esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Priority-Based Layered QC-LDPC Decoder with Double Update Queues
### Structure of the Priority-Based Layered LDPC Decoder with Double Update Queues  
Figure 2 shows the detailed architecture of priority-based layered LDPC decoder with double update queues. The parallelism of processing units is equal to the size of submatrix in the corresponding PCM. In the WiMAX decoder, the parallelism is equal to 96 [5]. In the 5G NR decoder, the parallelism is equal to 384 [6].  
Figure 1: Illustration of the priority-based layered schedule for pipeline conflicts.  
Before decoding, LLRs are initialized and denoted as \(LLR^{itit}\)s. They are stored into the LLR RAM. LLR RAM is composed of a simple dual-port block RAM (BRAM) which is used to store the latest updated LLR. The old LLR value can be read repeatedly provided that no new LLR value is written in the same address. This feature is conductive to the implementation of the proposed decoding schedule.  
When the decoding starts, LLR is read out from RAM according to the address given and sent to the LLR barrel shifter. It is not essential to use the reverse barrel shifter to shuffle the submatrix as the identity matrix before storing back to the LLR RAM [20]. Instead, the barrel shifter that shuffles based on the absolute shift value can be well applied in the decoder. After shuffling, LLR is sent into variable node units (VNUs) to calculate \(V^{it}\)s. Then, \(V^{it}\)s are passed to the CNUs. At the same time, \(V^{it}\)s are buffered into FIFOs waiting for the update of LLR.  
In the CNUs, the minimum (min) and the second minimum (smin) absolute value, the index of the minimum value (min index), and the sign product (sign product) of \(V^{it}\)s are achieved. Then, registers will store this intermediate data until new \(R^{it}\)s are required for update in the next layer. \(R^{it}\)s generated from CNU are added with \(V^{it}\) buffered out from FIFO and achieve the updated LLRs. When LLRs are updated, they are stored back to the LLR RAM.  
In this paper, we propose double update queues instead of a single update queue [21] to update the LLRs. Compared with a single update queue, double update queues accelerate the update of LLRs in a layer and decrease the occurrence of pipeline conflicts. This will be discussed in detail in Section 3.3.  
When pipeline conflict happens, the gain calculator module and gain adder module are used to migrate the conflicts. These will also be discussed in detail combining with the flow chart in Section 3.3.  
Due to the design of priority-based schedule and double update queues, the signs of all the \(V^{it}\)s are buffered into four separated FIFOs in the CNU. The two minimum values, the sign product and the index of the minimum are used to generate the \(R^{it-1}\)s for VNU  
Figure 2: Architecture of the priority-based QC-LDPC decoder with double update queues.  
used in the next iteration and \(R^{it}\)s for overlapping submatrices, non-overlapping variable nodes (double update queues) and gain (priority-based schedule).
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 4 OCTA Algorithms
### Intensity- or Amplitude-Based OCTA Algorithms
By optimizing the frame number under conditions of low tissue bulk motion, capillaries can be reliably detected [49]. As the dynamic tissue signal has a lower temporal correlation, at a given time lag, than static tissue, correlation has been investigated as a parameter for angiography. In 2011, [PERSON] et al. [50] demonstrated in vivo human volar forearm imaging of the capillary density and vessel diameter with correlation mapping optical coherence tomography (cmOCT). The correlation between OCT frames acquired at time \(t\) and \(t+T\) at the same position is:  
\[\mathrm{cmOCT}(\mathrm{x},\mathrm{z})=\sum_{p=0}^{V}\sum_{q=0}^{W}\frac{[ \mathrm{I}(\mathrm{x}+p,\mathrm{z}+q,t)-\overline{\mathrm{I}(t)}][\mathrm{I}( \mathrm{x}+p,\mathrm{z}+q,t+T)-\overline{\mathrm{I}(t+T)}]}{\sqrt{[\mathrm{I} (\mathrm{x}+p,\mathrm{z}+q,t)-\overline{\mathrm{I}(t)}]^{2}+[\mathrm{I}( \mathrm{x}+p,\mathrm{z}+q,t+T)-\overline{\mathrm{I}(t+T)}]^{2}}}, \tag{5}\]  
where V and W define the extent of the spatial region for correlation calculation, and \(\overline{\mathrm{I}(t)}\) denotes the spatially averaged intensity over this region. This is a spatially averaged, correlation-based algorithm with normalization. After this calculation, a 2D correlation map can be formed by applying a threshold to binarize the image into static and dynamic regions. In 2012, [PERSON] et al. [51] proposed split-spectrum amplitude-decorrelation angiography (SSADA) to image the human macula and optic nerve head. Ensuring a nearly isotropic coherence volume size by splitting the spectrum to degrade the axial resolution to equal the transverse resolution, they then applied a method similar to cmOCT.
## 3 CubeSats and Inter-Satellite Links (ISLs)
### LEO Exploration  
Historically, LEO constellations have not utilized frequent LEO-to-LEO laser communication links due to the combined effect of several factors. LEO satellites, with their lower altitudes and higher orbital velocities (7-11 km/s) compared to MEO or GEO satellites, experience shorter communication windows and require more frequent handovers between satellites to maintain a connection. This handover process introduces complexity and increases the risk of disruption [70]. Additionally, atmospheric drag necessitates frequent orbital corrections for LEO satellites, further complicating link maintenance [71]. Finally, achieving comprehensive global coverage with LEO constellations demands a large number of satellites, and the challenge of maintaining continuous links between these rapidly moving spacecraft has historically favored communication link implementation in MEO and GEO constellations with fewer and slower-moving satellites [72].
# Dynamic Analysis and Security Characteristics of Carrier-Based Aircraft Arresting in Yaw Condition
## 5 Calculation and Analysis
### The Influence of Yaw Angle on Arresting Safety  
The landing area of the flight deck as shown in Figure 19 is actually the area surrounded by safety warning lines on both sides of the inclined deck, which is often referred to as the inclined landing runway. The landing runway should not only meet the safe landing requirements of the carrier aircraft, but also ensure that there is enough distance to complete the missed approach after confirming the failure of the hooking rope [22]. In addition, there should be a long enough turning area to meet the turning operation of the carrier aircraft.  
In the picture:  
\[\left\{\begin{array}{l}B_{a}=B_{T}+2B_{S}\\ S_{a}=d+\Delta x_{1}+\Delta x_{2}+\Delta x_{3}+S_{s}+S_{t}+S_{S}\end{array}\right. \tag{10}\]  
where \(B_{a}\) is the width of the landing runway of the aircraft deck, \(B_{T}\) is the total width of the aircraft with the largest wing span, \(B_{S}\) is the width of the safety margin between the safety warning sign and the aircraft's wing tip, \(S_{a}\) is the total length of the landing area, \(d\) is the length of the landing area, \(\Delta x_{1}~{}+~{}\Delta x_{2}~{}+~{}\Delta x_{3}\) is the length of the blocking area, \(S_{d}\) is the length of the deceleration braking area, \(S_{t}\) is the length of the turning area, and \(S_{S}\) is the length of the safety reserve area.  
It is assumed that the deck landing runway width of an aircraft carrier is 40 m and the wingspan of an aircraft carrier is 24.56 m; then, \(B_{S}\) is 7.72 m.  
Under different yaw conditions, the deviation of carrier aircraft stopping is shown in Table 5.  
According to the analysis of arresting offset, when the yaw angle is 5 degrees, the carrier aircraft stops just at the edge of the safe landing area. If the yaw angle is bigger than 5 degrees, the aircraft will rush out of the safe landing area, which may lead to safety accidents. Therefore, the yaw angle should be reduced as much as possible to reduce the lateral offset of the carrier aircraft, so as to prevent the aircraft from colliding with the aircraft or the ship island building outside the safe landing area in the process of arresting and improve the arresting safety.
## 2 Evolution of SDN Control Architecture for VANETs
### Distributed Controller Model  
The conveyed SDN-based model was acquainted with the disappointments and confinements of the single SDN-based controller [13]. The disseminated SDN-based model is utilized to adjust the heap among various controllers and multi-center frameworks in the controllers. The model is utilized to deal with the whole system adequately. The disperse controllers are utilized to share the lot of information, in this way guaranteeing the reliability of correspondence [19]. The examination of a circulated SDN-based model by the focal controls model shows that the appropriated frameworks are progressively more responsive, quicker, and increasingly productive regarding enormous worldwide system zones. Nonetheless, in spite of the advantages of the dispersed SDN model, numerous difficulties must be defeated before the appropriated controller can be executed [21]. The mapping of the control plane and sending plane must be designed consequently rather than physically. The controller must have a wide perspective on the whole system to help the framework. Much of the time, it is hard for each controller to have wider access to the systems. These controllers utilize nearby calculations to create coordination between various controllers. Hence, a calculation or technique is essential to coordinate the whole system and give a global image of it. Figure 4B represents the distributed controller model.
# Performance Analysis of Ocean Surface Topography
###### Abstract  
Interferometric imaging radar altimeter (InIRA) is the first spaceborne Ku-band interferometric synthetic aperture radar (InSAR) which is specially designed for ocean surface topography altimetry. It is on the Tiangong II space laboratory, which was launched on 15 September 2016. Different from any other spaceborne synthetic aperture radar (SAR), InIRA chooses a near-nadir incidence of 1\({}^{\circ}\)-8\({}^{\circ}\) in order to increase the altimetric precision and swath width. Limited by the size of the Tiangong II capsule, the baseline length of InIRA is only 2.3 m. However, benefitting from the low orbit, the signal-to-noise ratio of InIRA-acquired data is above 10 dB in most of the swath, which, to a certain extent, compensates for the short baseline deficiency. The altimetric precision is simulated based on the system parameters of InIRA. Results show that it is better than 7 cm on a 5-km grid and improves to 3 cm on a 10-km grid when the incidence is below 7.4\({}^{\circ}\). The interferometric data of InIRA are processed to estimate the altimetric precision after a series of procedures (including image coregistration, flat-earth-phase removal, system parameters calibration and phase noise suppression). Results show that the estimated altimetric precision is close to but lower than the simulated precision among most of the swath. The intensity boundary phenomenon is first found between the near range and far range of the SAR images of InIRA. It can be explained by the modulation of ocean internal waves or oil slick, which smooths ocean surface roughness and causes the modulated area to appear either brighter or darker than its surroundings. This intensity boundary phenomenon indicates that the available swath of high altimetric precision will be narrower than expected.  
InSAR; near-nadir incidence; Ku-band; ocean surface topography; altimetry 2017 9, 933; doi:10.3390/rs9090933
## 3 Simulation Signal Analysis  
### The Performance of Mode Decomposition Provided by the Proposed Method  
For the first simulation, we present a classical mode mixing example here. A modulating pure signal plus an intermittent harmonic signal will inevitably lead to mode mixing when it is analyzed by EEMD, due to the local nature of the method. The analyzed signal is \(y=x_{1}+x_{2}\) with  
\[x_{1}=\left\{\begin{array}{ll}0&if\;1\leq n\leq 200\\ \sin(2\pi f_{1}(n-501))&if\;201\leq n\leq 400\\ 0&if\;401\leq n\leq 600\\ \sin(2\pi f_{1}(n-501))&if\;601\leq n\leq 800\\ 0&if\;801\leq n\leq 1000\\ \end{array}\right. \tag{9}\]  
\[x_{2}=\sin(2\pi f_{2}n)(1+\sin(2\pi f_{3}n))\;1\leq n\leq 1000 \tag{10}\]  
where \(f_{1}=0.255\), \(f_{2}=0.065\) and \(f_{3}=0.032\). The mixed simulation signal is plotted in Figure 1. Table 1 shows the parameter selection for EEMD.  
Moreover, the result provided by the introduced method and EEMD is drawn in Figures 2 and 3, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Noise Standard Deviation** & **Ensemble Size** & **Maximum Number of Sifting Iterations** \\ \hline
0.2 & 200 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameter settings for Ensemble Empirical Mode Decomposition (EEMD).  
Figure 1: The simulation signal: (**a**) original intermittent harmonic signal; (**b**) modulating pure signal; (**c**) compound signal.  
The optimal amplitude of the noise is set as 0.2 and the ensemble size is 200 for the EEMD method. Nevertheless, parameter selection is not necessary for RPSEMD. It can be seen from Figure 2 that the IMF1 and IMF2 correspond to the original intermittent harmonic signal and modulating pure signal, respectively. However, more unreasonable mode functions have been generated in EEMD. Thus, RPSEMD outperforms EEMD in the mode decomposition of complex signal.  
Figure 3: The result provided by the EEMD method.  
Figure 2: The result provided by the regenerated phase-shifted sinusoid-assisted EMD (RPSEMD) method.
## 6 Experimental Results
### Dealing with Large Datasets  
In this section, we show the results of the proposed approach when used with large datasets. As a case study, we tested ESCAPE with some datasets containing Amazon user reviews. Data are retrieved from the Amazon Customer Reviews Database ([[https://s3.amazonaws.com/amazon-reviews-pds/readme.html](https://s3.amazonaws.com/amazon-reviews-pds/readme.html)]([https://s3.amazonaws.com/amazon-reviews-pds/readme.html](https://s3.amazonaws.com/amazon-reviews-pds/readme.html)), accessed on 1 April 2022), and reviews have been collected between 1995 and 2015. Reviews that refer to different categories belong to different datasets. In particular, we have now focused on the following data, described in Table 12:  
* D7: Digital Music (349,933 documents);
* D8: Luggage (325,588 documents);
* D9: Video Games (409,551 documents).  
The following subsections include results obtained for the joint-approach and the probabilistic approach. Since the datasets are characterized by a very-sparse data distribution, we did not consider global weight entropy in these experiments. For the probabilistic approach, we only consider D8 and D9, where documents have the highest average length.  
For visualization results, we focus only on dataset D8, both for the joint-approach and the probabilistic approach.
## 4 Maximum Power Point Tracking (MPPT) Algorithm
### P&O Algorithm  
In this algorithm based on the previous value of the power, the value of the reference voltage and current keeps on changing (i.e., increasing or decreasing) until it attains the maximum power point [27; 28]. This perturbation causes changes in the power of the solar module. If the power increases due to the perturbation, it moves the operating point of the PV array to MPP and continues to disturb the PV voltage in the same direction. After reaching the maximum power, the next instant power reduces and consequently reverses the perturbation. The size of the perturbation kept is minimal in order to maintain the power variation. For set the reference perturbation voltage, the additional Proportional Integral controller was utilized to calibrate the duty cycle ratio of the MPPT-based converter. It was observed that there was some power loss due to this perturbation and that it sometimes also failed to track the power under rapidly varying atmospheric conditions. However, this algorithm is well known for its simplicity, showing better stability in the response under slow transient conditions and at a very high voltage oscillation.
# Changing the Formations of Unmanned Aerial Vehicles
## 3 Small Unmanned Aerial Vehicle  
A quadcopter, an unmanned aerial vehicle powered by four propellers, is an example of innovative UAV technology. Its basic structure consists of a lightweight but durable frame that integrates key components: four electric motors with propellers, positioned at the ends of arms extending from a central point; an avionic system; and a rechargeable battery. These independently controlled propellers provide an exceptional vertical take-off and landing (VTOL) capability and stability and maneuverability in the air. The construction of the quadcopter is depicted in Figure 1.  
The UAV software (UAVbee version 1.8) is a crucial element responsible for its fundamental functionalities, such as its precise control, navigation, and the execution of complex tasks. The avionic system of the UAV includes sensors, a GNSS receiver, and a flight controller [5]. The flying platform enables communication with the user through dedicated applications or interfaces. Figures 2 and 3 illustrate an exemplary design of the UAV utilized in formation flight tests [1,11,20].  
Figure 1: Construction of the quadrotor: (**a**) the power supply and drive module; (**b**) the control module of the UAV.  
The quadrotor has advantages such as in its maneuverability, hovering, and vertical take-off and landing (VTOL). Unfortunately, it does not have a large payload and flight range. UAV aircraft carry large loads over longer distances at high speeds [24]. Therefore, UAV quadrotors are used in civil and military applications, where VTOL and hovering are important. An example of an autonomous [25] UAV system is the Falcon Drones company's UAV inspection and monitoring system. This system is dedicated to patrolling industrial infrastructure. The industrial zone does not have enough space for the taking off and landing of aircraft. The system has a docking station where the quadrotor lands and takes off. That is why the VTOL function is so important. Hovering allows for the precise inspection of selected waypoints.  
Quadrotors are used for reconnaissance and to strike targets up to several kilometers from troops in military applications. Due to their size, they are easy to use and accompany soldiers in many missions. Fixed-wing aircraft and multirotors have many advantages and complement each other, which is evident in military solutions.  
Figure 3: The flying platform during tests.  
Figure 2: One of the flying platforms used in the research.
## 2 Materials and Methods
### Methods
#### 2.2.3 Planning Cycle  
Task-driven strategy means how to determine the start-up time of autonomous planning. Currently, three types of strategies are commonly used: cycle-driven, event-driven, and hybrid-driven. Cycle-driven makes planning decisions according to a predefined time interval \(T\). Event-driven makes planning decisions based on events, meaning decisions are started up as soon as an event occurs. Hybrid drive considers both cycle-driven and event-driven, meaning decisions are started up according to a certain time interval, also when a major event occurs.  
GEO meteorological satellite usually acquire 'full-disk' cloud cover images at fixed intervals (e.g., every 15 min for FY-4A). Based on this ability, we use cycle-driven autonomous planning, where the planning period is the time interval at which the meteorological satellite acquires cloud cover images. It should be noted that this interval is not fixed as 15 min, but it depends on the actual time of the acquisition by the meteorological satellite.
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
## 5 Vehicle Model
### Vehicle Velocity  
In the global coordinate frame, \(X_{0}Y_{0}Z_{0}\), the vehicle translational velocity vector, \(\dot{v}_{0}^{v}\), is related to its body-fixed velocities in \(X_{v}Y_{v}Z_{v}\), \(\dot{v}_{v}^{v}\), as [49]:  
\[\dot{r}_{0}^{v}=R_{0}^{v}(\vec{q}^{v})v^{v} \tag{4}\]  
Figure 1: Vehicle 3D Kinematic Model.  
where \(v^{v}\triangleq\dot{v}_{v}^{v}\). The angular velocity vector of a vehicle about its body-fixed reference frame, \(\omega^{v}=[\omega_{x}^{v}\ \omega_{y}^{v}\ \omega_{z}^{v}]^{\top}\), is used to calculate the time derivative of the quaternion vector, \(\dot{q}^{v}\), as:  
\[\dot{q}^{v} =\frac{1}{2}G^{v^{\top}}\omega^{v},\quad\omega^{v}=2G^{v}\dot{q}^{v} \tag{5a}\] \[G^{v} =\left[-q^{v},\quad q_{0}^{v}I_{3}-S(q^{v})\right] \tag{5b}\]  
The unit-length constraint equation that relates the time derivatives of the quaternions is also given as \(\dot{q}^{v^{\top}}\dot{q}^{v}=0\).
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 3 Results
### Validation  
#### 3.4.1 Similarity between Original Drone Images  
Table 3 displays the similarity values of the RGI images obtained by drone in August and September with respect to the July image. SSIM ranged from 0.543 to 0.842. The major dissimilarities (lower values) were found in the correlation parameter (s) in almost all bands. In particular, the Near Infrared band (I) had the lowest similarity values on both dates. Identical data behavior was observed when comparing August and September images (data not shown), with SSIM values of 0.757, 0.805 and 0.550, respectively, for the RGI bands.  
Figure 5: Evolution of the GCI spectral indices of a point associated with vegetation (green striped line: gci-veg), of a point associated with soil (brown dotted line: gci-soil) and of the pixel in which these points are located in the Sentinel 2 images (gray continuous line: gci-S2). Period: 1 May to 31 October 2019.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**August Image**} & \multicolumn{3}{c}{**September Image**} \\ \hline
**Parameter/Band** & **R** & **G** & **I** & **R** & **G** & **I** \\ \hline \(c\) & 0.936 & 0.938 & 0.972 & 0.890 & 0.923 & 0.918 \\ \(s\) & 0.935 & 0.928 & 0.743 & 0.895 & 0.838 & 0.597 \\ \(l\) & 0.962 & 0.961 & 0.996 & 0.935 & 0.947 & 0.991 \\ \(SSIM\) & 0.842 & 0.837 & 0.719 & 0.744 & 0.732 & 0.543 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The similarity between original drone images. Reference: July image.