esa-sceva/satcom-chunk-collection huggingface dataset:
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 1 Introduction  
Medical imaging of the torso helps doctors look at the organs and bone structures in the body. The organs will be damaged to varying degrees when the abdomen is subjected to penetrating or blunt trauma. The vital issue in trauma is damage to the internal organs, with the liver and kidneys being the most severely damaged [1]. Injuries to crucial internal organs can cause severe conditions such as bleeding, shock, infection, or peritonitis. They will be life-threatening if the injured person does not receive prompt and effective treatment. Applying computed tomography, magnetic resonance imaging, and other cross-sectional imaging techniques to clinical practice has led to qualitative changes in disease diagnosis [2; 3; 4]. These imaging images effectively help physicians make consultations for more accurate results and timely disease treatment.
## 5 Result and Analysis
### Nasa-Tlx
Regarding effort, voice interaction was significantly easier than all other interaction methods except for scaling interactions. No significant differences were observed among the other interaction methods. This indicates that users found voice interaction notably less strenuous. The Bimanual Scaling interaction method proposed in this study showed some superiority over current common interaction methods, though the effect was not markedly significant. Figure 8: Mean score of each NASA-TLX dimension. Statistical significance is shown as * for \(p\) < 0.05, ** for \(p\) < 0.01, and *** for \(p\) < 0.001. Error bars indicate standard deviation.
## 2 Materials and Methods
### Pseudovirus Construction for In Vitro Neutralization Assays  
The pseudoviral particles (PVs) containing the SARS-CoV2 Spike-D614G protein were generated according to the methodology described by [PERSON] et al. [28], with modifications. Basically, we generated a replication-defective Vesicular Stomatitis Virus (VSV) PV in which the backbone was provided by a pseudotyped \(\Delta\)G-luciferase (G*\(\Delta\)G-luciferase) rVSV (Kerafast, Boston, MA, USA), that packages the expression cassette for firefly luciferase instead of VSV-G in the VSV genome. Briefly, the full-length cDNA of Spike- D614G, and the Spike variants B.1.1.7 (alpha, first identified in the UK), P.1 (gamma, first identified in Manaus, Brazil) and B.1.617.2 (delta, first identified in India), were cloned into the eukaryotic expression vector pcDNA3.1, using the _EcoRV_ restriction site and a blunt-end ligation strategy, to generate the recombinant plasmids pcDNA-3.1-Spike-D614G, pcDNA-3.1-Spike-B.1.1.7, pcDNA-3.1-Spike-P.1 and pcDNA-3.1-Spike-B.1.617.2. HEK-293T cells growing in Optimum media (Gibco, Whaltman, MD, USA) with 2% of FBS were transduced with G*\(\Delta\)G-VSV at a multiplicity of infection of 4. Twenty minutes later, the cells were transfected with 30 \(\mu\)g of pcDNA-3.1-Spike-D614G, using Lipofectamine 3000 (Thermo Fisher Scientific, Carlsbad, CA, USA) and incubated for 6 h at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\). Then, the cells were washed 4 times with PBS in order to remove all the residual G*\(\Delta\)G-VSV, and cultured in complete media at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\). After 48 h the supernatant containing the PVs was collected, filtered (0.45-\(\mu\)m pore size, Millipore) and stored in single-use aliquots at \(-80\)\({}^{\circ}\)C. The 50% tissue culture infectious dose (TCID50) of SARS-CoV-2 PV was determined in sextuplicates and calculated using the Reed-Muench method as previously described [28].
## 2 Literature Review
### Hydrogen Safety and Leak Detection  
Safety concerns such as hydrogen leaks, fire hazards, and structural integrity necessitate robust monitoring and mitigation strategies. These include advanced hydrogen leak detection technologies like optical sensors, electrochemical detectors, and infrared imaging, which enable real-time monitoring [6]. Additionally, structural integrity must be addressed through the use of hydrogen-resistant coatings and novel alloys to counteract hydrogen embrittlement affecting metals and composite materials [24]. Moreover, safe aircraft operation requires emergency venting and containment systems that effectively manage controlled venting to prevent overpressure scenarios and minimize explosion risks [39].
# Automated Discovery of Successful Strategies in Association Football
## 1 Introduction  
Until recently, automated association football (henceforth called football, for short) analysis was limited by the quantity and relevance of the available data, which required researchers to be responsible for both the collection and analysis of relevant data. Fortunately, growing interest in sports analytics has led to the development of more advanced data collection techniques [1]. In football, we are moving from simple match sheet data with high-level information such as line-ups, substitutions, goals, and cards to other sources of information such as optical tracking and event stream data [2]. Event stream data, also known as soccer logs, provide descriptions of all on-the-ball actions performed by players (e.g., passes, shots, and fouls), along with their location in the field [2]. Unlike previous sources of data, event stream data are a source of _spatiotemporal information_; the events in the log are ordered based on their occurrence in time while simultaneously tracking the spatial location of each event in the field. Event stream data are becoming increasinglyavailable for research purposes (e.g., [3; 4; 5]), opening the possibility for further studies on data-driven decision making.  
In general, a _strategy_ can be understood as a plan or series of activities designed to achieve a major or overall aim [6; 7; 8]. In football, the study of strategies can be separated by the type of play under study (e.g., corner kick, penalty kick, free kick, open play). The core ideas we use to analyze strategies in this work apply to all types of plays within football. As we shall see, context plays a vital role in defining strategies. Therefore, a strategy to execute a corner kick will be inherently different from the strategy to execute a free kick, and their automated analysis will require different considerations. For this reason, in this work, we focus on the study of strategies for the corner kick use case. Corner kicks are one of the most critical plays in football. Numerous studies have estimated that set plays (e.g., corner kick and free kick) account for approximately 30% to 40% of goals [9; 10; 11; 12], with corner kicks being the most effective among them [10]. Although the conversion rate of corner kicks is low, at approximately 2.1% [12], it remains relatively high compared to other types of plays, such as open plays, which have a conversion rate of approximately 1.1% [12].
## 2 Literature Review
### Residual Stress and Warping Mechanisms in Resin-Based 3D Printing
#### Influence of Support Strategies and Build Platform Adhesion  
Support strategies have a significant influence on residual stress and warping during the resin printing process. Studies show that inappropriate support structures can exacerbate warping by introducing additional stresses at the layer interfaces [32; 36]. Innovative support designs and strategies, including adaptive support placements and automated generation based on active stress analysis, have been proposed to enhance part fidelity and minimize the need for post-process correction [54; 55].  
Additionally, enhancing the adhesion characteristics of the build platforms through optimized coating applications has been shown to create improved foundation stability, leading to lower instances of layer separation due to warp-induced tensions [13; 56].
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 5 Discussion
### Potential Applications per Class
#### 5.2.5 Recovery Action  
The significance of the \"Recovery Action\" classification in managing PDoS attacks extends far beyond immediate incident response. It is a comprehensive approach that influences strategic planning, resource allocation, and overall organizational preparedness in the face of cyber threats. Knowledge of a required recovery action contributes to the business operations, continuity, and risk management.  
Detailed knowledge of the recovery action needed allows organizations to more accurately forecast recovery timelines and resource requirements. For instance, knowing that a PDoS attack necessitates full-device replacement versus a simple restart enables the targeted allocation of financial and technical resources.  
Moreover, this classification aids in developing tailored training programs for IT staff and incident response teams. By understanding the spectrum of potential recovery actions, organizations can ensure that their teams are equipped with the necessary skills and tools to respond to a variety of scenarios. This preparation is crucial in reducing recovery time and mitigating the impact of attacks.
## 3 Numerical Results
### Case (A): Antenna Array  
Both the transmitter and the receiver used an eight-antenna array arranged in different shapes (i.e., L-shape, circular-shape, and Y-shape), as shown in Figure 3. The transmitter was located at the center of the office at 2 m height.  
Figure 2: (**a**) The simulated environment (top view); (**b**) The location of transmitting and receiving points in the simulated environment.  
The receivers were evenly distributed in the office at 1.5 m height. The performance for each shape of the antenna array in UWB frequency of 3.1-10.6 GHz was evaluated. The average BER versus SNR, the ratio of transmitting signal power to the noise power, for the 105 receivers are shown in Figure 4a, whereas the outage probability versus SNR for these receivers is shown in Figure 4b. In both figures, for most of the SNR values, the circular array has the smallest average BER and outage probability. For example, in Figure 4a, when the SNR is 68 dB, the BER values for the circular array, the L array, and the Y array are 0.18%, 0.61%, and 0.87%, respectively. In Figure 4b, when the SNR is 68 dB, the outage probability values for the circular array, the L array, and the Y array are 0.95%, 1.90%, and 1.90%, respectively. It is clear that the BER and the outage probability of the circular-shape array are better than those of the other two arrays in most of the circumstances.
# A Corner-Highlighting Method for Ambient Occlusion
## 6 Discussion  
Our algorithm stands strong when there are not many corners to be occluded in the scene. The fundamental of the corner highlighting method is to omit unnecessary ambient occlusion computation on not-occluded surfaces. The more spacious and simple the scene is, the better the performance will result. Two test scenes _Small Desert City_ and _Cartoon Temple_ highly meet this requirement. Their walls and floors are sufficiently flat and objects are sparsely placed, allowing a minimal chance for ambient occlusion to happen.  
On the other hand, our algorithm stands weak on rendering complex scenes where the majority of the place needs occlusion check. Our method intends to check possibly occluded areas. If all areas are affected by the occlusion, our algorithm will highlight the entire screen, which leads to no more than a rendering waste. If the goal of the rendering pipeline is, as an example, to support high-quality first-person shooting games, this is not the algorithm to have an interest in; such scene setups are recommended to run ambient occlusion _without_ our algorithm.  
Another downside of our algorithm is that it requires a considerable amount of time and work to generate model object outlines _prior to the real-time operation_. Not only preparing _integrated normal vectors_ for the outlines, but the developer also has to be aware of its local concave corners and its complexity. For instance, generating an outline for a room model object is often treated apart from the regular ones since its faces are towards inside.  
Improper outline generation may cause false region highlighting, _over-extruded_ outline faces, excessive outline mesh rendering, and so on. Our algorithm is based on a trade-off between ambient occlusion computing and meshes rasterizing; we focus on less ambient occlusion computation in the cost of more mesh rasterization. Outline model objects are also recommended to be simplified.
# Interference Analysis for Vehicle-to-Vehicle Communications at 28 GHz
## 1 Introduction
### Motivation and Contributions  
Though significant work has been done on the optimization and performance analysis of sub-6-GHz vehicular networks, there is a lack of understanding regarding the communication characteristics of vehicular networks at higher frequencies. Especially, it is important to characterize the inter-vehicle (V2V) communications for both LOS and non-LOS conditions. Since real-time simulations require a significant amount of computation resources, the closed-form expressions of different performance metrics of vehicular networks can help evaluate the performance of such networks. In addition, these expressions can help provide more insights regarding system performance in different states.  
Motivated by the above-stated positive considerations, we perform packet error probability (PEP) analysis of V2V communications under co-channel interference at 28 GHz. Besides this, we also provide an analytical expression of the ergodic capacity of vehicular networks at 28 GHz. Using this expression, a diversity analysis has also been performed which shows that the probability of receiving an erroneous packet depends on the number of multipath clusters and channel non-linearity in V2V communications. These expressions have been validated by performing extensive Monte Carlo simulations. The results indicate that the provided analytical expressions closely follow the simulation results.
# Lightweight Multiscale CNN Model for Wheat Disease Detection
## 2 Materials and Methods
### Dataset Preprocessing  
Since the color of wheat images can deviate from the true color due to different illumination, this can bring errors to the subsequent network model recognition. In this study, contrast enhancement is applied to the image in order to reduce the effect of light inhomogeneity on the image. Data augmentation is a technique that expands the training set to enhance the generalizability and robustness of deep learning. Considering the insufficient number of wheat disease images, the neural networks may overfit to the training set, leading to overfitting problems in the CNNs during testing. Therefore, the wheat disease data were augmented by rotating, symmetrically flipping, and increasing the contrast, among other operations. The original dataset was augmented to 8495 images (1156 leaf rust images, 1380 powdery mildew images, 1342 wheat smut images, 1096 root rot images, 1161 scab images, 1272 tar spot images, and 1086 healthy images). Several examples of enhanced images are shown in Figure 2. The augmented dataset was split into the training set and test set with an 8:2 ratio. The results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wheat Types** & **Images** & **Training Images** & **Testing Images** \\ \hline healthy & 1086 & 869 & 217 \\ leaf rust & 1156 & 925 & 231 \\ powdery & 1380 & 1104 & 276 \\ wheat loose smut & 1342 & 1073 & 269 \\ root rot & 1096 & 877 & 219 \\ fusarium head blight & 1161 & 929 & 232 \\ tan spot & 1272 & 1018 & 254 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wheat disease data after enhancement.  
Figure 1: Wheat diseases. (**a**) Powdery mildew; (**b**) leaf rust; (**c**) healthy wheat; (**d**) root rot; (**e**) fusarium head blight; (**f**) leaf rust; (**g**) tan spot; (**h**) black chaff.
## 3 Results and Discussion
### Thermal Conductivity Results
However, the porous structure interface with film coating may improve the thermal conductivity of coated laser processed samples. After laser processing, the surface areas of as-received OFHC substrates increased with respect to untreated ones. The increased surface areas can effectively flatten the power density distribution  
Figure 6: The thermal conductivities of laser processed OFHC, Ti film, and Ti–Zr–V–Hf coated laser processed OFHC substrates. for neutron tubes. For single metal Ti films, the laser processing technique can improve the thermal conductivities by 3.5%-11.5% as shown in Figure 6. Laser processing can reduce the thermal conductivities of film coatings at a certain degree, while film deposition can improve the thermal conductivities of film coatings, especially at high test temperatures. It can be explained that the surface porous structures formed by laser processing were filled by coatings which benefit the improvement of thermal conductivity. The hydrogen pumping property of Ti-Zr-V-Hf film is better than the one of Ti film as reported by [PERSON] et al. [13; 36]. Theoretically, the deuterium and tritium pumping properties of Ti-Zr-V-Hf film are superior to the ones of Ti film [37]. Hence, combined with the laser processing method, the Ti-Zr-V-Hf film with laser processed OFHC substrates may improve the neutron yield in neutron tubes effectively. The validity and related mechanism of Ti-Zr-V-Hf film with laser processing OFHC targets in neutron tubes requires further systematic study.
###### Abstract
Existing works on duty cycling aim to achieve network coverage and/or connectivity by finding the best nodes to sleep in order to minimize the number of awake nodes while maintaining network coverage and/or connectivity. Figure 1: (**a**) Position of sensor node defined by trilateration. (**b**) GPS error added to sink packets, the intersection between the packets is not a single point. (**c**) Poor position packet samples to perform trilateration. Figure 2: Greedy geographic forwarding example. Node 3 is going to receive the packet because it is closer to the sink. This journal presents energy-aware connected k-neighborhood (ECKN), a holistic solution that integrates position estimation, packet routing, and sleep scheduling in wireless sensor networks. We observed that these processes share some common features and can benefit from using the sensor node positions. Specifically, our approach leverages the sink's position broadcast for packet routing and enables nodes to estimate their positions based on this information. Moreover, our sleep scheduling algorithm aims to maintain network connectivity to facilitate successful packet delivery. By integrating these functionalities, our goal is to enhance the network lifetime by minimizing any overhead that may arise. The remainder of this paper is structured as follows: Section 2 provides an overview of related work in this area. Section 3 details our proposed algorithm for position estimation. Section 4 presents the packet routing algorithm. Section 5 describes the sleep scheduling algorithm we propose. The performance criteria and experimental results obtained via simulation are presented in Section 6. Finally, Section 7 concludes the paper.
# Enhancement of High-Intensity Focused Ultrasound Heating by Short-Pulse Generated Cavitation
## 1 Introduction  
High-intensity focused ultrasound (HIFU) has attracted attention as a noninvasive modality for the thermal ablation of solid tumors [1]. Ultrasound generated outside a body is focused on a target tumor and the tissue in the focal region is thermally coagulated within several to a few tens of seconds in a HIFU treatment. HIFU has been clinically applied to many kinds of target tissues, such as prostate [2; 3; 4; 5], breast [6], liver [7; 8], and pancreatic cancers [9] and bone metastasis [10]. One of the problems encountered with a HIFU treatment is a long treatment time especially for a large tissue volume to be treated. With a typical equipment for a HIFU treatment of prostate cancer, an ultrasound exposure at 4 MHz for 3 s produces a treatment size of 3 \(\times\) 3 \(\times\) 12 mm\({}^{3}\)[2], resulting in a median operating time of 142 min (ranging 35-390 min), including cooling time between consecutive HIFU exposures, to treat the median prostate volume of 21.9 cm\({}^{3}\) (ranging 4.6-68.8 cm\({}^{3}\)) [4]. Since the cooling time, which is needed to prevent intervening healthy tissues from a non-negligible temperature increase, is on the same order of HIFU exposure time, an efficient heating method is required for a drastic reduction of the HIFU treatment time.  
Microbubbles are known to have a role to accelerate ultrasonic heating when subjected to an ultrasound field. Microbubbles are forced to oscillate in an ultrasound field. The volumetric oscillation of bubbles increases ultrasonic energy dissipation in the vicinity of the bubbles through three mechanisms such as acoustic radiation, thermal, and viscous damping, which resulted in the enhanced heating effect. The effect of bubble-enhanced ultrasonic heating has been investigated experimentally [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24] and numerically [25; 26]. Although increased heat depositions were obtained using microbubbles in the previous research, distorted coagulation regions or temperature distribution were observed, such as tad-pole shaped [12; 22; 24] or pre-focal [13; 19; 24; 25; 26] heating regions. A \"triggerHIFU\" sequence that consists of high-intensity short pulses and following moderate-intensity long bursts has been investigated to avoid such distorted coagulation regions while enhancing the ultrasonic heating [27; 28; 29; 30; 31; 32; 33; 34; 35; 36].  
In this paper, several types of trigger HIFU sequence are introduced and the investigation of cavitation phenomena and enhanced heating effects by trigger HIFU sequences are reviewed.
## 3 Experimental Validation and Analysis
### Overall Deformation Reconstruction  
3.6.1. True Deformation Sequence  
Figure 16 illustrates the specific implementation process of overall dynamic deformation reconstruction. This involves removing high-frequency noise from acceleration data, performing a frequency-domain second integral to remove linear trend terms, and obtaining high-frequency dynamic displacements. The GNSS displacement series retains low-frequency displacements and is interpolated to match the sampling rate of the high-frequency dynamic displacement signals reconstructed from the acceleration data. Finally, the high-frequency dynamic displacements reconstructed from the acceleration data and the low-frequency dynamic displacements extracted from the GNSS data are combined to reconstruct the overall structural deformation.  
To extract low-frequency dynamic displacement information, the displacement data was filtered. Next, the acceleration data was denoised and detrended to obtain high-frequency dynamic displacements after the second integral. Through displacement interpolation, the displacement data collected by the two sensors was used for overall deformation reconstruction, resulting in the overall displacement shown on the left side of Figure 17. The right side of Figure 17 displays the deformation reconstruction sequences for the selected time period in this study, which will serve as the basis for subsequent analyses.  
Figure 16: Overall Deformation Reconstruction.  
Figure 17: (**a**) Overall deformation reconstruction; (**b**) Deformation reconstruction during the prediction period.