esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Quantitative MRI Analysis  
#### 2.4.1 T2-Weighted Brain MRI Tissue Segmentation  
Structural segmentation of T2-weighted images using Morphologically Adaptive Neonatal Tissue Segmentation (MANTiS) toolbox [33] was adapted as previously described by our group [16; 18] to allow for intracranial, brain and CSF volume analysis in older infants. FMRIB Software Library (FSL; v.5.0) tools were also used for preprocessing and post-segmentation editing of older infants' scans. In summary, analysis involved the following steps:  
* **Preprocessing:** (i) Intracranial space segmentation: T2 images were skull-stripped using the unvalidated 'Simple Watershed Scalping' module in the MANTiS toolbox, followed by manual editing in FSLview; (ii) Bias field correction was performed using FMRIB's Automated Segmentation Tool (FAST) [34]; (iii) Setting the image origin employed the 'Origin to the Center of Mass' module in the MANTiS toolbox;
* **MANTiS Segmentation:** Preprocessed images underwent MANTiS segmentation pipeline [33]. Automated CSF segmentations were visually inspected and subsequently edited to correct for any tissue misclassifications as described below;
* **Post-Segmentation Editing:** Automated CSF segmentations were (i) masked to zero voxels outside of intracranial space, (ii) thresholded at 40% to eliminate voxels with <40% probability of representing CSF, and (iii) converted to binary masks. Additional complex editing was undertaken due to the frequent exclusion of CSF spaces and inclusion of brain tissue. Specifically, the partial volume estimate map of CSF generated by FAST [34] was (i) thresholded at 50% to eliminate voxels with <50% of their volume comprising CSF, (ii) converted to a binary mask, and (iii) combined with subject's thresholded/binarized MANTiS CSF mask for each case. Consequently, FAST's CSF map effectively filled in spaces missing in MANTiS' CSF segmentation (e.g., cisterns, 4 th ventricle, and sulcal spaces), resulting in a 'comprehensive' CSF image. Additional minor manual editing was performed to erase misclassified brain tissue. A single individual performed all manual editing to ensure consistent tissue delineation. This final total CSF segmentation was further divided into extra-axial and ventricular spaces by manually erasing ventricular CSF from the total CSF segmentation to produce extra-axial space segmentation.
## 2 Materials and Methods
### Encapsulation Efficiency  
One milliliter of each type of capsule solution was added to 9 mL of _saline solution_ (0.85%), serially diluted, and plated on M17 agar medium to determine the viable cell concentration in capsule solutions. One gram of each type of capsule was homogenized in 9 mL of 10% (\(w/v\)) sterilized sodium citrate by vortexing for 1 min, serially diluted, and plated on M17 to get the number of entrapped cells per g of capsule. The enumeration of viable cells from both the solution and the capsule was done using the micro drop technique by placing 20 \(\upmu\)L aliquots on the surface of M17 agar plates and incubating at 37 \({}^{\circ}\)C for 48 h. The encapsulation efficiency (EE%) was determined using the equation [24]:  
\[\text{EE\%}=(\text{N}_{1}/\text{N}_{0})\times 100\]  
where N\({}_{1}\) is the number of viable entrapped cells (log CFU/g) released after the encapsulation procedure and N\({}_{0}\) is the number of viable cells (log CFU/mL) added to the mixture before encapsulation.
# In-flight Wind Field Identification and Prediction of Parafoil Systems
###### Abstract  
The wind field is an essential factor that affects accurate homing and flare landing of parafoil systems. In order to obtain the ambient wind field during the descent of a parafoil system, a combination method of in-flight wind field identification and prediction is proposed. First, a wind identification method only using global position system information is derived based on the flight dynamics of parafoil systems. Then a wind field prediction model is constructed using the atmospheric dynamics, and the low-altitude wind field is predicted based on the identified wind field of high-altitude. Finally, simulations of wind field identification and prediction are conducted. The results demonstrate that the proposed method can identify the wind fields precisely and also predict the wind fields reasonably. This method can potentially be applied in practical parafoil systems to provide wind field information for homing tasks.  
wind field; identification; prediction; parafoil system; autonomous homing Article
# Detection and Tracking of Weak Exoatmospheric Target with Elliptical Hough Transform
## 5 Simulations
### Field Data Analysis
To investigate the algorithm performance in stressful environments, we add some imaginary (non-real and man-made) clutter data, combining the raw satellite data to constitute a simulated cluttered environment. The number of clutter detections is still assumed to be Poisson distributed with spatial density \(\lambda=8.69\times 10^{-5}/\text{km}^{3}\), which determines an average number of 18 false alarms of a single scan in the surveillance space. Thus, the field data are preprocessed with the following steps: adding clutter data (imaginary), implementation of the elliptical Hough transform, and inversion to the original CS. Figure 18 shows the accumulation effects in the Hough parameter space of the synthetic data. The parameters are Scheme II, Plane-constrained integration with \(q=0.001\), time interval [0, 272] s, and average \(\text{SNR}=10\) dB. The other parameters are assumed to be the same as earlier. It can be seen from Figure 16 that the corresponding quartic curves intersect at some point and have a high value in the parameter space. In fact, by setting the secondary threshold \(\eta^{\text{Hough}}=10\) and using the K-means clustering method, we obtain the detected elliptical parameters \((\hat{a},\hat{b},\hat{\theta})=(6808.3778\text{ km},\,6833.7457\text{ km},0.003523^{\circ})\), which are now very close to the true values \((a,b,\theta)=(6811.4487\text{ km},6811.4483\text{ km},0^{\circ})\). Figure 17: Real satellite measurement data converted into the THIS-CS. Figure 18: Accumulation effect of EHT with real data (Scheme II, Plane-constrained integration, with \(q=0.001\), [0, 272] s). (**a**) \(a-b\) space. (**b**) \(a\) dimension. (**c**) \(b\) dimension. Once the parameters in the Hough parameter space are obtained, they can be converted back into the measurement CS or any other CSs to show the time history and current position of the target. Figure 19a shows the corresponding detected curve, assumed (imaginary) clutter, and raw satellite measurements in the THIS-CS, while Figure 19b shows the same result in the radar-centered ENU-CS. It can be seen from these two figures that the EHT works well, as the detected trajectory approximates highly to the true measurements of the satellite. An interesting fact in Figure 19 is that there seems to be a fixed deviation in the detection process. Even without adding any clutter (using the original tracking data), this fixed deviation will still exist and cannot be ruled out. The reason partly lies in the fact that a spherical Earth model is employed, which might result in systematic errors and cannot reflect the truth. Perhaps adopting a more precisely ellipsoidal Earth model would mitigate this effect, and this needs further investigation.
# Security Architecture for Cloud-Based Command and Control System in IoT Environment
## 1 Introduction
Hence, it is necessary to derive the security requirements for a cloud-based information system, by analyzing the security requirements applied by the U.S. military when adapting to cloud computing, analyzing the existing security requirements of the Korea military information system, and also the security requirements of the cloud computing system. In addition, it is necessary to design a security architecture to build a cloud-based secure C4I system in an IoT environment based on the derived security requirements. Therefore, we derived security requirements for countermeasures against security threats related to the cloud environment in the previous work [1]. In the previous work, we analyzed the instructions of security requirements for the Korean military information system and the security requirements in the U.S. military for cloud computing. In this paper, we focus on implementation for authentication and authorization as an IdAM module. Based on the analyzed security requirements, we design a security architecture and implement the IdAM module for authentication and authorization in the cloud-based command and control system. Additionally, we propose the operational view (OV) of the C4I system that focuses on access control. The proposed security architecture has a cloud-based and centralized IdAM. Unlike the general system environment, in the C4I system, the usage of a different authentication system for each branch (i.e., the army, navy and air force) is inefficient in performing real-time operations, and therein occurs the inconsistency problem. The proposed centralized IdAM based on cloud computing provides consistent authentication, authorization, and access control to achieve the easy management of identity information and credential. Moreover, it affords a lower maintenance cost by each system. In addition, we implement a testbed that performs the authentication and authorization of the designed security architecture. The testbed uses biometrics for multifactor authentication. We focused on implementing the specific test example as a method of authentication and authorization. It represents the applicability of several scenarios that include various authentication methods, such as biometrics, certificates and cryptographic modules. This paper is organized as follows. Security factors such as the interworking, security architecture, and the security requirements of the Korean and U.S. national defense C4I system, are analyzed in Section 2. Security requirements of the cloud-based C4I system are derived in Section 3. The cloud-based security architecture for the Korean military based on the derived security requirements is proposed in Section 4. The test scenario and implementation of a testbed based on \"OpenStack\" is proposed in Section 5. In Section 6, the existing C4I system is compared with the proposed security architecture, and Section 7 presents the conclusions of our study.
## 3 Results and Discussion
### Hg in Wastewater Can Be Removed by BL21-7  
To identify whether the engineered bacteria BL21-7 could be used to remove Hg from wastewater, 200 mL of BL21-7 bacteria and BL21-0 bacteria (_E. coli_ BL21 harboring the empty vector pET28a) were treated as described in Methods and were added to 1 L of wastewater. The cultures were shaken at 37 \({}^{\circ}\)C and 200 rpm for 24 h. Then, the bacteria were collected and the Hg content in the wastewater was measured. The results showed that before the wastewater was treated using the engineered bacteria, the Hg content in the wastewater samples was 4.85 \(\pm\) 0.31 \(\upmu\)mol/L (Figure 9). After the engineered bacteria BL21-7 were added to the wastewater and shaken at 37 \({}^{\circ}\)C for 24 h, the Hg content in the wastewater was reduced to 2.73 \(\pm\) 0.15 \(\upmu\)mol/L (Figure 9). Approximately 43.7% of the Hg in the wastewater had been removed. However, after BL21-0 was added to the wastewater and shaken at 37 \({}^{\circ}\)C for 24 h, the Hg content in the wastewater was 4.8 \(\pm\) 0.27 \(\upmu\)mol/L (Figure 9).  
Figure 8: Growth curves of BL21-7 cultured at different temperatures.  
Figure 7: Growth curves of BL21-7 cultured in PB media at different pH values.  
This result demonstrates that the engineered bacteria BL21-7 can accumulate Hg in wastewater. Only bacterial cells were added to the wastewater in our experiments. If substances containing sufficient nutrients were added to the wastewater with the bacterial cells, the effect on Hg accumulation might be more significant.
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 3 Multi-Factor Berthing Safety Study on an Actual Unmanned Ship
### Coupling CFD Model of Actual Unmanned Ship
#### 3.1.2 Grid Independent Verification  
In order to ensure the accuracy and reliability of the calculation results, through the control of the size of the grid unit, three sets of different grid number of grid schemes, take unmanned ship navigation speed of 4 m/s, water depth for the design conditions of 0.53 m, in the case of precise control variables to calculate the absolute value of the size of the resistance of unmanned ships, as shown in Table 4.  
The largest difference between the resistance of schemes 1 and 3 is 4.42%, within a reasonable range. After considering the selection of scheme 1 for subsequent calculations.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 3 Methods
### Hybrid GRU-Transformer
#### 3.4.3 Transformer  
Transformer is designed to have a more fine-grained process of sequential data. It adopts the self-attention mechanism [51], which is used to capture the interaction information between any pair of time step i and j. Compared to LSTM and GRU, it is stronger to make use of long term information at the expense of more parameters.  
As sketched in Figure 4, the input at each time step will first be transfered by a Embedding layer to a vector, which is a representation of information a high-dimensionalspace. Then the vector is combined with the positional information to be the input of the multi-head attention unit. For each head of attention unit, there are 3 matrix parameters for the transformer to learn: the key weights \(W_{K}\), the query to be keyed weights \(W_{Q}\), and the value weights \(W_{V}\). The embedding \(X\) is multiplied with the above 3 matrix to get the key matrix \(K\), query matrix \(Q\) and value matrix \(V\). Denote \(d_{k}\) the dimension of K, then  
\[Attention(K,Q,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{13}\]  
One tuple of \((W_{K},W_{Q},W_{V})\) weight matrix is named an attention head, and in a multi-head attention layer there exist several heads. The outcomes of each attention head are added and normalized to be passed into the next layer. The feed-forward layer is the weight matrix which is trained during training and is applied to every respective time step position.  
Transformer is originally designed for and widely used in the natural language process (NLP) field [52]. In the stock price prediction and FDG-Trans, we make use of the multi-head self-attention mechanism. After the process by LSTM and GRU, multi-head transformer helps with extract useful information about interactions between different time steps.
# Reduction of Hexavalent Chromium Using _Sorbaria sorbifolia_ Aqueous Leaf Extract
## 3 Results
### Reduction Effacies of PLE
#### 3.1.4 Effects of Ionic Strength  
It is imperative to explore the relationship between varying ionic strength and PLE mediated Cr(VI) reduction, as several ionic species may be present in natural water systems. Ionic strength was adjusted by addition of NaCl to acquire the desired ionic strength. Cr(VI) reduction using our green reductant was tested at different ionic strength and contact time. The obtained results presented in Figure 4 reveal that Cr(VI) reduction ability decreased (96-88%) with increasing ionic strength (10-500 mM) but had less effect (\(\sim\)8%) at a wide range of ionic strength solutions and hence, PLE can be utilized for remediation of Cr(VI) from different aqueous sources, including fresh water (\(\sim\)8 to 10 mM) to sea water (\(\sim\)400 mM) content.  
Figure 2: Effects of pH: [Cr(VI)]\({}_{0}\) = 10 mg:L\({}^{-1}\), Cr(VI) volume = 25 mL, contact time = 60 min, pH = 2–10, PLE quantity = 1.5 mL.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## Materials and Methods
### Ionospheric Correction Models  
Ionospheric delay models are generally categorized as an empirical model or a mathematical functions model. The former is based on data observed in long-term records and represents the characteristic variation patterns while the latter is fitted by mathematical functions and utilize the actual measured ionospheric delay of a certain area for a period of time.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
###### Abstract  
Spiking neural networks inspired by biological models are gaining popularity in artificial intelligence due to their ability to solve diverse problems while reducing energy consumption. As a result of the trade-off between the need to transmit large amounts of data and the power consumption of hardware deployment, artificial vision systems are particularly well-suited to construction using spiking neural networks (SNNs). How to communicate with the neuromorphic network effectively is one of the challenges associated with building systems that utilize SNN systems. It is necessary to convert the data to spike form before they can be processed by an SNN as input, unless neuromorphic or event-triggered sensing systems are employed. We present a configurable circuit based on a focal plane array (FPA) capable of providing spike-encoded readout data at the pixel level. With this type of circuit, the current signal of the photoelectric sensor can be encoded into two spike encodings with different precision, which are sent for processing to SNNs. This provides image information at two different scales for the artificial vision system based on SNNs. With this feature, we can use this circuit and different SNN structures to build an artificial target recognition system that is closer to the biological visual system.  
spiking neural networks; spike encoding; focal plane array; artificial vision system +
Footnote †: journal: Applied sciences  
1 Laboratory of Solid-State Optoelectronic Information Technology, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; [EMAIL_ADDRESS]  
2 School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China  
3 State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China; [EMAIL_ADDRESS]  
4 Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China  
5 Key Laboratory of Semiconductor Neural Network Intelligent Perception and Computing Technology, Chinese Academy of Sciences, Beijing 100083, China; [EMAIL_ADDRESS] (G.C.); [EMAIL_ADDRESS] (Z.L.)
## 5 Performance Evaluation  
To estimate the performance of our proposed algorithms, a 3D UASN with random deployments of \(n\) nodes over an area of size \(1000\times 1000\times 1000\) m\({}^{3}\) was simulated in MATLAB. Table 1 gives the network parameters employed in our simulation model in detail. In order to observe the performance more intuitively, we select three performance parameters: the information gain \(G\), the data collection completion time \(T\) (corresponding to data delay), and the gain to cost ratio \(\eta\). These parameters can be calculated by Equations (15)-(17):  
\[G=\sum_{i=1}^{N}[Q*(1-P_{packet}(i))^{m})] \tag{15}\]  
\[T=L_{path}/v_{AUV}+\sum_{j=1}^{M}[T_{residence}(j)] \tag{16}\]  
\[\eta=G/T \tag{17}\]  
where \(Q\) is the packet size of each node, \(P_{packet}(i)\) is the probability of transmission error of node \(i\) (see Section 3), \(m\) is the transmission round, the \(L_{path}\) is the total length of traversal path, \(M\) is the number of stay points, and \(T_{residence}(j)\) is the residence time of the AUV in each stay point \(j\). The residence time is the sum of initiation time, scheduling time, transfer time, and additional compensatory time. It varies with the number of nodes within each probabilistic neighborhood and the value of transmission rounds adaptively. As we can see from the formulations, the gain to cost ratio \(\eta\) is to evaluate data collection performance as a tradeoff between the data collection completion time and information gain. The greater the value of \(\eta\) is, the better the data collection performance.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 3 System Description
### Opportunities and Potential of Renewable Energy in Bangladesh
#### 3.1.1 Solar PV Energy  
Banglades is a tropical country that lies between 20\({}^{\prime\prime}\)34\({}^{\prime}\) and 26\({}^{\prime\prime}\)38\({}^{\prime}\) North Latitude and 88\({}^{\prime\prime}\)01\({}^{\prime}\) and 92\({}^{\prime\prime}\)41\({}^{\prime}\) East Longitude. The country has a lot of potential for harvesting renewable energy from the sunlight. The daily average solar radiation of Bangladesh is 4.59 kWh/m\({}^{2}\)/day, while solar intensity remains in the range of 4-6.5 kWh/m\({}^{2}\)/day, according to NASA's database [14]. Bangladesh has the potential to receive around 70 PWh of solar energy annually, which can fulfill the entire country's electrical load [46]. Standalone solar PV-powered base stations are already deployed as a result of the aforesaid prospective solution. According to the authors of [20], in Bangladesh, around 521 solar PV-powered base stations were installed in the year 2015, and telecom companies are working to increase that number.
# Airborne Quantum Key Distribution Performance Analysis under Supersonic Boundary Layer
## 2 Method  
### Background  
In the air-to-ground communication scenario of airborne QKD, we assume \(d\) is the shortest projection distance to the horizon plane, between the aircraft ([PERSON]) and the receiving ground station ([PERSON]). [PERSON] flies at a constant speed in a certain path obliquely above [PERSON], as shown in Figure 1. \(h\) is the relative height of [PERSON] and [PERSON] which is constant. \(l\) is the distance between [PERSON] and [PERSON]. \(\varphi\) is zenith angle. During the entire flight, the azimuth angle \(\alpha\) of the aircraft is varying with a range of \([-80^{\circ},80^{\circ}]\).  
Figure 1: Schematic diagram of airborne QKD downlink.
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 3 Results  
### Datasets  
#### 3.1.1 Vimeo-90k  
We leveraged the Vimeo-90k [32] as our training repository in the present investigation. This vast dataset contains high-quality video data, making it ideal for an array of video processing tasks at the low level, including video noise mitigation, deblocking, video frame interpolation, and video super-resolution. It includes 9000 assorted video clips derived from diverse scenarios and activities. Hence, its versatility renders it highly suitable for training our proposed model. For our purposes, we utilized the Septuplet subset from this dataset, which is composed of 91,701 groups of video clips, each containing seven frameswith a resolution of 256 \(\times\) 448. To generate our training dataset, we executed a Gaussian blur (\(\sigma\) = 1.6) on the high-resolution frames and downscaled them by a factor of 4, resulting in low-resolution patches of dimensions 64 \(\times\) 64.