esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 kd-Tree Data Structure for THB-Spline Refinement
### kd-Tree-Based Mesh Representation  
The THB splines are defined over a sequence of nested domains which form a hierarchical mesh. An efficient representation of the hierarchical mesh along with a robust mesh generator plays an important role in the performance of the THB spline-based local refinement.  
[PERSON] et al. [39] proposed a quad-tree data structure to represent two-dimensional THB spline meshes. As will be shown later, a quad-tree results in exponential space complexity in the worst case. Recently, an alternative binary tree data structure has been utilized [40] for multi-dimensional meshes. The internal nodes of the tree store the splitting lines during domain subdivision, whereas the leaves of the tree contain homogeneous pieces of the domain. The nature of this new data structure is in fact a kd-tree. Figure 9 illustrates an example of kd-tree representation for a two-dimensional hierarchical mesh.  
Figure 8: Illustration of the basis functions of a two-level (**a**) hierarchical B-spline and (**b**) truncated hierarchical B-spline. The \(N^{l}\) in the dashed line can be represented by a linear combination of \(N^{l+1}\), and is therefore removed to avoid linear dependence.  
Figure 9: kd-tree representation of a two-dimensional hierarchical mesh. (**a**) Splitting lines and cells in the hierarchical mesh; (**b**) kd-tree representation. In (**a**), the domain is subdivided into homogeneous cells, with each belonging to only one hierarchical level, while in (**b**) the splitting lines and the cells are stored in the internal nodes and leaves of a kd-tree data structure, respectively.  
A significant advantage of kd-tree representation over quad/oct-tree is that the former approach enables unequal subdivision of the domain, which results in fewer splits during tree construction. Consequently, the number of leaves generated by kd-tree subdivision is smaller than that generated by quad-tree or oct-tree subdivision. Figure 10 illustrates an instance of successive stripe refinement on a square domain. As the boundaries of the refinement regions are not aligned with bisection planes, a quad-tree representation will contain \(3\cdot 2^{n}-3\) internal nodes and \(9\cdot 2^{n}-8\) leaves, leading to a space complexity of \(O(2^{n})\). In contrast, its kd-tree counterpart only needs \(n\) internal nodes and \(n+1\) leaves, of which the space complexity is \(O(n)\).
## 4 Experimental Analysis of Link Quality
### Observations  
In a WSAN with mobile (actuator) nodes, the distance between the mobile actuator and the sensor that are involved in transmitting the sensed data will change over time, thus aggravating the variability of link quality. This is why we pay a special attention to examining how the link quality varies over distance.  
Fig. 4 shows the packet loss rates on different nodes when the transmit power is set to 0 dBm. Within the distance of 7m, the packet loss rates remain less than 10%. Beyond 30m, the packet loss rates are fairly close to 100%, implying that almost all packets sent to the nodes are lost. This indicates that the radio range is approximately 30m. For links with distances ranging from 7 to 30m, the packet loss rates could vary drastically. For example, the packet loss rates vary nearly from 0% to 100% in the area between 9 and 13m. It can be observed that nodes far away from the transmitter may possibly undergo smaller packet loss rates than those near the transmitter.  
Fig. 5 plots the recorded data when the transmit power is set to be -5 dBm. Similarly, the packet loss rates show great variability and irregularity both over different distances and at a given distance. Compared to the higher power case shown in Fig. 4, almost 100% packet loss rates are observed at a smaller distance, and the radio range decreases to 26m.  
Figure 4: Packet loss rate versus distance (0 dBm).  
We have observed that the packet loss rates over the wireless channel in real WSANs are highly variable and irregular. The assumptions that link quality is exclusively based on distance, which is often made when modelling the link quality of sensor networks [35,36], simplify system analysis but can be practically questionable. This could be further justified by the fact that the link quality of WSANs depends also on many factors other than distance and transmit power, such as the environment, noise, radio frequency, modulation scheme, and (hardware) platform in use, just to mention a few. Due to the unpredictability of the link quality in terms of packet loss rate and the impracticability to model it accurately, it is now imperative to develop a platform-independent paradigm to enhance the reliability of WSANs under lossy conditions. A desirable solution should be widely applicable to diverse application scenarios with different system and environment setups.
## 3 Renewable Energy Resource Modeling
### Wind Power Generators  
Unlike traditional generators, the output of wind power generators (WPG) is unpredictable because of fluctuations in wind energy. Such randomness in the production of WPG contributes to uncertainties in power flows and losses [17]. The power output, denoted by \(P_{wt}\) can be mathematically expressed as a function of wind speed in the following way [17]:  
\[P_{wt}(v_{s})=\left\{\begin{array}{l}0,\ if\ v_{s}<v_{cut-in}\ or\ v_{s}>v_{ cut-out}\\ \\ P_{wt}^{rated}\times\left(\frac{v_{s}-v_{cut-in}}{v_{total}-v_{cut-in}} \right),\ if\ v_{cut-in}\leq v_{s}\leq v_{rated}\\ \\ P_{wt}^{rated},\ if\ v_{rated}\leq v_{s}\leq v_{cut-out}\end{array}\right. \tag{25}\]  
where \(P_{wt}^{rated}\) is the rated power of the installed WPG in (MW), \(v_{s}\) is the wind speed, \(v_{rated}\) is the rated speed (m/s), \(v_{cut-in}\) is the lowest wind speed at which the WPG will begin to generate power, and \(v_{cut-out}\) is the maximum wind speed at which a WPG is designed to operate safely. When the wind speed \(v_{s}\) is within the range of \(v_{cut-in}\) and \(v_{rated}\), the power output of the WPG increases linearly as the wind speed increases.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Parameters and Basic Structure of 5G PHY Layer
#### 2.2.3 PHY Layer Channels  
There is a list of PHY channels used in 5G NR. These channels play crucial roles in synchronization, system information, and overall communication.  
* Physical Broadcast Channel (PBCH): This carries essential system information for UEs. It provides information such as downlink system bandwidth and timing information in radio frames, and it is part of Synchronization Signal (SS) burst set periodicity, system frame number, and other upper-layer details.
* Physical Downlink Control Channel (PDCCH): This carries essential control information called COntrol REsource SET (CORESET) that guides UEs on how to receive and decode downlink data. It provide information about information element multiplexing, channel coding, rate matching, scrambling, modulation, and dynamic resource control. Contrary to the 4G channel, it is not allocated across the entire system bandwidth and is more generalized.  
Figure 4: TDD mode vs. FDD in 5G NR.  
* Physical Downlink Shared Channel (PDSCH): The main channel used for carrying user data from gNB to the UE. There are only minor changes between 4G and 5G.
* Physical Uplink Control Channel (PUCCH): This carries information such as Hybrid Automatic Repeat reQuest (HARQ) feedback, Channel State Information (CSI), and Scheduling Request (SR). In 5G, a short format to support low latency application is introduced.
* Physical Uplink Shared Channel (PUSCH): This carries data in the UL. It has the same function as in 4G.
* Physical Random Access Channel (PRACH): This is used by the UE to initiate random access procedures. The preamble contains information about the UE's identity and timing adjustment. It allows the UE to request resources for UL transmission and establish synchronization with the network. Zadoff-Chu sequences are used to generate the random access preamble, similar to LTE technology.  
With respect to the NR predecessor, the Physical Control Format Indicator Channel (PCFICH), carrying organization of data and control information in the downlink by Control Format Indicator (CFI), is removed. The Physical Hybrid ARQ Indicator Channel (PHICH), carrying hybrid Automatic Repeat reQuest (ARQ) indicators (ACKnowledged (ACK) Non-ACKnowledged (NACK)), is moved and is indicated in UL Downlink Control Information (DCI) in NR.
## 5 Case Studies for Construction Site Layout Planning and Managerial Implication
### Case Study 2
* \(\delta_{j,k,l,m}=\) Auxiliary binary-type variable where one means material \(j\) is transferred from floor \(l\) cell \(k\) to floor \(m\), otherwise zero. Equation (9) has two components used to calculate the total distribution and material transportation costs. The first component involves costing the usage of a hoist to move materials from the ground floor to one of the storage floors. Also included is the horizontal cost of transportation from the elevator to one of the storage cells. We defined a set of binary decision variables \(x_{j,l,k}\) representing the location of the materials storage. The second component represents the total cost of material distribution from the floor of storage to each floor of the building requesting materials. The expectation is for requests for material type \(j\), materials \(Q_{j,m}\) on every floor, and \(m\) to be presented for the calculation. Four optimization algorithms were tested in this experiment, namely DE, PSO, SOS, and HSOS-LO. The control parameters for the optimization algorithms tested were set as follows: the population size (_pop_size_) for DE, PSO, SOS, and HSOS-LO was decided to be 50; the maximum number of function evaluations (_maxFE_) for DE, PSO, SOS, and HSOS-LO was decided to be 12,000 (equal to 240 iterations for both PSO and DE; 60 iterations for SOS; and 40 iterations for HSOS-LO); and the number of independent runs was set to 100. Table 5 compares the performance of HSOS-LO with DE, PSO, and SOS. It was shown that HSOS-LO outperforms SOS, PSO, and DE in fitness value, consistency, number of iterations, and number of function evaluations. Table 6 compares the performance of HSOS-LO to the previous research. According to Table 6, HSOS-LO gave a better solution than the references result, saving $5024 in transportation costs. The result is obtained within the average computational time of 0.492 s. Finally, Figure 7 shows the convergence graphic of competing algorithms.
## 2 Related Work
### LSTM-WMCs  
LSTM-WMCs is a variant of LSTM that enables the memory cell to influence the value of the gates through a set of recurrent weights [22]. The core concept of this variant is that, in traditional LSTM units, the regulation of information flow through the three gates can be likened to the operation of three valves controlling the flow of information in fixed directions. The opening and closing of these valves are determined by \(x_{t}\), which represents the current input, and \(h_{t}-1\), which encapsulates past information. However, as indicated by Equation (9), \(h_{t}\) is, in fact, a subset of \(c_{t}\). This implies that \(c_{t}\) retains certain valuable and significant information from previous time steps that is not captured in \(h_{t}\). In the LSTM-WMCs variant, the cell state \(c_{t}\) is integrated into the gating mechanism, allowingfor more precise regulation of information flow within the gates. Figure 2 illustrates the structure of this LSTM variant.  
The equations below highlight the modifications introduced in this novel LSTM variant compared to the traditional LSTM:  
\[i_{t}=\sigma(W_{ix}x_{t}+W_{ik}h_{t-1}+\tanh(W_{ic}c_{t-1})+b_{i}) \tag{10}\]  
\[f_{t}=\sigma(W_{fx}x_{t}+W_{fh}h_{t-1}+\tanh(W_{fc}c_{t-1})+b_{f}) \tag{11}\]  
\[o_{t}=\sigma(W_{ox}x_{t}+W_{bl}h_{t-1}+\tanh(W_{ox}c_{t})+b_{o}) \tag{12}\]  
where \(W_{ic}\), \(W_{fc}\), and \(W_{oc}\) represent \(n\times n\) weight matrices that define the connections between the cell state and the input gate, forget gate, and output gate, respectively. The hyperbolic tangent function here serves the same purpose as in Equation (9), ensuring that only relevant and appropriately scaled information is propagated between gates. This mechanism prevents the unbounded cell state from exerting excessive influence on the gate dynamics, thereby avoiding undesired saturation.
## 2 Mathematical Dynamic Model of the IRW
### Creep Force  
For an IRW, the left wheel, right wheel, and carrier are three different parts of the mechanical system, which means that the translational forward speeds of these three parts are also different from each other, resulting in different creepages on the left wheel and the right wheel, so the creep forces applied on the left wheel and the right wheel should be calculated separately.  
Considering the yaw speed \(\dot{\psi}\) of the carrier, the longitudinal forward speeds of the left wheel and the right wheel can be derived as follows, \(v_{\text{L}}\) and \(v_{\text{R}}\), respectively.  
\[\begin{cases}v_{\text{L}}=v+b\dot{\psi}\\ v_{\text{R}}=v-b\dot{\psi}\end{cases} \tag{2}\]  
Figure 1: The IRW model. (**a**) Back view; (**b**) top view.  
where \(v\) is the longitudinal forward speed of the carrier, and \(b\) is half the lateral distance between the left and right wheels.  
When calculating creep forces acting on the contact patches of the IRW model on the rail, these two different longitudinal forward speeds are adopted to improve model accuracy, which makes it different from the SW in the creep force calculation [21]. For example, to calculate the lateral creep force generated by the lateral speed for a SW, the force is given by  
\[F_{cy(j)}=-2k_{22}\frac{\dot{y}}{v} \tag{3}\]  
while on the IRW model, the left wheel, right wheel, and carrier are three different parts of the mechanical system, meaning three different translational speeds; thus, this creep force should be written as  
\[F_{cy(j)}=F_{Loy(j)}+F_{Rcy(j)}=-k_{22}\frac{\dot{y}}{v+b\dot{\psi}}-k_{22} \frac{\dot{y}}{v-b\dot{\psi}} \tag{4}\]  
where \(k_{22}\) is the lateral Kalker's creep coefficient.  
Other creep forces can be deduced as this pattern similarly. They are shown in Equations (28)-(31) at the end of Section 2.4 in detail.
## 6 Experimental Results
### Sensitivity to the Operating Conditions  
As far as the proposed technique is concerned, the effect of the load current and the input voltage were further investigated to assess its effectiveness in a real case scenario. The CM EMI shown in Figure 16a refers to different values of output current, more precisely 1, 2, 3, 4, 5 A, with the source inductance fixed to its optimal value, i.e., \(L_{\mathrm{s}}\) = 6.6 nH. As previously mentioned, such a value of source inductance is optimal for \(I_{\mathrm{LOAD}}\) = 3 A, whereas lower and higher values of current result in higher CM EMI. More precisely, for a 2 A and 5 A output current, the frequency peak is 5 dB higher than in the optimal case, whereas, for \(I_{\mathrm{LOAD}}\) = 1 A, it is 10 dB higher. In any case, the worst case, i.e., \(I_{\mathrm{LOAD}}\) = 1 A, corresponds to a peak reduction with respect to the oscillating case of 10 dB, which is not negligible. As far as input voltage variations are concerned, the corresponding CM EMI are shown in Figure 16b for \(V_{\mathrm{PS}}\) = 36, 42, 48, 52 V. Indeed, variations on the input voltage results in a different junction capacitance of the Schottky diode as the reverse voltage across \(D_{\mathrm{LS}}\) changes, meaning that the values of the equivalent resonant circuit are modified with respect to the nominal case, i.e., \(V_{\mathrm{PS}}\) = 48 V. Additionally, in this case, the 6.6 nH source inductance does not completely removed the peak at \(f_{\mathrm{r,on}}\) for input voltage values lower than the nominal one, even though its is strongly reduced with respect to the oscillating case.  
Figure 15: Measured CM of the designed Buck converter with 3 A output current, no damping and different values (\(3.7,6.6,12.5,25\,\mathrm{nH}\)) of the source inductance. The 6.6 nH value corresponds to the optimal case.
# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 2 Background
### 3D Gray-Level Co-Occurrence Matrix (3D GLCM)  
The 2D GLCM was extended to 3D [11] in order to be able to capture information between different slices of a volumetric image. The displacement vector has three components as defined in (10):  
\[\text{d}\ =\ (\text{d}_{\text{x}},\text{d}_{\text{y}},\text{d}_{\text{z}}) \tag{10}\]  
In this case, the orientation is defined for 13 independent directions. Table 2 shows the 13 corresponding displacement vectors.  
Let I be an input volumetric image of size m \(\times\) n \(\times\) p voxels. The 3D GLCM of I is given in (11) and its normalized version in (12):  
\[\text{GLCM\_3D}_{\text{d}_{\text{x}},\text{d}_{\text{y}},\text{d}_{\text{z}}} (\text{i},\text{j})=\sum_{\text{x}=1}^{\text{n}}\sum_{\text{y}=1}^{\text{m}} \sum_{\text{z}=1}^{\text{p}}\left\{\begin{array}{ll}1,&\text{if }\text{I}(\text{x},\text{y},\text{z})= \text{ i and }\text{I}\Big{(}\text{x}+\text{d}_{\text{x}},\text{y}+\text{d}_{\text{y}}, \text{z}+\text{d}_{\text{z}}\Big{)}=\text{ j}\\ &\\ &0,\text{ otherwise}\end{array}\right. \tag{11}\]  
\[\text{GLCM\_3D}_{\text{n}}(\text{i},\text{j})=\frac{\text{GLCM\_3D}(\text{i},\text{j})}{\sum_{\text{i}=1}^{\text{Nq}}\sum_{\text{j}=1}^{\text{Nq}}\text{ GLCM\_3D}(\text{i},\text{j})} \tag{12}\]  
where GLCM_3D\({}_{\text{n}}\) is the normalized version of GLCM_3D.  
We also had prior contributions in this thematic area by proposing a 3D version of the BM3 DLEBP operator and a more discriminative 3D GLCM based on which we built the  
\begin{table}
\begin{tabular}{c} \hline \hline
**Displacement Vector** \\ \hline (distance, 0, distance) \\ (distance, 0, 0) \\ (distance,0, \(-\)distance) \\ (distance, distance, distance) \\ (distance, distance, 0) \\ (distance, distance, \(-\)distance) \\ (0, distance, distance) \\ (0, distance, \(-\)distance) \\ (\(-\)distance, distance, \(-\)distance) \\ (0, distance, \(-\)distance) \\ (\(0,\) 0, distance) \\ \hline \hline \end{tabular} \({}^{\dagger}\) the distance between pixels in a pair = number of pixels moved in a certain direction.  
\end{table}
Table 2: Displacement vectors for 3D GLCM.  
proposed method. We describe the two proposed methods and the feature vectors' fusion in the subsequent section.
## 2 Materials and Methods
### Data for Evaluation of Models and Spatial Data Products  
For an evaluation of models and remotely sensed data products, we used eddy covariance (EC) flux tower data from the FLUXNET2015 dataset ([[https://fluxnet.fluxdata.org](https://fluxnet.fluxdata.org)]([https://fluxnet.fluxdata.org](https://fluxnet.fluxdata.org))) [37]. Our study included data from 151 EC tower sites that belonged to the following 12 terrestrial biomes: Croplands (CRO), closed Shrublands (CSH), deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), evergreen needleleaf forest (ENF), grasslands (GRA), mixed forests (MF), open shrublands (OSH), savannas (SAV), permanent wetlands (WET), and woody savannas (WSA). We used two criteria to filter the data, following [6]: (1) If more than 20% of the data in a given year was missing, the whole year was discarded, and (2) after this first step of processing, EC-tower sites with records for less than two years were completely discarded. EC-towers details are in Table A1.  
To simulate global GPP, the following spatial data products were used: (1) Meteorological data fields--radiation, air temperature, latent heat flux, and sensible heat flux--derived from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2), (2) MODIS satellite products including enhanced vegetation index (EVI) and normalized difference wetness index (NDWI), and (3) global atmospheric CO\({}_{2}\) concentration product from the Earth System Research Laboratory Global Monitoring Division ([[https://www.esrl.noaa.gov/gmd/dv/site/](https://www.esrl.noaa.gov/gmd/dv/site/)]([https://www.esrl.noaa.gov/gmd/dv/site/](https://www.esrl.noaa.gov/gmd/dv/site/))).
## 2 Design Process
### Traffic Analysis  
This type of traffic requires specific and accurate analyses of the traffic demand to serve, which affects the design of the appropriate geometric and structural features, and its control, to ensure regular and safe flows at road intersections. The demand analysis required a survey of traffic flows, to collect data on traffic volume and components. Each type of vehicle surveyed was classified by means of a traffic monitoring system. Traffic was surveyed for longer periods in the daytime. Monitoring (point P in Figure 2) involved both directions of the busiest arm, thus highlighting where highest traffic flows can be located (red in Figure 2).  
Figure 1: The intersection to redesign (photo: Google maps).  
Table 2 reports data collected during early morning time.  
The observed traffic volume of the vehicles from all the arms does not exceed 500 vehicles/h, therefore according to [PERSON] [32] there is no need for evaluation of traffic capacity of the one-lane roundabout.  
The analysis of traffic composition highlighted that volume of motorcycles is negligible, whereas heavy vehicles account for an average 15% of the total volume of passenger cars and heavy vehicles (Figures 3 and 4). More specifically, heavy vehicles were 21% and 12% of total traffic respectively for directions A and B represented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Time**} & **Motorcycles** & **Passenger Cars** & **Heavy Vehicles** \\ \cline{2-4}  & & **Units** & \\ \hline
5:30–5:45 a.m. & 0 & 22 & 1 \\
5:45–6:00 a.m. & 2 & 19 & 0 \\
6:00–6:15 a.m. & 0 & 19 & 5 \\
6:15–6:30 a.m. & 0 & 24 & 2 \\
6:30–6:45 a.m. & 0 & 37 & 5 \\
6:45–7:00 a.m. & 2 & 35 & 11 \\
7:00–7:15 a.m. & 1 & 37 & 7 \\
7:15–7:30 a.m. & 0 & 36 & 4 \\
7:30–7:45 a.m. & 1 & 41 & 6 \\
7:45–8:00 a.m. & 1 & 43 & 8 \\ \hline Total & 7 & 313 & 49 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Observed vehicles in both directions.  
Figure 2: Monitored traffic scenario (photo: Google maps).
# Correction of Aero-Optical Effect with Blow-Suction Control for Hypersonic Vehicles
###### Abstract  
High-speed turbulence induces significant aero-optical effects that severely disrupt the functionality of imaging systems of hypersonic vehicles. In this study, the aero-optical correction of various jet cooling modes is investigated using a Terminal High Altitude Area Defense (THAAD)-like seeker model and the imaging impact of high-speed flow field and flow control on the optical window is analyzed by the Delayed Detached Eddy Simulation (DDES) method. The findings reveal that a jet mode parallel to the window exhibits better cooling effectiveness compared to a perpendicular jet mode along the body axis; however, it introduces additional wavefront distortion, leading to degraded imaging quality. Although micro-vortex generators (MVGs) can reduce density fluctuations near the window from a refractive index perspective, they do not effectively mitigate wavefront distortion or improve window cooling efficiency. Finally, incorporating suction control, a comprehensive flow control solution, significantly improves the flow field structure near the window, resulting in a more uniform temperature distribution and reduced wavefront distortion. Applying this flow control method results in a 14.7% reduction in wavefront distortion at 3 Ma and an approximately 20% maximum value reduction at 5 Ma. This study proposes a novel and comprehensive flow control method to effectively mitigate the aero-optical effect in hypersonic flows, providing a new avenue for subsequent researchers in this field.  
seeker head; aero-optical effect; flow control; cooling; micro-vortex generator