-OCT in bones, cartilage, muscles and tendons, and skin imaging. Further fields of application were imaging of cardiac and vascular tissue, of cancerous tissue, and of neural tissue. The evolution of the eight groups over the past 20 years is shown in Figure 1c. Here, an increasing number of research papers on PS-OCT in the eye during the last decade can be observed. The relative share of publications grouped in the respective fields is shown in Figure 1d. Figure 1: PS-OCT in the scientific literature. Results from a literature search for “polarization sensitive optical coherence tomography” and “polarization sensitive optical coherence tomography” on PubMed ([[https://www.ncbi.nlm.nih.gov/pubmed/](https://www.ncbi.nlm.nih.gov/pubmed/)]([https://www.ncbi.nlm.nih.gov/pubmed/](https://www.ncbi.nlm.nih.gov/pubmed/)), accessed on 5 January 2017). (**a**) Number of documents published per year since 1997; (**b**) Break-up of the publications into seven medical and one technical area. The latter includes PS-OCT measurements of technical samples and phantoms; (**c**) Absolute numbers of publications per year are shown for the eight fields of application; (**d**) Time course of the relative share of the eight subgroups. The color legend in (**b**) also applies to (**c**) and (**d**). This review paper strives to provide an overview of the state of the art in PS-OCT. While other review papers provided a more in-depth discussion of the physical principles of the technique [9; 10], this article is particularly focusing on applications of PS-OCT. In the following, we will first introduce basic concepts for describing polarization of light as well as commonly used technical approaches for realizing PS-OCT. Then, using the eight categories mentioned above, we will discuss important applications of PS-OCT in the biomedical field and for non-biomedical use.
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 3 Sensitivity Analysis
### Near-Field Far-Field Boundary  
In antenna engineering, the near-field and the far-field are spatial regions with different characteristics. In the region close to an antenna, i.e., in the near field, there is no electro-magnetic (EM) radiation. In the far-field region, a combination of electrical and magnetic waves, known as EM wave, is radiated. For a coil system, the distance \(d\) at which the near-field passes into the far-field is conventionally defined by the frequency \(f\) of the transmitted signal and the speed of light in the medium \(c_{\text{medium}}\)[16]:  
\[d=\frac{c_{\text{medium}}}{2\pi f}. \tag{1}\]  
This equation is valid for all setups where the size of the antenna is much smaller than the wavelength. The maximum transmission range of a near-field MI communication system is limited by the corresponding near-field far-field boundary. For example, the near-field far-field boundary in air for \(c_{\text{air}}=~{}3\times 10^{8}\) m/s and \(f=100\) kHz is given as \(d_{100\text{kHz}}\approx 477\) m.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 5 Software System
### Top Side System  
The station was designed to allow the user to control the system thoroughly. The station consists of a power supply, computer, screen, joystick controller, laptop, and upper Arduino as a controller for the upper control station. The joystick and the upper Arduino (transmitter) were connected to the laptop and was connected to the joystick. The upper Arduino receives the information (i.e., coordinates) from the joystick and transmits it to the bottom Arduino to operate the motors using \"Processing IDE\". We developed a graphical user interface (GUI) to allow the pilot to control the speed and motion of motors. The top Arduino communicates with the bottom Arduino underneath the water through a universal asynchronous receiver-transmitter (UART) to simplify the process that any of two Arduino can send and receive data from each other.  
The purpose of the code in the processing is to handle the top-side system and connects the Logitech extreme with the upper Arduino. The joystick sends the speed to the upper Arduino in a hexadecimal arrangement to save memory since the maximum speed (255 ms) can be saved only in 2 bytes. The upper Arduino receives the speed and sends it through serial communication in a UART cable to the bottom Arduino underneath the water. We used Logitech 3D pro joystick. We programmed Logitech 3D pro joystick using \"Processing\" and controlled motors using a Proportional-integral-derivative (PID) control PID.
###### Abstract
[4] to conduct routing decisions to explore a road network whose status and available resources are initially unknown. * By routing decisions in a network that is updated dynamically during the exploration using a UAV with a limited flying range. * This study presents refueling strategies based on a dynamic fuel buffer that avoids the risk of having a route failure due to running out of fuel. * We propose three criteria for refueling decisions based on the information available from the road network and the flying capacity at each moment of the exploration. * Our research considers that as long as a refueling station is detected, which is accessible from the DMC, it can be used to refuel the UAV. Road accessibility is evaluated to ensure that the station can be resupplied with fuel. * Our research assesses the state of the road network, including refueling stations, and maps the disrupted network to use in the humanitarian aid deployment operation. * Our research provides information about which percentage of potential refueling stations is best for different network sizes and disruption levels, and thus supports tactical decision-making. * We additionally provide information on the best refueling strategy when the number of potential refueling stations is fixed, i.e., when a disaster hits for a certain network size and disruption level. The rest of this paper is structured as follows. In Section 2, a literature review is presented. Section 3 provides a formal description of the problem addressed. The solution approach is presented in Section 4. The numerical study and computational results are provided in Sections 5 and 6. Finally, Section 7 presents the main conclusions and proposes future work. Figure 1: Known road network. Figure 3: Aerial network. Figure 2: Disrupted network.
## 3 Results and Discussion  
### Analysis of the Results from the SLDV  
Velocity plots obtained directly from the SLDV were analyzed. The Lamb wave propagation of the specimen is visualized in Figure 2. The schematic representation of the PZT placement and damage locations are shown in Figure 2a. The experiment was carried out with frequency ranges of 10, 15, 20, 25, 50, 100, 150, and 200 kHz, and it was found that the 150 kHz frequency provides good visualization of results. The full wavefield results obtained from 150 kHz at different time periods are analysed. Figure 2b shows the propagation of the waves in the form of circles. The waves reach the damage region (hole) and are distracted, thus showing the location and damage shape. This perfectly shows the hole region with scattered Lamb waves. The amplitude of the S0 mode is much lower than A0 mode, therefore, it is barely noticeable in the full field of the wave measured by the SLDV. Thus, the antisymmetric A0 mode waves are predominantly visible and marked in all of the figures. When the waves reach the damage zone, they are reflected and mode change occurs. The important aspect is to visualize the wave reflections after they touch the impact damage zone. The SLDV helps in quality analysis of the material without destroying it. Figure 2c shows the detection of BVID 1. The waves are scattered at the damage region (marked with black circles) and mode change occurs. Figure 2d shows the wave pattern analysis and detection of BVID 2. As mentioned earlier, SLDV is a powerful NDT tool. In our previous research, we identified debonding with a diameter of 0.5 cm in honeycomb structures with 15 kHz and 0.9 cm BVID in carbon fiber reinforced polymer (CFRP) structures with 150 kHz.  
The SLDV measures the out-of-plane displacement and helps in the early inspection. As shown in Figure 2, one can visualize the S waves reaching the damaged region and its conversion. This shows a clear visualization of the damage. The times required for the waves to reach the damage zones are summarized in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency (kHz)** & **Hole (\(\mu\)s)** & **BVID 1 (\(\mu\)s)** & **BVID 2 (\(\mu\)s)** \\ \hline
150 & 29 & 37 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Times required for detection of damage.
## 3 Results
### Disaster Management
#### 3.1.2 Health and Safety  
Studies revealed that drones with multi-gas detectors and high-definition video cameras significantly aid rescuers in environments with fire, toxic, or flammable hazards, operating effectively even without visual contact or GPS signals, due to their high-power engines and multiple propellers [89]. Drones can also help in reducing distribution risk and improving the efficiency of medical materials delivery associated with public health emergencies [92]. [PERSON] et al. [91] proposed an AI-powered model that adopts drones to deliver test kits to patients, as well as collect the test samples back. The potential for non-contact medical supply distribution is crucial in highly infectious health events such as pandemics to minimise person-to-person contact.
# Priority-Based Content Delivery in the Internet of Vehicles through Named Data Networking +
## 4 The Priority-Based Forwarding Framework
### Interest Retransmissions  
After the Interest transmission, a consumer could not receive the Data due to losses over the error-prone wireless channel or to the temporary unavailability of producers or neighbor nodes storing the desired content.  
Therefore, an Interest is retransmitted if the requested Data is not received within the relevant Retransmission Timeout (RTO) from neither of the two network faces. The RTO depends on the measured RTT at each received Data packet, and it is set according to [PERSON]'s estimation. In particular, in the implementation, we refer to the built-in ndnSIM routines for the RTO computation.  
Retransmission of Interests is enforced only at the consumer side; intermediate nodes simply forward Interest/Data. Such a choice is meant to control packet redundancy over the shared wireless medium, and it is even more reasonable when the parallel strategy is applied.