esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods of Nanoparticles Preparation  
2.2.1. Iron Oxide Nanoparticles (Fe3O4)  
Iron (II) chloride (3.12 g) and iron (III) chloride (4.80 g) were dissolved in DI-water (30 mL) and stirred at 90 \({}^{\circ}\)C under N\({}_{2}\) gas. Then, ammonium hydroxide (20 mL) was added to the reaction mixture and kept under stirring for 150 min. Finally, the solid was separated and washed with water and ethanol.  
2.2.2. Iron Oxide Covered with Mesoporous Silica (Fe3O4@MSNs)  
Iron oxide coated with mesoporous silica (Fe3O4@MSNs) was synthesized according to previously reported studies by [PERSON] et al. [56]. To this end, 0.5 g of Fe3O4 was suspended in 150 mL of DI-water, followed by the addition of CTAB (1.0 g) at 38 \({}^{\circ}\)C. Then, ammonium hydroxide (7 mL) was added to the reaction mixture. A solution containing 5 mL of TEOS and 20 mL of n-hexane was slowly added to the reaction mixture with stirring. After 15 h, the solid was separated and washed with DI-water and ethanol.  
2.2.3. Amino Functionalized Iron Oxide Coated with Mesoporous Silica  
(Fe3O4@MSN-NH2)  
Fe3O4@MSN-NH2 nanoparticles were obtained by suspending 1.5 g Fe3O4@MSN nanoparticles in a solution containing 0.5 mL APTES and 60 mL methanol. The mixture was refluxed for 15 h. The solid was separated and washed with DI-water and ethanol.  
2.2.4. ATRP Initiator Functionalized Iron Oxide Coated with Mesoporous Silica  
(Fe3O4@MSN-Br)  
Fe3O4@MSN-Br nanoparticles were obtained by dispersing 1.0 g of Fe3O4@MSN-NH2 in a solution of 25 mL of DCM and 1.5 mL of triethylamine. To the mixture, 1.2 mL of 2-bromo-2-methylpriponyl bromide was slowly added and stirred for 48 h at room temperature. The solid was separated and washed with DCM and ethanol.  
To extract CTAB, Fe3O4@MSN-Br nanoparticles were dispersed in a solution of ammonium nitrate in ethanol (10 mg/mL) and refluxed overnight with stirring. The solid was separated and washed with ethanol.  
2.2.5. Poly(2-dimethyl aminoethyl methacrylate) (PDMAEMA) Brushes Grafted on Iron Oxide Coated with Mesoporous Silica (Fe3O4@MSN-PDMAEMA)  
In a 25 mL round bottom flask, 0.4 g of Fe3O4@MSN-Br was dispersed in a solution containing 4 mL of ethanol and 1 mL of DI-water and degassed for 15 min. A solution containing 2-dimethyl aminoethyl methacrylate (DMAEMA) (2 mL), 2,2 bipy (0.0067 g), CuBr2 (0.0009 g), ethanol (8 mL), and water (2 mL) were transferred into the reaction mixture and degassed for 15 min. To the polymerization mixture, 0.0076 g of ascorbic acid was added and stirred for 5 h. The solid was separated and washed with 10 mM of HCl solution and ethanol.  
2.2.6. Quaternization of PDMAEMA Grafted on Fe\({}_{3}\)O\({}_{4}\)@MSNs (Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA)  
Fe\({}_{3}\)O\({}_{4}\)@MSN-PDMAEMA (0.5 g) was dispersed in 40 mL of DMF. Then, 0.5 g of 2-iodoethanol was added to the solution under stirring and heated at 120 \({}^{\circ}\)C overnight. The solid was separated and washed with DMF and ethanol.
# FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
## 3 Proposed Method
### Fefssd
#### 3.2.2 Feature Enhancement Module  
In the original SSD, the depths of the earlier layers, such as Conv4_3 and FC7, used for small target objection are very shallow. Therefore, the reason for the poor detection of small targets is that the shallow layers do not have enough semantic information. Motivated by [13; 19], we designed the shallow feature enhancement module for the shallower layers. Specifically, as shown in Figure 3a, we used the two similar branches to deepen the shallow layers. In the left branch, we used \(k\times k\) group convolution followed by \(1\times 1\) convolution, which can broaden the receptive field and learn more non-linear relations. It's worth noting that we resolved the \(k\times k\) convolutional layer into a \(1\times k\) and a \(k\times 1\) convolutional layer for maintaining the receptive field as well as saving the calculation time of the SFE module. The only thing that differs in the other branch is the inversion of the \(1\times k\) and \(k\times 1\) convolution layers.  
In addition, the basic network of the SSD is followed by an auxiliary structure consisting of a series of convolutional layers, which forms a set of feature maps with progressively enhancing the extraction of semantic information and broadening the receptive field. For the auxiliary structure, we think it is not representative enough, and the deeper feature maps may ignore a lot of important details about the input image. In our implementation, we maintained the same cascade structure of SSD, and used the DFE module to replace the first few convolutional layers in the auxiliary structure. The inner structure of DFE module is shown in Figure 3b. We used the basic unit in the Dual Path Networks [20] as our DFE module, which combines the advantages of DenseNet [18] constantly exploring new  
Figure 2: Feature Fusion Module, in which each conv box denotes a Conv + BN + ReLu processing. (**a**) 3-Way FFM is inserted in the Conv4_3, FC7, Conv6_2, Conv7_2, and Conv8_2 of the feature pyramid structure and (**b**) 2-Way FFM is inserted in the Conv9_2.  
features and ResNet [19] implicitly reusing features, so more details of the input image can be obtained. The proposed Deep Feature Enhancement in Figure 3b can be expressed as Equation (2):  
\[\begin{split}\text{DFE}_{output}&=concat(\text{Res}(x ),\text{Dense}(x))\\ \text{Res}(x)&=add(\lambda_{1}(x),\delta(x))\\ \text{Dense}(x)&=concat(\lambda_{2}(x),\delta(x)) \end{split} \tag{2}\]  
where \(x\) is the feature maps input into the DFE module, \(\lambda_{1}(\cdot)\) and \(\lambda_{2}(\cdot)\) represent a BN + Relu + \(1\times 1\) convolutional layer operation, and \(\delta(\cdot)\) is the main operation of deep Feature Fusion Module.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 4 Localization Algorithms
### Trilateration  
Trilateration, or multi-lateration as it is more properly called in the general case where N references are available, is an algorithm that estimates the position of the target based on geometric properties.  
As before, RSSI measurements are collected and converted to distances d\({}_{i}\), at which point the target position is obtained as the result of a non-linear system that defines a circumference for each Anchor:  
\[(x-x_{i})^{2}+(y-y_{i})^{2}=d_{i}{}^{2} \tag{7}\]  
where \(d_{i}\) is the radius of the circumferences and \((x_{i},y_{i})\) the center of the circumferences, as shown in Figure 6. The intersection of the circumferences locates the position of the target. In case of three Anchors, this results in the following system of equations:  
\[(x-x_{2})^{2}+(y-y_{2})^{2} =d_{1}{}^{2}\] \[(x-x_{2})^{2}+(y-y_{2})^{2} =d_{2}{}^{2}\] \[(x-x_{2})^{2}+(y-y_{2})^{2} =d_{3}{}^{2} \tag{8}\]  
However, because of inevitable measurement errors in the distance estimation, the three-Anchor trilateration system can only be solved exactly theoretically. This is because noise perturbs the three circles, making it impossible for them to intersect exactly, which means that the estimated target position is almost never obtained as the intersection of circumferences, but falls inside an estimated area represented with a grid in Figure 7.  
Figure 6: Position detected by system of three Anchors (r\({}_{1}\) equivalent to d\({}_{i}\)).
## 2 Materials and Methods
### Wireless Electroencephalograph  
The EEG acquisition system consisted of two modules: The first module, which was embedded in the animal, concerned the recording, conditioning, and wireless transmission of the EEG signals. The second module, which was called the base module, received data from the embedded module and connected the system to a computer [54].
## 4 Performance Evaluation  
### Experimental Setup  
The proposed models were built in a hardware environment comprising Intel (R) Core (TM) i7-10510U CPU @1.80 GHz. The workstation's operating system was Windows 10. The integrated development environment and the deep learning symbolic library were PyCharm-Python 3.6 and PyTorch 1.10.2, respectively. Graphics processing units (GPUs) are well-suited for deep learning tasks due to their parallel computing capabilities, which allow them to perform matrix multiplications and other computationally intensive operations much faster than CPUs. Renting GPUs from public cloud providers, such as the RTX A4000 and RTX 2080 Ti, is essential in accelerating the training process for deep learning models.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 4 Results and Discussion
### Visualization Examples  
As one of the main goals of this research is to improve the viewing experience of POI overlays, we also demonstrate that the recognition and tracking data can effectively be used to generate more dynamic visualizations. Therefore, an integration is made with the Vizrt visualization engine in order to test our proposed solution end to end. Figure 14 shows the result of this integration with two visualized examples where the name is anchored to the POI itself.
# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 2 Preliminaries  
### Atmospheric Scattering Model  
In the field of computer vision, atmospheric scattering models are often used to describe the formation formula for hazy images, as shown in Equation (1).  
\[I(x)=J(x)t(x)+A(1-t(x)), \tag{1}\]  
where \(x\) is the pixel position, \(I(x)\) is the hazy image, \(J(x)\) is the clear image, \(A\) is the global atmospheric light, and \(t(x)\) is the transmission map described by Equation (2). \(J(x)t(x)\) is called direct attenuation, and \(A(1-t(x))\) is called airlight.  
\[t=e^{-\beta d(x)}, \tag{2}\]  
where \(\beta\) is the scattering coefficient of the atmosphere and \(d(x)\) is the distance between the camera and the observed objects. According to Equation (2), the larger the distance, \(d(x)\), the lower the transmittance. From the direct attenuation term, \(J(x)t(x)\), a clear image becomes blurred and unclear for a larger \(d(x)\) due to the atmospheric light, which makes the image brighter and whiter.  
The single-image dehazing problem is an ill-posed problem because it requires both global atmospheric light and transmission maps to be obtained simultaneously. Therefore, statistical methods for obtaining empirical rules are necessary for model-based approaches.
# Mobile Broadband Performance Evaluation: Analysis of National Reports
## 3 Case Studies
### Broadband Deployment Report: USA  
_Regulator_\(\rightarrow\) The Federal Communication Commission (FCC) has published a yearly broadband deployment report on the quality of communication service for US consumers since the beginning of the 21 st century. The 2020 report has a five-year evaluation from 2014 to 2018 included [10].  
_Operators_\(\rightarrow\) In the US there are four prominent operators providing mobile communication services: Verizon, AT&T, Sprint, and T-Mobile.  
_Metrics_\(\rightarrow\) To assess the deployment of advanced telecommunication for mobile services, which can reach a 5 Mbps download speed and 1 Mbps upload speed (5 Mbps/1 Mbps), via mobile broadband communication. However, the evaluation of 10 Mbps/ 3 Mbps access to mobile broadband services is also carried out. On the other hand, metrics such as latency is rejected due to the lack of reliable and sufficiently comprehensive data source for such analysis.  
_Evaluated service_\(\rightarrow\) In this report, the evaluation of only 4G LTE is included. For the 5 Mbps/1 Mbps evaluation, the country is categorized into rural, urban, and tribal lands, whereas 10 Mbps/3 Mbps do not include the third category.  
Result_\(\rightarrow\)
* With the 5 Mbps/1 Mbps speed test, compared to 2014, rural and tribal lands have shown a 3% increase in geographical mobile service coverage in 2018, whereas there is an insignificant increase in urban coverage as almost all the area was covered in 2014.
* There is 97% of urban and 83% of rural areas coverage of 4G service with 10 Mbps/ 3 Mbps in 2018. However, this result is significant compared to 2014, which was just 70% and 80% coverage for rural and urban, respectively.  
Policies\(\rightarrow\) The FCC acknowledges that there is still some work to be carried out to close the gap in coverage between rural and urban areas as well as the advertised and median speeds.  
* The rule for an over-the-air reception device (OTARD) is expected to be updated such that 5G deployment can start.
* A Universal Service Fund (USF) to improve the rural area coverage is proposed.
* A new \"Digital Opportunity Data Collection Order\" data collection mechanism is adapted to determine the availability of broadband service.  
Although most of the population living in rural and urban areas is covered by at least one of the MNOs, geographical coverage is still low.
# SPIN: A Blockchain-Based Framework for Sharing COVID-19 Pandemic Information across Nations
## 6 Related Work
### Health Information Exchange  
Health information exchange (HIE) solutions have been studied extensively in the literature [68]. It allows healthcare practitioners to access and share patients' information. It defines standards and architectures to provide secure and efficient access of data within national boundaries. HIE systems are either centralized, such as cloud-based solutions [69; 70], or decentralized, such as blockchain-based solutions [71; 72; 73; 74]. A major difference between most HIE solutions and SPIN is that the latter focuses on sharing health information across nations, which imposes additional challenges to authenticating and verifying information about individuals. We highlight the similarities and differences between SPIN and some related HIE blockchain-based solutions as follows. BlocHIE [72] maintains two loosely coupled blockchains: one for medical records and the other for personal health data. In contrast to SPIN, individuals directly interact with the blockchain network for various tasks, such as signing medical records and submitting personal health data. This design is not feasible for an across-nation solution, as individuals may not be able to access the blockchain network. Similar to BlocHIE, SPIN stores private data off-chain and records their hashes on the blockchain network as evidence of the transaction. However, it is not clear how the private off-chain data are shared across hospitals in BlocHIE. Another paper reports on M-Blocks [73], which uses a private blockchain to store and manage patients' data. The paper lacks details about how hospitals will share data across private blockchain networks. Other authors have proposed ssHealth [74], a healthcare system utilizing blockchain and edge computing technologies to share information. It consists of entities that generate and process health data, such as health service providers, medical Internet of Things (IoT) devices, and internal edge nodes. Patient information is stored in the blockchain network to be accessed by other entities, such as insurance companies, pharmacies, and government health agencies. In contrast to ssHealth, SPIN stores the hash of private data on the blockchain network and not the actual data.
## 4 Results and Discussion
### Toward Lifetime Prediction  
The Benioff law was applied to AE data recorded during the static fatigue tests in order to evaluate the lifetime of the studied material. This law is based on a power law description of the cumulative energy (\(\Omega\)) released by the composite (Equation (2)) and characterized by 4 parameters (\(\Omega_{r}\), \(\gamma\), \(\varphi\) and \(t_{r}\)).  
[PERSON] [11] studied the applicability on AE signals recorded for C/SiC and SiC/SiC composites. Data beyond the minimum of \(R_{AE}\) were in better agreement with the Benioff law than those before the minimum. This point was confirmed with the estimation of the \(c\)-value.  
The Optimum Circle Method was applied to the fatigue tests. Optimum values (leading to the minimum \(c\)-value) of \(t_{\text{start}}\) are reported on the Figure 9, which means that the approximation by the Benioff law was relevant for the data collected behind this time. A value of \(t_{\text{start}}\) was taken every hour in time interval of 10% to 90% of rupture time \(t_{\text{r}}\). The minimum \(c\)-value appears clearly around on average 40%-50% of rupture time. The energy release prior to rupture under static fatigue exhibits a critical evolution at 50% of rupture time regardless of the applied stress level. Thus, the Benioff law, initially used to study the activation of earthquakes, may also be applied to the damage of  
Figure 11: Activities of several clusters during static fatigue test at 500 \({}^{\circ}\)C and \(\sigma/\sigma_{\text{R}}=0.79\).  
Figure 12: Evolution of the coefficient of emission for several clusters at 500 \({}^{\circ}\)C and \(\sigma/\sigma_{\text{R}}=0.79\).  
composites. However, this procedure requires preliminary tests until rupture to determine \(\gamma\) and \(\varphi\) for the studied material.
## 2 Background and Related Works  
### Ldm  
The ITS station is an object that affects road traffic and includes vehicles, infrastructure, and mobile devices. LDM is used for rapidly and accurately processing data exchanged between ITS stations in the C-ITS environment, and a concept standard was defined in ETSI. LDM consists of four layers, as shown in Figure 2, and defines HD maps, dynamic information, such as road conditions, accidents, and weather, traffic signals, or moving objects as LDM objects. Static information is stored in Layers 1 and 2, and dynamic information is stored and managed in Layers 3 and 4. The ITS Station implements the C-ITS environment using LDM objects and updates the LDM by sending and receiving objects between stations. Communication message standards such as cooperative acknowledgment message (CAM), event-driven message (DEMN), and road side alarm (RSA) are used when sending and receiving data.  
The LDM concept for autonomous driving can be broadly divided into vehicle LDM and central LDM. Vehicle LDM is an implementation of the LDM concept in the vehicle. The vehicle LDM, object storage, and control unit for vehicle control are connected through internal communication. In order to  
Figure 1: Sensor big data processing system for autonomous vehicles in the cooperative intelligent transport system environment.  
Figure 2: Concept of LDM.  
provide accurate information for autonomous cooperative driving, the central LDM receives real-time information from external affiliated organizations in addition to the existing ITS station. External linkage organizations provide weather information, road condition information, and traffic information.
## 4 Hydrodynamics around the Jack-Up Platform
### Distribution of Velocity and Pressure  
In the analysis of the modeling results for three-dimensional cases, in order to make the results in the computational domain clearly visible to readers, it is better to display the results by sectioning the computational domain. Here, a plane at Y = 50 m is used to cut the seawater-platform domain, and then the computational results are displayed on the XZ plane at Y = 50 m.  
Figure 6 shows the distributions of the wave-induced dynamic pressure (note: the initial hydrostatic pressure is excluded) and the velocity in the zone around the jack-up platform at \(t\) = 56.4 s (the time when a wave trough is under the platform) and \(t\) = 62.4 s (the time when a wave crest is under the platform). It is observed that the hydrodynamic pressure below the wave trough is negative, while it is positive below the wave crest, and the magnitude of the hydrodynamic pressure exceeds 40 kPa. This hydrodynamic pressure  
Figure 5: Wave profiles at four typical times during one wave period around the jack-up platform.  
is the environmental load that drives the vibration response and even the instability of the offshore platform and seabed foundation. The velocity distribution in Figure 6 shows that the maximum wave speed in the \(x\) direction is about 2.5 m/s; meanwhile, the maximum wave speed in the \(z\) direction is about 2 m/s.  
Figure 7 shows the evolutionary time history of the wave profiles at the different typical positions A-F and S around the pile legs of the jack-up platform. As can be seen from the wave profile at S shown in Figure 7, the wave generated by the numerical wave-maker is stable, the waveform is close to a simple harmonic wave, and the wave height and wave period can reach the expected values. It has been shown in Figure 1 that the points A to F are located around the pile legs of the platform. Compared with the regular waveform at S, the waveform at A-F is no longer close to a simple harmonic wave, and the wave height slightly increases, presenting a certain degree of nonlinearity, but wave breaking does not occur. This phenomenon indicates that the pile legs of the jack-up platform have hindered the wave propagation, and the wave trains have a certain degree of reflection and superposition. Overall, the wave motion around the pile legs becomes complicated. It can also be seen in Figure 7 that the wave heights at A and D are higher than those at E and F, because the points A and D are respectively located at the head-wave side of the No. 1 and No. 2 pile legs, where waves will climb after interacting with the two pile legs.  
Figure 6: Distribution of the wave-induced pressure and velocity at \(t=56.4\) s and t = 62.4 s around the offshore platform on the section Y = 50 m in Case 1 (\(H\) = 7 m, \(d\) = 50 m, \(T\) = 15 s).