esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Radiological Imaging Modalities  
Since the emergence of COVID-19, due to the high number of deaths, many organizations in different countries have sought to develop rapid and accurate diagnostic methods. Given that COVID-19 causes distinct spots on the lungs--as shown by the sample in Figure 3--a radiological examination of the chest is an important tool that can be used in the diagnostic process [19]. CT scans play an essential role in diagnosing pneumonia, especially given their efficiency and accuracy in detecting the features of pneumonia. Accordingly, many prior studies (in Section 4.3) have used the CT scan modality for the examination and diagnosis of COVID-19 despite the fact that CXR is more appropriate for the process, for the following reasons: firstly, it is difficult to control the spread of infectious diseases in CT suites and to decontaminate CT scanning machines; secondly, CT scans are costly, and are not available in all hospitals; thirdly, CT scanners are not portable [21]. In comparison, CXR is portable, cheap, simple to perform, and available in most hospitals. Therefore, CXR is the most commonly applied method for the radiological examination of the lungs [22]. The American College of Radiology (ACR) recommends that portable chest radiography tools should be used to limit the risk of disease transmission when scanning COVID-19 patients [23].  
Figure 2: An example of ICD-10 hierarchical structure of the respiratory system diseases.
# Numerical Analysis of EOD Helmet under Blast Load Events Using Human Head Model
## 3 Results and Discussion
### Brain Acceleration  
To obtain the brain CG acceleration, different parts of the head have been grouped: white and grey matter brain, corpus callosum, brain stem, cerebellum, falx cerebri, tentorium, and cerebrospinal fluid, as is shown in Figure 8.  
The obtained results of accelerations history, for both the cases (without and with helmet) and the three load masses, are presented in Figure 9.  
Brain acceleration increase with the TNT mass for both the cases, reaching considerably higher values, in situations where the protective helmet is not used. Table 5 presents the maximum peak accelerations obtained from curves shown in Figure 9.  
These values show a peak brain acceleration reduction of 80.29% for the case of lower TNT mass. In the intermediate case, the acceleration was reduced by 78.93%, and finally, for the 0.100 kg TNT explosion, the brain suffers 77.34% less when the helmet is used. Moreover, the time at which peak acceleration is reached increases significantly with the use of the helmet, attributed to the energy absorption by the helmet.  
From these accelerations, it is possible to obtain relevant parameters to evaluate different brain injuries. These parameters are HIC (Head Injury Criteria) and GSI (Gadd Severity Index), whose analysis is shown below.  
Figure 8: Center of gravity brain position to calculate the acceleration.  
Figure 9: Resultant acceleration evolution measured on the center of gravity of the brain. (**a**) Without helmet model. (**b**) Model using the protective helmet.
## 2 Physical Modelling Experiment Design
### Mining and Monitoring System  
Prior to initiating the model experiment, grid lines were drawn at 10 cm intervals, and punch points were established for particle image recognition and for monitoring deformations within the rock formations.  
Reflecting the characteristics of the airspace and drawing on experiences with the width of coal pillars left in room-and-pillar mining, the mining for this physical simulation experiment was segmented into 14 stages. The total length of the model working face measured 210 cm, with coal pillars systematically numbered in sequence. Each pillar, measuring 5 cm, contributed to a total of 42 numbered coal pillars. The deformation caused by mining impact in this experiment was due to the mining of coal, which disrupted the stress equilibrium within the rock layers. This disruption led to a redistribution of stress within the mining area. The redistributed stress was transmitted layer by layer, and as this disturbance accumulated over time, it ultimately affected the surface. The deformation of the overlying rock layers and the pipeline was thus a result of this progressive redistribution of stress. Following each stage of mining, the model was allowed to rest for one hour to ensure the full deformation of the overlying rock layer.  
During the experiment, a professional camera captured real-time photographs, and PIV (particle image velocimetry) software (V9.2) was employed to analyze and record the displacement field changes in the overlying rock layer, as shown in Figure 2. To monitor the deformation of the pipeline and changes in soil pressure around the pipe under mining conditions, a total of 15 TMS-C04A resistive thin-film pressure sensors were strategically placed around the pipeline. These sensors were symmetrically distributed across five sections on both sides of the model, positioned at the bottom, middle, and top of the pipe, respectively (see Figure 2). Additionally, 21 120-3 CA strain gauges were affixed to seven sections of the pipeline to detect circumferential and longitudinal strains. An optical  
Figure 1: Laboratory physical and mechanical texts: (**a**) uniaxial compression test, (**b**) failure modes of the specimens.  
fiber was bonded to the pipeline using epoxy resin at the top, middle, and bottom surfaces to record the axial strain. The specific layout of the measurement points is illustrated in Figure 3.
## 5 Conclusions and Future Work
Additionally, we observed that the train track and the vehicular channels have a certain similarity. The railroad channel with an omnidirectional antenna is similar to a suburban environment in the vehicular channel. Moreover, the railroad channel with a bidirectional antenna is similar to a highway LoS environment in the vehicular channel. Interestingly, the channel characteristics at crossing #4 are unique no matter what antenna is used. From the behavior, we can define that the population of the surrounding buildings and the size of the LoS window can highly affect the RF propagation characteristics. Our experiments considered five rural-suburban combined crossings with three runs of measurements. In addition, the tested train track is two lanes. However, our data shows a little change in the fading environment along a railroad track within the range of the measuring equipment. Our measurements were conducted with a train speed of 10 mph due to regulations. However, there are faster operating train speed services provided in other countries. The propagation channels for faster-moving trains may be different than what we presented in this paper, especially in the Doppler spread. Since the train operation environment varies, more experimental data with various scenarios are needed to develop generalizable models. As wireless communication systems develop to 6G, the complexity of channel modeling will increase and require considering various scenarios [28; 29; 30]. Additionally, the frequency band will vary for 6G wireless communication systems, which will be another necessary study to be conducted [24; 31; 32]. Methodology, J.C.; validation, J.C. and S.A.; formal analysis, J.C.; writing--original draft preparation, J.C.; writing--review and editing, J.C. and S.A. All authors have read and agreed to the published version of the manuscript. This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00986, Development of artificial intelligence-based base station electromagnetic wave human exposure prediction algorithm). Data sharing is not applicable to this article. This work was supported by Institute of Information & Communica-tions Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00986, Development of artificial intelligence-based base station electromagnetic wave human exposure prediction algorithm). The authors declare no conflict of interest.
## 3 Overview of the U-Smile Method
At level \(3\), the weighted overall BA-RB-I coefficients are calculated as weighted means of their respective net coefficients. In **Step 3**, the U-smile plot of the BA-RB-I coefficients displays the four subclasses along the x-axis and the corresponding BA or RB values on the y-axis. Point color indicates class (blue for non-events, red for events), fill indicates prediction direction (solid for improved, light for worsened), and point size reflects the I coefficient, showing the proportion of affected individuals. * Level 2 (class-specific): Two values per coefficient family express net effects for non-events and events, calculated as the difference between improvement and worsening in each class,
* Level 3 (overall weighted): One value per coefficient family expresses weighted averages of class-level net coefficients, accounting for class imbalance with weights proportional to class sizes. For mathematical details and formulas of the BA-RB-I coefficients, please refer to our previous work. The U-smile plot (Figure 1, Step 3) provides a visual summary of the results. The four subclasses are shown in a fixed order on the x-axis, and the y-axis shows BA or RB coefficient values. The vertical distance between points in each class represents the values of class-specific net BA or RB coefficients. In the extended version of the U-smile plot, point sizes are scaled according to the corresponding I coefficient values, linking the magnitude of prediction changes with the proportion of affected individuals. The shape of the U-smile plot (Figure 2) provides an intuitive and immediate interpretation: a smile means prediction improvement, a frown--prediction worsening, a flatline--means no significant change in prediction, and a zigzag means prediction improvement in one class and worsening in the other. Figure 2: The U-smile plot is a graphical summary of Levels 1 and 2 of the U-smile method. **(A)** The U-smile plot of the BA or RB coefficients visualizes the effect of adding a new variable to a reference model. The points represent the subclass-specific coefficients of Level 1 of the U-smile method. A smile, a frown, a flat line and a zigzag are the main shapes of the U-smile plot. We observe a double smile (panel a) when the new variable improved prediction in both classes, and a smile + flat line or flat line + smile shape (panels b and c) when the prediction was improved in one class with no changes in the other. Worsened prediction in both classes is indicated by a double frown shape (panel d), and frown + flat line or flat line + frown shapes (panels e and f) show prediction worsening in one class with no changes in the other. A double flat line in the U-smile plot indicates no change in prediction in both classes (panel g), while a zigzag pattern is produced when prediction was improved in one class and worsened in the other (panels h and i). **(B)** The vertical distances between blue and red points represent class-specific net BA and RB coefficients of Level 2 of the U-smile method and quantify the net effect size of prediction improvement offered by a new marker for non-events and events, respectively.
# Target Classification Using Frontal Images Measured by 77 GHz FMCW Radar through DCNN
## 5 Deep Convolutional Neural Networks
As seen in Table 1, the classification accuracy for 6 classes yielded 87.1%. The confusion matrix is shown in Table 2. If we want to classify only 3 classes such as vehicles, humans, and clutter, then the accuracy is 92.6%.
###### Abstract  
Purpose: To evaluate the clinical and histological outcomes of patients that receive implant-supported crowns after vacuum plasma surface treatment (VPST) of biomaterials used in socket preservation (SP) and guided bone regeneration (GBR). Materials and methods: This study was designed as a case series. Patients in need of tooth extraction and socket preservation or guided bone regeneration were enrolled. The socket preservation technique was performed after tooth extraction using a heterologous collagen bone graft and a collagen xenomatrix, both activated with vacuum plasma. Meanwhile, a two-stage horizontal ridge augmentation was performed using a customized titanium mesh and a mix of autologous (untreated) and heterologous (treated) bone grafts, along with a treated collagen membrane. ACTILINK Reborn with Universal Vortex Holder (Plasmapp Co., Ltd., Daejeon, Republic of Korea) was used to treat all biomaterials. The outcome measures were implant and prosthesis failures, complications, and histological examination. Soft and hard tissue samples were collected at the time of implant placement only in patients treated with SP. Results: A total of six patients were treated--three with socket preservation and delayed implant placement, and three with staged GBR. No implant or prosthesis failed. One customized titanium mesh broke after plasma treatment, requiring replacement with a pericadium membrane. No other complications occurred. Histological analysis at three months post-surgery revealed well-vascularized newly formed bone at different stages of maturation with integrated bone graft particles, while the soft tissue appeared to be physiologically structured. Conclusion: VPST may enhance the hydrophilicity of biomaterials, supporting favorable healing outcomes in SP and GBR. Further randomized controlled trials with appropriate sample size calculations are needed to confirm these preliminary results.  
Keywords:plasma treatment; socket preservation; guided bone regeneration (GBR); biomaterials; hydrophilicity; surface energy; histological evaluation; clinical audit +
Footnote †: journal:
## 2 Proposed System
### Scene Understanding for Lav Control  
**Motivation:** Figure 6 presents examples that effectively demonstrate why scene understanding is an important ability for a UAV to contain to follow the different working conditions. For example, when landing or delivering baggage on a ship, even if the same flight action is used, the flight strategy selected by a UAV should be completely different from that it applies when working at the polar station (a detailed definition of the parameters in the flight strategy can be found in Table 1). Based on this observation, we introduce the deep learning-based scene-understanding algorithm into the UAV control system.  
We selected the well-known Resnet-101 [29] to achieve the scene-understanding process, owing to its outstanding classification results with a much more complex and deeper network structure (as illustrated in Figure 7). The Resnet-based scene classifier was trained with the Places365 dataset (its implementation is presented in the following section). Further information on Resnet is beyond the scope of this paper, and its detailed description can be found in [29]. In our system, as illustrated in Figure 8, the scene classifier was applied to the aerial images captured from the UAV every 3 s. To reduce the effects of random false alarms, a statistics voting process was applied: from the five successive classification results, the most frequently appearing result was considered as the final scene-understanding result. The corresponding flight strategy for the scene-understanding result of our UAV control system is presented in Table 1.  
Figure 5: Structure diagram of the system.  
Figure 6: Under different working conditions, a UAV should choose different flight strategy.
## 4 System Design and Goals
### Message Format  
The new HI message type conforms to the MANET neighborhood discovery protocol (NHDP) specification [13]; thus, the HI message is technically similar to the HELLO message, with the following exceptions:  
* When the HI message type is the HI message instead of the HELLO message.
* When the HI message is only generated in response to the HELLO message.
* When nodes are not permitted to generate a HI message without first hearing the initial HELLO message.  
When only nodes that generated the initial HELLO message can respond to a HI message after the DB chores period. The only authorized response to a HI message after the DB period is with a TC message.
## 3 Circuit Performance
The zero crossing is smooth due to push–pull operational amplifier configuration; (**c**) A parametric plot of the current measured directly with a high-accuracy current transducer vs. the current measured indirectly on the monitor output of the current supply while the circuit is driven as in (**b**). The line is a linear fit with a slope of 1.0179(4). waveforms with the circuit connected to one pair of coils, we produce a sinusoidal current modulation \(I=0.5I_{\text{P-p}}\sin\left(2\pi ft\right)\) with \(I_{\text{P-p}}=1\) A and \(f=1\) kHz. The current is measured using the output monitor (Figure 2b) and a current transducer (IT 400-S Ultrastab, LEM). A parametric plot of the one measurement method vs. the other is shown in Figure 2c. The monitor output reliably represents the actual current supplied by the circuit, with a Pearson correlation coefficient between the measurement methods of 0.9998(6) and a slope of a fitted linear relation equal to 1.0179(4). We verify that a sinusoidal current modulation from \(-0.5\) A to 0.5 A and 0 A to 1 A can be generated at frequencies up to 2 kHz before it becomes distorted. The OPA549 power amplifiers have internal protection against overheating--once 125 \({}^{\circ}\)C is reached, they shut down and short E/S pins to the ground until they have cooled down. The thermal resistance of a bare amplifier equals \(\theta_{\text{JA}}=30\)\({}^{\circ}\)C/W, which makes them heat up quickly at high continuous currents. We assure proper operation of the devices for the entire design current range by adding a radiator with thermal resistance \(\theta_{\text{HA}}=6\)\({}^{\circ}\)C/W, which, in total, gives \(\theta_{\text{JA}}=7.9\)\({}^{\circ}\)C/W. We further increase the heat dissipation efficiency by adding a fan such that the temperature of the amplifier is always well below 125 \({}^{\circ}\)C. The power amplifiers are mounted at the edge of the circuit board such that a better (in terms of lower thermal resistance) or even water cooled radiator could be installed if currents >4 A are to be used. All noise measurements are performed with the cooling fan turned on.
## 2 Overview of Candidate Technologies for Wearable Wireless Networks
### Body-to-Body Technologies and Implementations  
For BBN communications, the devices are supposed to communicate over relatively long distances and often are equipped with better battery lifetime in comparison to BAN sensors. Table 1 summarizes the BBN communication standards. The distance from one BAN to another is likely to exceed several hundred meters and therefore short-range and low-power technologies (as mentioned for on-body) are not suitable choices. Consequently, the dynamic range constraint eliminates some candidates for BBN communications including IEEE 802.15.6, IEEE 802.15.4 (ZigBee), IEEE 802.15.4j Medical Body Area Network (MBAN) and IEEE 802.15.4a (ultra-wideband, UWB). As can be seen in Table 1, the eligible standards that can fulfill technical and operational requirements for BBN communications are; WiFi IEEE 802.11a/b/g/n and Bluetooth IEEE 802.15.1 (and Bluetooth low energy, BLE). In comparison to Bluetooth, WiFi is the most relevant to BBN communication for the following reasons: (1) the communication range of WiFi, which is up to 250 m (outdoor), is higher than the range assured by Bluetooth which could reach a maximum of 100 m (under specific conditions for class 1); (2) there are a large number of mobile devices implementing at least one of the WiFi varieties cited above; and (3) bandwidth assured by WiFi is around 150 Mb/s (in case of IEEE 802.11n) and could even reach 500 Mb/s (IEEE 802.11 ac), compared to the hundreds of Kb/s offered by Bluetooth.  
Furthermore, Bluetooth low energy (BLE) and WiFi are expected to provide short range coverage from 100 to 200 m with a throughput range from a few Mbps to hundreds of Mbps. BLE or Bluetooth Smart technologies are strong candidates for the BBN communications, however, they remain limited in terms of range (100 m theoretical) as well as low transmission power and therefore they are high power interference-sensitive. On the other hand, recent variants of the WiFi standard (i.e., IEEE 802.11n, IEEE 802.11 ac, etc.) offer provisions to operate in multi-band frequencies (i.e., 2.4 GHz and 5 GHz). Additionally, using WiFi, devices could operate for more than 10 h with one battery. Thus, WiFi could be considered as a pertinent candidate for future BBN communications.  
Indeed, these above statements were proved through recent extensive simulations in [11], where it was concluded that WiFi IEEE 802.11 has the best performance in the considered application (disaster relief networks) compared to ZigBee and Wireless Body Area Network (WBAN). Furthermore, a recent experiment [12] validated WiFi IEEE 802.11n as a BBN communication protocol for the disaster relief applications despite the limitations detailed in Section 4. To sum up, WiFi IEEE 802.11 standard in general remains the most appropriate technology for BBN communications.