esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Contribution of the Proposed Method  
Two critical scenario components were extracted using the proposed method: dynamic occlusions and changes in traffic lights. Other studies have also highlighted the importance of considering dynamic occlusion in traffic scenarios [30; 33], which indicates that the proposed method can extract reasonable factors. In [30], the authors mentioned dynamic occlusion as a factor that will make the scenario more challenging and explained the mechanism to express dynamic occlusion in a scenario. In [33], the authors claimed that the accident data used to identify the scenarios lacked information about the obstruction in the visual field owing to obstacles, including dynamic occlusions. Both highlight the importance of considering dynamic occlusions based on the knowledge that occlusions lead to accidents. However, the selection of factors based on prior knowledge may not be feasible in unknown or complex scenarios. Compared with the others, the results of the proposed method show the importance of considering this factor with evidence. Therefore, it is suggested that the proposed method can contribute to explicitly extracting critical scenario components in urban environments if driving data on near-miss incidents or accidents can be obtained.  
The problems faced by autonomous-type ADSs are similar to those faced by a human driver. ADSs are superior to human drivers in terms of object detection range, accuracy, and parallel processing power, and they can achieve higher driving performance. However,because autonomous-type systems and drivers are similar in observing the surroundings around the vehicle and controlling only the vehicle itself, they share the same limitations. Information from other sources is necessary to solve the problems caused by these limitations. Thus, cooperative-type systems, which can obtain information from other sources (e.g., surrounding vehicles and infrastructure), are essential for implementing safe ADSs. The methodology proposed in this study, which utilizes driving databases of human drivers and extracts critical environmental factors based on database analyses, is considered useful for establishing the requirements for cooperative-type systems.
## 3 Results and Discussion
### Effect of Soil Type  
#### 3.1.1 Horizontal Movement of the Soil Nailing Slope  
The effect of the soil type on the horizontal movement of the slope face of the nailed soil system during the construction stage and loading stages is shown in Figure 4a. It is clear from the figure that the inclusion of reinforcement reduces the horizontal movement of the slope face. The figure also depicts that the horizontal movement of the slope in the middle third is higher than the slope top and bottom levels, and it has the lowest value at  
Figure 3: Laboratory model with different soil and building foundation parameters; (**a**) section and elevation of nailed soil slope model; (**b**) 3D model of nailed soil slope (all dimensions are in mm).  
the top of the slope. This is attributed to the boundary condition of the soil slope. The top level of the slope with foundations and bottom portion will restrain the movement of the slope face.  
Figure 5 shows the effect of the soil type on the horizontal movement of the slope face during the construction stage and loading stages of varying foundation pressure. It can be inferred from the results that the increase of the soil density reduces the lateral movement of the soil-nailed slope. Reduction in the slope movement may be because the friction between soil and reinforcement is strongly dependent on the density of the soil and restrained dilatants behavior in dense granular soil. The lateral movement of the slope face of the soil with 69% relative density is less than half of the lateral movement of the slope face of the soil with 35% relative density at higher foundation pressure.  
Figure 4: Influence of soil density on the horizontal movement of slope face in construction and loading stages; (**a**) loose sand; (**b**) medium sand; (**c**) medium sand.
# An Experimental Study on Repairing of Reinforced Concrete Beams Having Damaged Longitudinal Bars
## 1 Introduction
In the NSM approach, the surrounding concrete serves as a protective layer for the NSM bars or strips, effectively shielding them from environmental, thermal, and mechanical damage. Consequently, this protective mechanism contributes to delaying or preventing premature failure of the reinforced structure [21]. Numerous studies have investigated the flexural and shear properties of RC beams strengthened with NSM bars or strips composed of FRP materials, demonstrating that these strengthening techniques significantly enhance the structural performance [34; 35; 36; 37; 38]. However, the concrete cover in the reinforced concrete element may not be sufficient for NSM application. In this reinforcement technique, a sufficient spacing distance is required to create grooves without harming existing reinforcement. Furthermore, the beam width should be available to provide at least a minimum spacing between  
Figure 1: Damage to RC beams due to service openings. the NSM bars [34; 39; 40]. To overcome this problem in this study, the side-near-surface mounted (SNSM) technique was used [41]. In this paper, the behavior of cutting the tensile reinforcement in RC beams is discussed and the effects of different repair methods on the flexural behavior of the beams were examined. The aim of this study is to contribute to the growing body of knowledge regarding the effectiveness of various repair techniques for reinforced concrete beams, particularly in the context of enhancing structural integrity and performance post-damage. By systematically evaluating the flexural behavior of beams repaired with different materials and methods, this research seeks to identify optimal strategies for restoring and improving the load-carrying capacity of compromised structures. This research not only aims to provide empirical data on the performance of repaired beams but also aspires to establish a framework for future investigations into innovative repair methodologies that can address the challenges posed by aging infrastructure and environmental degradation. The damaged beams were repaired using the NSM method, SNSM method, and EBR method using both steel and GFRP rebar. The results of the load-carrying capacity, rigidity, failure behavior, and energy consumption capacity of the repaired reinforced concrete beams were compared with each other.
# Blockchain Based Delay and Energy Harvest Aware Healthcare Monitoring System in WBAN Environment
## 1 Introduction
### Research Contributions  
This work mainly focuses on energy efficient health monitoring with low delay and high security. The major contributions of this paper are sorted as follows,  
* We deployed dual sinks such as emergency sink and periodic sink to collect the data without congestion. All critical packets are forwarded to the health center without any delay.
* All patients who need to monitor healthcare are registered to the key management server (_KMS_) using the extended PRESENT algorithm. Clustering is generated using the best objective function (BOF), which is computed by node residual energy, transmission power, bandwidth, and signal to noise ratio (SNR) via spotted hyena optimizer (SHO).
* Faulty data packets are removed in the sink with the use of analyzing the records and it results in low bandwidth and energy consumption. If the CH is drained of its potential to transmit the data, then the next node is selected among the patient body sensors.
* The idle channel is used for emergency packet transmission and selects the optimum channel for the remaining two medical packet types by considering RSS, SNR, channel capacity and radio power. Hence, interference is eliminated in this step.
* We transfer each normal data packet via multihop transmission, which considers packet size, data traffic type, _TTL_, required delay and data rate. A multi-hop data routing using best forwarders selection by considering node residual energy, RSS, path duration and distance to the CH.
* We sense and classify the environment and healthcare data for each patient using the deep reinforcement learning algorithm. We proposed two stream-DNN in DRL, which outperforms the conventional Markov model.
* We propose a PRESENT algorithm for encryption of medical data and critical data directly transmitted to the CH and then forwarded to the PBA. Finally, data packets are stored in the blockchain and cloud servers based on packet sensing type.
## 2 Methods  
### Historic NDVI Observations  
Broadband satellite sensors, like the NOAA Advanced Very High Resolution Radiometer (AVHRR), provide some of the most valuable long-term observations for monitoring the Earth surface, especially the biosphere. In particular, the AVHRR-derived Global Inventory Modeling and Mapping Studies (GIMMS) dataset provides the longest and most consistent set of observations: its recent version (NDVI3g) is the first to exceed thirty years (1981-2011). The raw data has been thoroughly corrected for orbital drift, clouds and atmospheric aerosols, among others [20], and processed into the common normalized difference vegetation index (NDVI)--a normalized ratio between red and near-infrared reflectance ranging between 0 and 1 for vegetated areas [21]. As the acquisition started in July, we excluded the half year of 1981 and used exactly thirty years composed of 720 fortnightly observations in our analysis. A few other post-processing steps were followed as described below.  
Atmospheric conditions influence the quality of optical remote-sensing data. The GIMMS data, despite the corrections and temporal compositing, still contains residual invalid measurements, well indicated using quality flags. Based on the quality flags, we only considered pixels with at least 75% valid data points (_i.e_., flag value zero) through the whole time span. Using this threshold, parts of the tropics and most land surfaces above 65 degrees latitude were excluded from the analysis. Furthermore, we masked pixels with a median NDVI value lower than 0.1, in order to exclude non-vegetated areas. Before applying masking steps, we aggregated the data into monthly observations, using the maximum-value method [22], which is similar to the method used in GIMMS. This manipulation further reduced cloud and other noise effects, but would have influenced the extraction of phenological metrics or the accuracy of short-term trend estimates. However, our interest in large-scale and long-term processes justifies the temporal aggregation. This was supported by a test on a spatial subset of the data, which confirmed that the aggregation did not alter the trend retrieval accuracy as compared with non-aggregated data (results not shown).
# Fault Controls on Hydrocarbon Migration--An Example from the Southwestern Pearl River Mouth Basin
## 3 Data and Methods
### Methods
#### 3.2.2 Evaluation of Fault Stability  
Geomechanical analyses using the slip tendency and fracture stability approaches were carried out to evaluate fault stability and the risk of vertical fluid flow. Slip tendency (Ts) is a method used to quickly assess the relative likelihood of a fault surface experiencing slip under the current effective stress field. It is determined by the ratio of the resolved shear stress (\(\tau\)) to resolved normal stress (\(\mathrm{\sigma_{n}}\)) acting on the fault surface [63]. The slip tendency is expressed as follows:  
\[\mathrm{T_{s}}=\frac{\tau}{\mathrm{\sigma_{n}}} \tag{6}\]  
If Ts is equal to or greater than the coefficient of static friction (\(\mathrm{\mu_{s}}\)), which is typically assumed to be 0.6 [64; 65], it indicates that slip is more likely to occur on the fault surface. The slip tendency method does not explicitly require the input of fault properties for modeling, but rather relies on the assumption of a friction coefficient. On the other hand, fracture stability (\(\mathrm{F_{s}}\)) refers to the increase in pore pressure necessary to reduce the effective stresses to a point where a fault plane experiences shear, tensile, or hybrid failure [66]. Modeling fracture stability requires the consideration of failure envelopes, which necessitates the input of known rock properties, such as cohesion and the angle of internal friction. This approach explicitly requires the knowledge of the fault rock composition for accurate modeling. Both of fault lateral and vertical sealing analyses were conducted with the TrapTester(r) 7.2 software.
## 4 IoT-Cloud-Based EWS
[89] gave a table for ten different IoT disaster management systems based on several properties, such as IoT architecture ownership, cloud-enabled, computer technology area, main focus, and disaster type. They also classified IoT-based disaster recovery systems into four groups: Service-oriented, natural, artificial, and post-disaster. They compared IoT-enabled disaster management methods according to their wireless communication technologies, sensor types, and some additional features. They also put a comparison diagram exhibiting that Bluetooth and Wi-Fi were the best cost, and Bluetooth and ZigBee had the best power usage among the IoT communication technologies. The authors proposed case studies for forest fire detection and EEWS based on IoT devices. For the earthquake warning part, they mentioned the usage of Vibration Sensors (Accelerometer), PIC (Peripheral Device Controllers), ZigBee communication procedures, LCD monitors, and RS232 cables. An IoT alarm message was sent to smartphones, and an alarm message via GSM standard was sent to other cell phones. [PERSON] et al. [193] have optimized a schedule for distributing relief items using IoT technologies, such as smart cities. There are some dynamic features dependent on the disaster conditions, such as changing relief demand and resource availability. IoT is suitable for such issues related to continuously flowing and dynamically changing data [194]. The authors symbolized different time periods with sliding time windows in which the data update occurs. For the first window, relief distribution is decided according to the availability of vehicles, relief resources, priority of the disaster area, and delivery routes. The distribution schedule has been optimized repeatedly in the next time slots. The fragility of the problem that is being targeted, as well as its direct effect on human life, makes it imperative that a solution be found that is intelligent, trustworthy, and flexible despite the considerable efforts that have been put into developing the state-of-the-art. In this section, we throw light on the primary research explorations that have been done in this area. The primary efforts in developing IoT for the EEWS are outlined in detail in Table 3.  
In order to prevent the loss of human life, the implementation of an EEWS is an absolute necessity. In order to effectively manage disasters and reduce the danger of earthquakes, it is essential to have the ability to promptly detect the features of an earthquake. With technologies already in place, such as the IoT network, social media, global positioning system (GPS), and mobile nodes, these attributes can be sent to help mitigate the effects of a catastrophic earthquake. Figure 8 depicts a comprehensive EEWS with many administrations assisting in relieving the earthquake tragedy. The EEWS will include complete statistics regarding hospitals, railways, fire services, ambulances, airports, and so on based on these administrations. This proposed system does integrate social media, IoT technologies, cloud systems, and
## 6 Discussion  
We examined the effectiveness of a surgical navigation system for transsphenoidal pituitary surgery developed by applying U-Net and bendable devices. Experimental results indicated that U-Net was useful for ICA and optical nerve segmentation by training CTA images. It was also noted that U-Net training results were not only applied to segmentation but also to the surgical navigation system for transsphenoidal pituitary surgery. By performing experiments, we demonstrated that applying the U-Net training results to surgical navigation, we could reduce the possibility of the surgical tool approaches vital organs. Reducing the possibility of surgical instruments accessing vital organs would reduce the complications during TSA surgery. We also showed that using our proposed bendable endoscope and bendable surgical tool, we could assist surgeons in easily checking previously blind regions. We had conducted experiments with other patients' phantoms. The results for other patients' phantom experiments are given in Appendix A.  
However, there are more technical challenges to be overcome. The degree of bending in the proposed bendable device was not updated in the navigation system. To enhance this challenge, future work will involve incorporating a fiber Bragg grating sensor to measure the bending angle of the proposed bendable device, enabling the bending angle to be updated in the navigation system [36]. We also plan to apply the proposed navigation system and bendable devices to actual patient after receiving approval from Korean FDA.  
Improving the quality of the camera in the bendable endoscope is another technical challenge. In this research, while a camera with HD resolution was used in the bendable endoscope, a higher quality bendable camera would make operations easier for the surgeon, through having better visibility.  
In additional future work, we plan to apply a point cloud registration method, to avoid unnecessary contact between the patient and the matching tool, by using a 3-D scanner instead of PPR. In addition, we will add an augmented reality function to the navigation system, to overlay important organ locations on the camera view of the bendable endoscope.  
Finally, in order to improve vital organ automatic segmentation performance, other neural networks--such as Nifty Net [37] or Deep Lab v3 [38]--will be tested in an effort to provide improved neural network prediction performance.
## 2 Multiaxial Fatigue Assessment Method
### Evaluation Procedure  
The fatigue-life evaluation and assessment procedure, which is shown in Figure 1, is presented as follows:  
1. A multiscale finite-element model of a short hanger's connection to a bridge deck was created according to the design profile of the Dashengguan Yangtze River High-speed Railway Bridge, where a substructural technique was used to link two levels of finite element models, i.e., the sparse whole-bridge model and defined connection model. Then, the multiple-dimensional finite-element model was calibrated and validated by comparing its calculation results to the field motoring data.
2. According to the multiscale finite element calculation of the tensile principle stress, the position of the maximum value was first obtained and validated under the action of high-speed trains. By mechanical analysis of the element at this position, the critical damage plane was confirmed to be perpendicular to the direction of the tensile principal stresses. Then, histories of the normal stress and shear stress on the critical plane of fatigue failure were calculated and obtained for further fatigue-damage evaluation and assessment.
3. The stress amplitude spectra of these two histories were obtained by rain-flow processing. Then, the spectrum of the equivalent stress amplitude under loads from high-speed trains was calculated according to Equation (3). Furthermore, the fatigue life in the form of the stress cycle number for each component of the spectrum was calculated by Equation (2). Miner's rule was introduced to calculate the fatigue damage contributed by each component of the spectrum, by which the total fatigue damage of each train was calculated by adding them together. Finally, the fatigue life of the connection was calculated and validated when the accumulation value of fatigue damage reached 1.0.  
Figure 1: Fatigue-life evaluation and assessment procedure.
# Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
## 2 Dataset  
In this research, three datasets, namely, Pavia University Dataset (PUD), Salinas Dataset (SD), and Indian Pines Dataset (IPD) are used. The PUD was compiled over Pavia, northern Italy, though an optical sensor called a reflective-optics-system imaging-spectrometer (ROSIS). PUD images have a spatial resolution of 1.3 m with 103 spectral channels of size 610 \(\times\) 610. There are nine classes in PUD.  
High resolution spatial imagery (3.7 m) is a feature of the Hybrid-sparsity and Statistics-Detector (HS-SD), which was acquired by the 224 band AVIRIS camera over the Salinas Valley in California. The covered region consists of 217 samples along 512 lines. Twenty water absorption bands, including 108-112, 154-167, and 224, were discarded. There were only at-sensor radiance data for this photograph. Vegetables, barren soils, and grape fields are all included. Sixteen categories are included in the Salinas ground truth. Indian Pines is a dataset for segmenting HSI. The imagery of the source dataset contains 145 \(\times\) 145 pixel HS bands covering a specific region in Indiana, United States. The data collection includes 220 reflectance bands, or wavelength ranges, for each pixel that represent various wavelength ranges of the electromagnetic-spectrum. There are a total of 16 classes in IPD. For visual understanding, Figure 1 illustrates the ground truth of all three datasets.
# Low Discrepancy Sparse Phased Array Antennas
## 2 Sampling Points on a Planar Aperture  
Assume a uniformly weighted \(N\)-element planar array lies in the x-y plane bounded by \(0\leq x\leq x_{\text{max}}\) and \(0\leq y\leq y_{\text{max}}\). The array factor is given by  
\[AF(u,v)=\sum_{n=1}^{N}e^{-jk[x_{n}u+y_{n}v]} \tag{1}\]  
where \(k=2\pi/\lambda\), \(\lambda\) is the wavelength, \((x_{n},y_{n})\) is the location of element \(n\), \(\theta\) and \(\phi\) are the elevation and azimuth angle, respectively, and \(u=\sin\theta\cos\phi\), \(v=\sin\theta\sin\phi\). We note that for a planar array, regular sampling takes the form of a rectangular lattice, triangular lattice, or concentric ring array. In this paper the rectangular lattice serves as the reference, where the grating lobes for a rectangular lattice appear at [1]  
\[\begin{array}{l}u_{m}=u_{s}+m\lambda/d_{x}\text{ for }m=0,\pm 1,\pm 2,\dots,\\ v_{n}=v_{s}+n\lambda/d_{y}\text{ for }n=0,\pm 1,\pm 2,\dots,\end{array} \tag{2}\]  
where \(u_{s}\) and \(v_{s}\) are the main beam location in the sine space.