esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The AMC and Antenna Designs
### The Proposed Circular Sector-Shaped Dipole Antenna with AMC  
Figure 3(a) illustrates the shape of a circular sector-shaped dipole antenna combined with a meandered arm. A circular sector-shaped dipole antenna, which is combined with eight meandered arms is symmetrically positioned on the top and bottom surfaces of a \(500\times 500\) mm\({}^{2}\) FR4 substrate with a dielectric constant of 4.3, a thickness of \(t_{s}=1.52\) mm, and loss tangent of 0.025. The elements of the dipole antenna located on the upper and lower surfaces are indicated by gray and white, respectively. The antenna was fed through a coaxial cable.  
Figure 3(b) shows the shape of the proposed AMC structure. The AMC structure consists of 25 unit cells, and the size is \(500\times 500\) mm\({}^{2}\), which is the same as the dipole antenna. A conductor with a radius of \(r_{g}=\)1.5 mm is removed to prevent a short circuit  
Figure 3: Design concept of the proposed antenna: (**a**) meandered arm; (**b**) circular sector-shaped dipole antenna; (**c**) optimized meandered arm, \(A_{1}\).  
between the AMC unit cell and the coaxial cable, which is used for feeding. Figure 4c shows a side view of a circular sector-shaped dipole antenna combined with an AMC structure. The gap between the circular sector-shaped dipole antenna and the AMC structure is 1 mm.  
To check the performance based on the presence and shape of the meandered arm, three antennas were simulated (Figure 5). All antennas are positioned symmetrically on the top and bottom of the FR4 substrate and combined with a \(5\times 5\) AMC structure.  
Figure 6 presents the simulated VSWR characteristics of three types of antennas with and without meandered arms. If the meandered arm structure does not exist, it partially fails to satisfy the VSWR characteristic of 3.5:1 or less in the HF, VHF, and UHF bands. The path through which the surface current can flow is increased by adding a stripe line to a reference antenna [27]. This caused an additional resonance mode, resulting in the improvement of antenna matching characteristics in the high-frequency band, as shown in Figure 6. By adding the strip arm, not only the path through which the surface current can flow increases, but also the wideband characteristics of the antenna can be realized due to the additional mutual coupling with the AMC [16]. The stripline was optimized in a meander shape to improve the antenna's operating bandwidth characteristics; thus, satisfying the VSWR of 3.5:1 or less in the HF, VHF and UHF bands of 14-371 MHz.
## 5 Evaluation of STAR COSMIC-2 Wet Profile Retrievals through Comparison with CrIS and ATMS
### Comparisons with CrIS Measurements Using CRTM and Double-Difference Method
#### 5.1.2 Comparison Results
The spectral  
Figure 15: (**a**) The spectral dependence of BT differences (\(\Delta\)BT\({}_{\text{C2-CrIS}}\)) between CRTM-simulated STAR COSMIC-2 BT and NOAA-20 CrIS measurements. (**b**) Peak weighting function position (in hPa) over the corresponding CrIS spectral channels. shift for CrIS is less than \(\pm\)3 ppm using the neon-based spectral calibration reference subsystem within the CrIS instrument (see [45; 46]), and the radiance differences compared with other hyperspectral IR sensors, e.g., atmospheric infrared sounder (AIRS) and the infrared atmospheric sounding interferometer (IASI), are within 0.1 K for the majority of CrIS channels [48]. Therefore, we can use CrIS as the reference sensor to evaluate the inter-consistency among the six RO sensors of COSMIC-2 (see below). The BT differences between each COSMIC-2 sensor (E1-E6) and CrIS measurements are defined as \(\Delta\)BT\({}_{\text{C2(En)-CrIS}}\) for \(n=1\) to 6. Here we can use the double-difference method to evaluate the relative differences among the six COSMIC-2 sensors over the SWIR bands of interest. In our double-difference analysis, a NOAA-20 CrIS was used as the radiometric transfer sensor. The temperature and humidity profiles from each of the six COSMIC-2 sensors were collocated with the NOAA-20 CrIS measurements. We first calculate the BT difference, \(\Delta\)BT\({}_{\text{C2(En)-CrIS}}\) for \(n=1\) to 6, between RO sensors (C2(E1-E6)) and CrIS over the CrIS SWIR channels. Then, the \(\Delta\)BT\({}_{\text{STAR/C2(E1)-CrIS}}\) differences are used as the references for the second BT-difference calculation. The inter-RO sensor bias between E1 of COSMIC-2 and the other RO sensor (E2 to E6 of COSMIC-2) are  
\[\Delta\text{BT}_{\text{STAR/C2(E1)-C2(En)}}=\Delta\text{BT}_{\text{STAR/C2(E1)- CrIS}}-\Delta\text{BT}_{\text{STAR/C2(En)-CrIS}}, \tag{6}\]  
for \(n=2\), 3, 4, 5, 6. In our double-difference comparison, both day and night collocated COSMIC-2 and CrIS data are simulated and compared. Figure 16 shows the NOAA/STAR COSMIC-2 WetPrf data evaluation by deriving the spectral BT differences \(\Delta\)BT\({}_{\text{STAR/C2(E1)-STAR/C2(En)}}\) between the COSMIC-2 E1 dataset and the other five COSMIC-2 RO sensors. It depicts that the STAR WetPrf-derived BT difference between five COSMIC-2 (E2-E6) sensors and COSMIC-2 E1 sensor over the spectral ranges below 2275 cm\({}^{-1}\) and above 2375 cm\({}^{-1}\) (with peak-sounding heights varying from near-surface to 100 hPa) are primarily within \(\pm\)0.1 K. Over the strong CO\({}_{2}\) absorption band from 2275 cm\({}^{-1}\) to 2375 cm\({}^{-1}\), the BT difference is within \(\pm\)0.175 K. This analysis indicates consistent precision among six COSMIC-2 RO sensors.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 4 Applications
### Remote Patient Monitoring  
Remote patient monitoring Figure 2 involves tracking a patient's vital signs via wearable devices and remote monitoring tools, and alerting healthcare providers to any concerning changes [63]. This is particularly beneficial for patients requiring continuous monitoring, such as those unable to care for themselves in a healthy state or needing constant observation due to health or age-related concerns [64].  
One notable example is the use of smartwatches and fitness trackers to monitor heart rate, blood pressure, and oxygen saturation in real time. These devices collect data continuously and send alerts to healthcare providers if they detect any abnormal patterns, such as irregular heartbeats or sudden drops in oxygen levels. This allows for timely intervention, potentially preventing severe health complications [65]. Another example is the use of continuous glucose monitors (CGMs) for diabetic patients. CGMs track blood glucose levels throughout the day and night, providing real-time data that can be shared with healthcare providers. This continuous monitoring helps in adjusting medication and dietary plans more accurately, improving overall diabetes management [53]. Additionally, remote patient monitoring systems can integrate with home-based devices like digital scales and blood pressure monitors. These devices automatically record and transmit data to a centralized system, where AI algorithms analyze the data for trends and anomalies. For instance, in patients with heart failure, sudden weight gain might indicate fluid retention, prompting an immediate review by healthcare providers [66].  
They integrate DL to analyze data collected from devices and tools, and NLP to analyze patient feedback and electronic medical records [67]. Implementing remote patient  
Figure 2: The overview of remote patient monitoring.  
monitoring requires not only the aforementioned AI technologies but also cloud computing for storing and sharing collected data and analyzing trends, fog computing and edge computing to bring cloud services closer to data collection devices, and blockchain technology to ensure the security and privacy of the entire system [65].
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 5 Methodology and Setups
### Simulation Environment  
Contiki Cooja serves as a comprehensive simulation platform designed explicitly for the Contiki operating system, a key player in the Internet of Things (IoT) landscape [18]. With its primary focus on IoT simulation, Cooja provides developers with a powerful tool to assess and refine their Contiki-based applications before real-world deployment. The platform boasts a range of features, including support for Contiki OS, emulation of resource-constrained IoT devices, and a graphical user interface that enables the creation and visualization of complex network topologies. Cooja facilitates multi-node simulations, allowing developers to test communication protocols and routing algorithms in a controlled virtual environment. Furthermore, it offers sensor node emulation, with models replicating various sensors found in typical IoT devices. Energy consumption modeling is a critical aspect, allowing developers to analyze and optimize their applications' energy efficiency. Real-time debugging tools also assist in identifying and resolving issues during runtime. Cooja's extensibility is evident through its support for plug-ins, allowing developers to customize the simulation environment. Widely adopted in both academic and industrial settings, Contiki Cooja plays a crucial role in IoT research and development. As an open-source platform, it encourages community contributions and enhancements, ensuring its continued relevance and effectiveness in the dynamic IoT landscape [19].
# Slicing on the Road: Enabling the Automotive Vertical through 5G Network Softwarization
## 3 5G-V2X Network Slicing Architecture  
In this section, we present the proposed 5G-V2X network slicing high-level architecture. Although there is no single design available of a unified framework that supports the network slicing concept [5; 13; 28; 32], there is a wide consensus on a _three-layer_ model [28] plus an additional management and orchestration layer, which neatly identifies the involved players and domains. Without loss of generality, our proposal builds upon the model described in [28], which includes the following layers: the Infrastructure layer, the Service layer, and the Business layer. The three layers encompass components that are aligned with the 5G service-based architecture and its key enablers, i.e., SDN, MEC, and NFV. Within the conceived general design of the three-layer architecture, we customize specific functionalities and procedures to support V2X slicing, based on the guidelines we identified in [25], and summarize in Table 1.
## 2 Experimental Scheme
### Testing the Confining Pressure and Parameter Setting  
The confining pressure during the triaxial compression test of the sample was determined according to the burial depth conditions of the storage cavern site. Groundwater-sealed storage cavern reservoirs were selected in coastal granite areas with stable groundwater levels where the surrounding rock integrity was relatively good and the surrounding rock quality grade was generally high. According to the in-situ stress measurement results of the water pressure fracturing method in a typical water-sealed storage cavern site built in China and through the tensor analysis and linear regression analysis, the fitted formulas of principal stress and elevation are as follows (the compressive stress is negative; \(\sigma_{H}\) is the maximum principal stress and the unit is MPa; \(\sigma_{h}\) is the intermediate principal stress and the unit is MPa; \(\sigma_{v}\) is the minimum principal stress and the unit is MPa; \(h\) is the elevation and the unit is m):  
\[\left\{\begin{array}{l}\sigma_{H}=0.0347h-7.2014\\ \sigma_{h}=0.0192h-4.9748\\ \sigma_{v}=0.026h-1.82\end{array}\right. \tag{1}\]  
Confining pressure is set by the burial depth of the storage cavern. The maximum burial elevation of the typical water-sealed storage cavern built in China is \(-146\) m, so \(h\) is equal to \(-146\) m. According to the above fitting formula, \(\sigma_{H}\) is equal to \(-12.3\) MPa, \(\sigma_{h}\) is equal to \(-7.8\) MPa, and \(\sigma_{v}\) is equal to \(-5.6\) MPa. Considering the access tunnel with a shallower burial depth and the shaft with a deeper burial depth, the confining pressure range is appropriately expanded. The final confining pressures are \(0\) MPa, \(5\) MPa, \(10\) MPa, \(15\) MPa, and \(20\) MPa.  
Figure 3: Curve diagram of P-wave velocity of sample changing with water immersion time.  
Figure 2: Diagram of sample water absorption changing with immersion time.  
A total of 15 samples were selected for triaxial compression test research and divided into five groups according to different confining pressures, with three samples in each group. The instrument used in the triaxial compression test was a TAJW-2000 rock mass multi-field coupling triaxial test system (Figure 4). The stress control method was used to increase the confining pressure at a rate of 0.05 MPa/s until the specified confining pressure [34]. After the confining pressure was stabilized, the specimen was loaded to failure at a displacement rate of 0.003 mm/s.
## 3 Results and Discussion
### Effect of Varying Crude Oil Grades on Slug Characteristics
#### 3.2.3. Slug Translational Velocity  
The effect of varying crude oil grades on slug translational velocity is presented in Figure 13. The slug translational velocity for high-density crude oil is higher than that for low-density oil. This difference can be explained by considering the mass per unit volume of liquid slug. As shown in Figure 12, higher density crude oil has shorter slug length. Although the slug length decreases, the decrease is larger than the increase in oil density. Hence the mass of the slug for higher density oil is less than the mass of lower oil density which causes the slug translation velocity to increase.  
The variation of velocity with respect to crude oil grade is minimal at a low gas velocity at 0.93 m/s liquid velocity. However, the increase in slug velocity becomes increasingly noticeable when the liquid velocity increases from 0.86 to 1 m/s at 2.44 m/s gas velocity.
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 3 Multi-Objective Optimization Deployment Model
### Obstacle Handling Description  
#### 3.3.1 Approximate Grid-Based Obstacle Handling  
The gridding of obstacles within the monitoring area is subject to two specific assumptions:  
* The locations of obstacles can be obtained in advance.
* The grid size should be significantly smaller than the size of the obstacles, as the grid size directly affects the accuracy of obstacle detection and coverage range estimation.  
The method of gridding obstacles is illustrated in Figure 1. When an obstacle covers an entire grid or when the boundaries of an obstacle fall within a grid, that grid is marked as an obstacle grid.  
Figure 1: obstacle gridization.
## 3 Pattern Recognition of Magnetic Field Data
### Feature Extraction  
In this phase; the size of the magnetic field data was reduced, descriptive properties were determined, and normalization was applied. The feature extraction phase plays the most important role in the system's performance. In this study, the following features related to datasets were extracted.  
* Maximum (Max): The element which has the greatest value in the dataset.
* Minimum (Min): The element which has the smallest value in the dataset.
* Standard deviation (\(\sigma\)): Criterion that is used to quantify the amount of dispersion of a dataset. It is calculated by Equation (1). \[\sigma=\sqrt{\frac{\sum_{i=1}^{n}\left(x_{i}-\mu\right)^{2}}{n}}.\] (1) In the Equation (1), xi is element \"\(i\)\" in the dataset, \(\mu\)is the mean value of the dataset, and \(n\) is number of elemets in dataset.
* The Kurtosis Coefficient (KC): The sharpness of the peak of a frequency-distribution curve. It is calculated by Equation (2). \[KC=\sqrt{\frac{\sum_{i=1}^{n}\left(x_{i}-\mu\right)^{4}}{n\sigma^{4}}}.\] (2)
* The arithmetic mean (AM): The arithmetic mean of the numeric values in the dataset.
## 4 Methodology
### ITM-OSELM Algorithm  
This section presents the ITM-OSELM algorithm. Data are presented to this classifier as chunks in a sequential manner, which have no label once provided. These labels become available after  
Figure 3: ITM-OSELM network and its two sources of updates: external memory EM and transfer learning TL.  
prediction, which permits the sequential training of the classifier. This is a normal OSELM training, although the weights initialization in the OSELM is not fully random. Two sources of information were used: the TL, which is responsible for transferring the weights from previous classifiers; and the EM, which is responsible for providing the weights from the external memory. The old weights must be stored in the EM once a new classifier is created to replace an old classifier. For computational time analysis, the needed time is calculated for the process conducted in each chunk of data. Algorithm 2 exhibits the ITM-OSELM algorithm.  
```
Inputs  Dt = {D0, D1,...} //sequence of labeled data N //total number of features L //number of hidden neurons g //type of activation function Outputs yp //predicted classes Ac //prediction accuracy Starts  activeFeatures = checkActive(D(0))  currentClassifier = initiateClassifier(activeFeatures, L)  currentEM = initiate(N, L) yp = predict(currentClassifier,D(0).x,g)  Ac(0) = calculateAccuracy(yp,D(0).y)  currentClassifier = OSELM(currentClassifier,D(0).x,D(0).y,g) for each chunk D(i) of data [Change,activeFeatures,newActive,oldActive] = checkActive(D(i),D(i-1)) if(Change)  nextEM = EMUpdateEM(currentEM,oldActive)  nextClassifier = transferLearning(currentClassifier,activeFeatures)  nextClassifier = updateNewActive(nextEM,newActive)  currentClassifier = nextClassifier  currentEM = nextEM end yp = predict(currentClassifier,D(i).x,g)  Ac(i) = calculateAccuracy(yp,D(i).y)  currentClassifier = OSELM(currentClassifier,D(i).x,D(i).y,g) endfor1
```  
**Algorithm 2** Pseudocode for the ITM-OSELM algorithm used to calculate accuracy.  
Function checkActive() takes two vectors of feature IDs: the first one in the previous moment and the second one in the current moment. This function has another role of comparing the two IDs to determine which features become active or inactive. The role of EMUpdateEM() is to take the current EM and old active features, and save their corresponding weights in the EM. The role of updateNewActive() is to take new memory and new active features, in order to build and restore their weights from the memory.
# Optical Lensless-Camera Communications Aided by Neural Network
## 3 Lensless Camera Communication System Experimental Platform  
To evaluate the BER performance of our system when used indoors and outdoors, we set up a lensless camera communication system, as shown in Figure 8. The receiver of the system is a lensless camera. To obtain the lensless camera, we used the camera module on the microcontroller, of which the lens is detachable. Then, we used a LED (as sender) to send signals. During the experiments, we made the lensless camera move slowly in a short range and used the lensless camera to capture the images of the LED on the sending end. It can be seen from the simulation results that the BER can meet the communication requirements. Therefore, our decoding algorithm can resist the movement of communication devices.  
Figure 6: The process of establishing the connection.  
Figure 7: The process of adjusting the training sequence. SIFS: short inter frame space; RTC: request to connect; CTS: confirm to send.
## 2 Materials and Methods  
For simulating the mmWave and sub-terahertz wireless channels, an open-source simulator developed by the New York University called NYUSIM is used. The product can simulate many real-life environments such as urban macrocell, urban microcell, indoor hotspot, and rural macrocell. The performance of a simulated communication channel is measured by the Signal-to-Noise Ratio (SNR). The SNR is a measure of the quality of the received signal and is defined as the ratio of the power of the received signal to the power of the noise present in the channel [29]. The data generated using the simulator are then used for the experiments with LSTM and LTC. The results from both are compared to test the hypothesis that LTC achieves better prediction. A mathematical expression is also fitted to the data using Symbolic Regression through genetic programming [30]. The mathematical expression gives insights into the nature of variations in the SNR values.
# Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny
###### Abstract  
This study refines the YOLOv7-tiny model through structured pruning and architectural fine-tuning, specifically for real-time eye state detection. By focusing on enhancing the model's efficiency, particularly in environments with limited computational resources, this research contributes significantly to advancing driver monitoring systems, where timely and accurate detection of eye states such as openness or closure can prevent accidents caused by drowsiness or intentation. Structured pruning was utilized to simplify the YOLOv7-tiny model, reducing complexity and storage requirements. Subsequent fine-tuning involved adjustments to the model's width and depth to further enhance processing speed and efficiency. The experimental outcomes reveal a pronounced reduction in storage size, of approximately 97%, accompanied by a sixfold increase in frames per second (FPS). Despite these substantial modifications, the model sustains high levels of precision, recall, and mean average precision (mAP). These improvements indicate a significant enhancement in both the speed and efficiency of the model, rendering it highly suitable for real-time applications where computational resources are limited.  
eye state detection; driver fatigue monitoring; YOLOv7-tiny; structured pruning; neural network optimization Article
## 5 Data Transmission Tests
### Test String Selection  
As it was claimed earlier on (see Section 3), for the sake of simplicity, a test string was contained within the packet payload transmitted by the end node rather than the actual measurements furnished  
Figure 4: Example of the height of the Earth bulge in transmission links.  
Figure 3: Evaluation of the distance from the horizon.  
by sensors although, in a successive phase of the project, it is expected to include such data encoded into a string.  
According to the LoRaWAN regional regulations [33], the packet payload has a variable maximum length which depends on the exploited SF to broadcast it. In order to limit the number of involved variables within the experiment, the test string was set to have the fixed length of 51 B since it is the minimum amid the aforesaid maximum sizes. Moreover, the aforementioned laws dictate rules on the temporal occupation of the band. Indeed, the adopted ISM band upon which the sensor node transmits signals cannot be utilised more than 1% of the time in order to allow as many devices as possible to make use of it.
## 1 Introduction
### System Model and Problem Formulation  
We consider a UAV wireless communication system where multiple UAVs, each equipped with a single antenna, communicate with the BS equipped with multiple antennas. In this paper, we adopt the LoRa as means of wireless communications between the UAVs and the BS. Additionally, we consider both scenarios of the downlink and the uplink communications from the BS to the UAVs and from the UAVs to the BS, respectively, which are shown in Figure 1.