esa-sceva/satcom-chunk-collection huggingface dataset:
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 3 Methodology
### Urban Functional Unit Division  
The urban functional unit division methods include grid method, block method, traffic analysis zone method, and geoscene segmentation method [47; 48; 49; 50]. The block method is simple and can describe the real boundaries of UFZs and is widely used in UFZs identification [51]. In this study, we utilize the blocks defined by the OSM road network as the basic unit for UFZs identification. To ensure precise block boundaries, we preprocess the OSM road network. First, low-level roads are removed and retain tertiary, secondary, primary, motorway, and trunk roads. We determine road widths for different levels and create road buffer zones using Chinese urban road standards and remote sensing images. The ArcGIS software's Erase function is then used to remove the road buffer zone polygons within the Fourth Ring Road to obtain separate urban functional units. We obtain a total of 1971 urban functional units, each assigned a unique ID. To refine the boundaries further, we consult Baidu maps to determine the functional types of the urban functional units and make necessary corrections. Figure 5 shows the process of urban functional unit division.
## 4 Tracking Control Based on EKF
### Successively Linearized Prediction Models
Linearize the nonlinear dynamics at the seed trajectories \(\big{(}\mathbf{x}^{0}(i|k),\mathbf{u}^{0}(i|k),\mathbf{b}^{0}(i|k)\big{)}\) for \(i=0,1,\ldots,N_{\mathrm{P}}-1\) as: \[\mathbf{x}(i+1|k)=\mathbf{x}^{0}(i+1|k)+\mathbf{A}(i|k)\Delta\mathbf{x}(i|k)+\mathbf{B}(i|k)\Delta \mathbf{u}(i|k)+\mathbf{E}(i|k)\Delta\mathbf{b}(i|k)\] (22) where \[\Delta\mathbf{x}(i|k) =\mathbf{x}(i|k)-\mathbf{x}^{0}(i|k)\] (23) \[\Delta\mathbf{u}(i|k) =\mathbf{u}(i|k)-\mathbf{u}^{0}(i|k)\] (24) \[\Delta\mathbf{b}(i|k) =\mathbf{b}(i|k)-\mathbf{b}^{0}(i|k)\] (25) are small perturbations around \(\mathbf{x}^{0}(i|k)\), \(\mathbf{u}^{0}(i|k)\) and \(\mathbf{b}^{0}(i|k)\), respectively; \(\mathbf{A}(i|k)\), \(\mathbf{B}(i|k)\), and \(\mathbf{E}(i|k)\) are the corresponding Jacobian matrices, respectively, of the nonlinear dynamics function Equation (20) evaluated at \(\big{(}\mathbf{x}^{0}(i|k),\mathbf{u}^{0}(i|k),\mathbf{b}^{0}(i|k)\big{)}\).
## 3 System Model  
A cache-enabled wireless network is depicted in Figure 1, where cache-enabled SBSs are denoted by \(\mathcal{S}=\{1,2,\ldots,s,\ldots,S\}\). The cache capacity of each SBS is equal to \(\mathcal{C}\). The users existing in the network are denoted by \(\mathcal{U}=\{1,2,\ldots,u,\ldots,U\}\). The distribution of SBSs and users follow Poisson Point Process (PPP) with intensity \(\lambda_{S}\) and \(\lambda_{U}\), respectively. The users connected with serving SBS \(s\) are represented by a binary element \(l_{s,\mu}\), where \(l_{s,\mu}=1\) if user \(u\) is connected to SBS \(s\), otherwise \(l_{s,\mu}=0\). We consider that the network contains a data library of \(F\) contents represented by the set \(\mathcal{F}=\{1,2,\ldots,f,\ldots,F\}\), where each content is of equal size \(|f|\). Then, we used a Zipf distribution to capture the spatial heterogeneity of the data popularity profile [31] as follows:  
\[\vartheta_{f}=\frac{f^{-\gamma}}{\sum\limits_{i=1}^{F}i^{-\gamma}}, \tag{1}\]  
where \(\gamma\geq 0\) denotes the contents popularity distribution skewness that controls spatial heterogeneity of contents popularity. It is imperative to design an optimal caching strategy \(X\) to improve data access via cache resources. We define a decision variable \(x_{sf}\) to determine whether content \(f\) is cached by serving SBS \(s\) or not. If \(x_{sf}=1\), then content \(f\) is cached by SBS \(s\), otherwise it is equal to 0. Thus, when a user \(u\) requests content \(f\) from serving SBS \(s\), then SBS \(s\) decides transmission of content \(f\) based on the caching strategy \(X\).
# A Proposal for a Zero-Trust-Based Multi-Level Security Model and Its Security Controls
## 5 Conclusions  
### Research Summary  
The increasing utilisation of ICT technologies has played a significant role in enhancing organisational productivity. However, traditional network segregation systems have become a barrier to adopting new ICT technologies. While research into MLS frameworks is being conducted to overcome these limitations, the static access control approach of traditional MLS frameworks remains inadequate for effectively addressing dynamic security threats. Consequently, there is a growing need for research to develop a new security model that integrates the Zero Trust model, which supports dynamic access control, with the MLS framework.  
This paper proposes a Zero-Trust-based MLS model and identifies potential risks through an impact analysis based on data classification by sensitivity. Additionally, to mitigate the identified risks, it systematically provides security controls to be applied to the components of the proposed security model and evaluates the model's security effectiveness through attack scenarios. This study aims to present a new security strategy for improving network segregation by designing a Zero-Trust-based MLS model capable of implementing dynamic access control and deriving suitable security controls for its components. Notably, this research holds significant importance, as it integrates the MLS framework announced in the Republic of Korea in September 2024 with the new security paradigm of Zero Trust, offering a developmental direction for future Zero-Trust-based MLS frameworks. The proposed security model serves as a strategic alternative by improving the traditional perimeter-focused network segregation system, enabling the adoption of  
Figure 7: Attack Scenario 2.  
new technologies, and enhancing productivity. Simultaneously, it addresses the issue of reduced security levels in the MLS framework by leveraging Zero Trust principles, thereby enhancing both information usability and security.
## 3 Results and Discussion
### Physical Properties
#### 3.1.2 Wave Velocity  
Wave velocity refers to the speed at which sound waves propagate through the grouting reinforcement body, offering valuable insight into the internal structure and density of the material. This information is essential for evaluating its mechanical properties and suitability for engineering applications. The wave velocity loss of grouted solids, measured before and after freeze-thaw cycles, serves as an indicator of the internal structural changes occurring within the material during these cycles. Wave velocity loss is commonly expressed as the wave velocity loss rate, which is calculated using the following Equation:  
\[K_{v}=\frac{v_{0}-v_{n}}{v_{0}}\times 100\% \tag{4}\]  
where \(K_{v}\) is wave velocity loss rate (%), \(v_{0}\) denotes the wave velocity before freezing and thawing (g), and \(v_{n}\) is the wave velocity after \(n\) freeze-thaw cycles (g) for \(n\) = 5, 10, 15, 20, 25, 30.  
Figure 14 illustrates the longitudinal wave velocity and wave velocity loss rate of the grouting reinforcement body as the number of freeze-thaw cycles increases. The figure reveals that the trend in wave velocity loss across grouting reinforcement bodies with varying crack dip angles follows a generally consistent pattern. As the number of freeze-thaw cycles increases, the wave velocity of the grouting reinforcement body decreases, while the wave velocity loss rate correspondingly rises. A rapid decline in wave velocity occurs after 15 freeze-thaw cycles, with the wave velocity loss rate exceeding 25% after 30 cycles. This phenomenon occurs because, during the freeze-thaw cycles, internal pores are continuously subjected to water migration and frost heaving forces, causing both the expansion of existing pores and cracks and the formation of new ones. As waves pass through these pores and cracks, refraction, reflection, and energy dissipation occur, leading to a reduction in wave velocity. Consequently, as the number of pores and cracks increases, the loss rate of longitudinal wave velocity also increases.  
Figure 13: Mass and mass loss rate of grouting reinforcement body.
## 5 Operation of the Switch  
### Basic Circuitry of the Switch  
For the implementation of the switch, we consider standard power harvesting and transmission specifications suitable for indoor IoT environments. The RF energy harvester is designed to operate with typical indoor ambient RF power density levels. The harvesting antenna's aperture size is selected to maintain reasonable harvesting efficiency while being practical for warehouse deployment. For power transmission, we utilize standard omidirectional antennas operating at industrial IoT frequencies, suitable for indoor environments with multiple reflections. The transmission circuit is designed to operate within standard indoor wireless transmission power limits.  
In SWIPT mode, the power harvesting circuit employs a standard rectenna configuration with a matching network designed for optimal power transfer at the operating frequency. For ABCS operation, the backscatter antenna is designed with a load impedance that can be switched between two states to achieve amplitude shift keying modulation. The reflection coefficient \(\alpha\) is set as indicated in Table 1, which provides a balance between reflection efficiency and power consumption.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Length of the warehouse space in meters (L) & 6 \\ \hline Width of the warehouse space in meters (M) & 6 \\ \hline Height of R in meters & 5 \\ \hline Height of D in meters & 2 \\ \hline R position in the x,y plane & (3.5, 3.5) \\ \hline D position in the x,y plane & (L, M) \\ \hline P\_t (transmission power) & 20 \\ \hline pathloss exponent (m) & 2 \\ \hline \(\rho\) (power splitting ratio for SWIPT) & 0.8 \\ \hline \(\sigma_{SR}\), \(\sigma_{RD}\) & 0.5 \\ \hline A (reflection coefficient for ABCS) & 0.5 \\ \hline Hysteresis factor & 0.05 \\ \hline Minimum data rate threshold & 10 \\ \hline Minimum energy harvesting threshold & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 4 Experimental Results and Discussions
### Deeper Analysis of OBBO  
In order to intuitively show its drawbacks, the simulation of OBBO algorithm for large scale TSP, ch130, is firstly conducted with the setting listed in Table 1 as in [28]. We show that by using Monte-Carlo method.  
The optimal solutions are obtained at the end of 500 generations from both BBO and OBBO. Therefore, the number of candidate solutions explored by BBO is 25000 for each independent Monte-Carlo run. While the mean of candidate solutions explored by OBBO is \(32446.5\pm 485.7132\) over 200 independent Monte-Carlo simulations. From this viewpoint, it is no surprise that compared with BBO algorithm, the performance of OBBO may be improved as illustrated in [28] for its larger possibility space explored.  
The utilization rate of opposite paths is recorded and shown. From Figure 7, we can see that it is decreasing from 50% to 5% sharply over 6 times. After 12 times, it is very close to zero, which meansthe opposite paths cannot be selected as elitist solutions and then survived into the next generation. What deserves special mention is that the horizontal ordinate is the number of opposition-based generation jumping, not the number of generation (iteration).
# Bias Truck Tire Deformation Analysis with Finite Element Modeling
###### Abstract  
This paper presents a method for modeling of pneumatic bias tire axisymmetric deformation. A previously developed model of all-steel radial tire was expanded to include the non-linear stress-strain relationship for textile cord and its thermal shrinkage. Variable cord density and cord angle in the cord-rubber bias tire composite are the major challenges in pneumatic tire modeling. The variabilities result from the tire formation process, and they were taken into account in the model. Mechanical properties of the composite were described using a technique of orthotropic reinforcement overlaying onto isotropic rubber elements, treated as a hyperelastic incompressible material. Due to large displacements, the non-linear problem was solved using total Lagrangian formulation. The model uses MSC.Marc code with implemented user subroutines, allowing for the description of the tire specific properties. The efficiency of the model was verified in the simulation of mounting and inflation of an actual bias truck tire. The shrinkage negligence effect on cord forces and on displacements was examined. A method of investigating the influence of variation of cord angle in green body plies on tire apparent lateral stiffness was proposed. The created model is stable, ensuring convergent solutions even with large deformations. Inflated tire sizes predicted by the model are consistent with the actual tire sizes. The distinguishing feature of the developed model from other ones is the exact determination of the cord angles in a vulcanized tire and the possibility of simulation with the tire mounting on the rim and with cord thermal shrinkage taken into account. The model may be an effective tool in bias tire design.  
bias tire; textile cord; shrinkage; rubber; inflation analysis; finite element modeling +
Footnote †: journal: Applied Sciences
## 4 Test Results and Insights
### Task Success and Time  
Overall, most of the participants were able to complete the experiment. The task was marked as failed (negative) automatically when subjects could not finish it on time. During the fourth task involving the Stratos in the file explorer, two users needed more time to finish the task due to incorrect user input. In the first task, the start task, one Stratos user and four trackpad users selected the wrong menu item, resulting in task failure. The results for the completion status of each task are presented in Table 2.  
The average completion time for the task between the two groups is similar (Figure 9). However, the post hoc data analysis does not statistically confirm the assumption.  
Due to the limited number of subjects and the non-normality distribution of the sample, a Mann-Whitney U test with a significant difference \(\alpha=0.05\) was performed to check the null hypothesis (\(H_{0}\)). As shown in Table 3, the \(p\)-value is always higher than \(\alpha\), and the null hypothesis cannot be statistically confirmed. It is essential to mention that we examined only the time of successful users when considering the file explorer task, removing the instances of the only two participants who could not finish the task within 5 min.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Input Modality** & **Task** & **Positive** & & **Negative** \\ \hline Stratos & Start & 9 & 90\% & 1 & 10\% \\  & Music & 10 & 100\% & - & - \\  & Call & 10 & 100\% & - & - \\  & File Explorer & 8 & 80\% & 2 * & 20\% \\  & POI & 10 & 100\% & - & - \\ \hline Trackpad & Start & 6 & 60\% & 4 & 40\% \\  & Music & 10 & 100\% & - & - \\  & Call & 10 & 100\% & - & - \\  & File Explorer & 10 & 100\% & - & - \\  & POI & 10 & 100\% & - & - \\ \hline \hline \end{tabular}
* These were automatically set as negative because the users took more than five minutes to complete the task.  
\end{table}
Table 2: Completion status per task.  
Figure 9: Mean task completion time (in seconds); * the failed attempts are not used to calculate the mean.
## 3 The FQS Algorithm
### An Example
#### 3.4.3 Searching Pattern \(P\) in \(T\)  
After preprocessing steps, the search phase begins.  
**Attempt 1:** The first attempt compares the pattern, \(P\), to the text, \(T\), from the beginning, as shown in Figure 1. Because the maximum of expected shift (\(pos\)) is three (\(pos=3\)), the comparison starts at \(P[3]=G\) against the corresponding position in text \(T[j+pos]=T[0+3]=T[3]\). This will be the symbol, \({}^{\prime}T^{\prime}\), thus leading to a mismatch. The algorithm shifts the pattern, \(P\), to the next position with the shift distance determined by \(next[T[3]]=next[T]=4\). Additionally, the value of \(j\) is updated to \(j=j+next[T[3]]=4\).  
**Attempt 2:** The second attempt is shown in Figure 2. The algorithm still starts to compare \(P[3]=G\) to the corresponding position in text \(T[j+pos]=T[4+3]=T[7]=A\). It is still a mismatch. The shift distance is \(next[T[7]]=next[A]=1\). The value of \(j\) is updated to \(j=j+next[T[7]]=4+1=5\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c  
**Attempt 2**  
The third attempt is shown in Figure 3. The algorithm compares \(P[3]=G\) to text \(T[j+pos]=T[5+3]=T[8]=G\). The characters match. Then, the algorithm proceeds as the classic QS algorithm. After a one by one comparison, the algorithm finds an exact match here. It reports the occurrence position and determines the shift distance, \(j\). This shift distance is determined by the classic QS algorithm \(j=j+shift[T[j+m]]=5+shift[T[5+8]]=5+shift[T[13]]=5+shift[T]=5+4=9\).  
**Attempt 4**: The fourth attempt is shown in Figure 4. Algorithm FQS compares \(P[3]=G\) to text \(T[j+pos]=T[9+3]=G\). Since the symbols match, the algorithm follows the classic QS algorithm steps by comparing from right to left. The pattern's rightmost character is \(G\), which does not match the corresponding symbol, \(A\) in \(T\). The algorithm determines the next shift distance \(shift[T[j+m]]=shift[T[9+8]]
## Abbreviations  
**Glossary of Terminology and Abbreviations in Tables 1 and 2**  
ABI Advanced Baseline Imager  
AIREP Aircraft Report  
AIRS Atmospheric Infrared Sounder  
AMDAR Aircraft Meteorological Data Relay  
AMI Advanced Meteorological Imager  
AMSU-A Advanced Microwave Sounding Unit-A  
AMV Atmospheric Motion Vector  
ATMS Advanced Technology Microwave Sounder  
ATOVS Advanced TIROS Operational Vertical Sounder  
Aqua Satellite in EOS series  
AVHRR Advanced Very High-Resolution Radiometer  
BOGUS Bogus observations generated by national meteorological centers  
COSMIC Constellation Observing System for Meteorology Ionosphere and Climate  
CrIS Cross-track Infrared Sounder  
DMSP Defense Meteorological Satellite Program  
EOS Earth Observing System  
EUMETSAT European Organization for the Exploitation of Meteorological Satellites  
GEO Geosynchronous-orbiting (satellite)  
GOES Geostationary Operational Environmental Satellite  
GPSRO Global Positioning System Radio Occultation  
GRAS GNSS Receiver for Atmospheric Sounding  
EUMETSAT European Organization for the Exploitation of Meteorological Satellites  
GEO Geosynchronous-orbiting (satellite)  
GOES Geostationary Operational Environmental Satellite  
GPSRO Global Positioning System Radio Occultation  
GRAS GNSS Receiver for Atmospheric Sounding  
HIMS High Resolution Infrared Radiation Sounder  
IASI Infrared Atmospheric Sounding Interferometer  
JMA Japan Meteorological Agency  
LEO Low-Earth orbiting (satellite)  
METAR Meteorological Aerodrome Report  
MetOp Meteorological Operational satellite series operated by EUMETSAT  
MHS Microwave Humidity Sounder  
MODIS Moderate Resolution Imaging Spectroradiometer  
NEXRAD Next Generation Weather Radar  
NOAA National Oceanic and Atmospheric Administration  
PIBAL Pilot balloon (Upper-level wind observation)  
S-NPP Suomi National Polar-orbiting Partnership  
SAR Synthetic Aperture Radar  
SSMIS Special Sensor Microwave Imager/Sounder  
TanDEM-X German SAR Earth observation satellite  
Terra Satellite in EOS series  
TerraSAR-X German SAR Earth observation satellite
## 5 Microarray Results
\begin{table}
\begin{tabular}{l l l l l} \hline \multicolumn{2}{c}{**Gene Symbol**} & \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{**Fold Change**} & \multicolumn{1}{c}{_p-Value_} \\ \hline
1 & Hrasls & HRAS-like suppressor & \(-4.67\) & \(1.44\times 10^{-6}\) \\ \hline
2 & Trim12a & tripartite motif-containing 12A & \(-3.72\) & \(0.0004\) \\ \hline
3 & Ppp1r3c & protein phosphatase 1, regulatory & \(-2.84\) & \(0.0039\) \\  & (inhibitor) subunit 3C & & & \\ \hline
4 & AA467197; Mir147 & expressed sequence AA467197; microRNA 147 & \(-2.73\) & \(0.0002\) \\ \hline
5 & Cpxm2 & carboxypeptidase X 2 (M14 family) & \(-2.61\) & \(6.47\times 10^{-5}\) \\ \hline
6 & C7 & complement component 7 & \(-2.23\) & \(1.52\times 10^{-5}\) \\ \hline
7 & Syt12 & synaptotagmin XII & \(-2.16\) & \(0.0154\) \\ \hline
8 & Mmp8 & matrix metallopeptidase 8 & \(-2.05\) & \(0.0492\) \\ \hline
9 & Entpd4; Gm21685 & \multicolumn{3}{c}{econucleoside triphosphate} \\  & & diphosphohydrolase 4; predicted & \(-2.04\) & \(1.35\times 10^{-6}\) \\  & & gene, 21685 & & \\ \hline
10 & Aldob & aldolase B, fructose-bisphosphate & \(-2.03\) & \(3.35\times 10^{-5}\) \\ \hline
11 & Serpina3n & serine (or cysteine) peptidase inhibitor, clade A, member 3N & \(-2.01\) & \(0.0214\) \\ \hline
12 & Cle10a & C-type lectin domain family 10, member A & \(-2\) & \(0.002\) \\ \hline
13 & Prg4 & proteoglycan 4 & \((\)megakaryocyte-stimulating factor, articular superficial zone protein\()\) & \(-1.91\) & \(0.0237\) \\ \hline
14 & Lrg1 & leucine-rich alpha-2-glycoprotein 1 & \(-1.9\) & \(0.0019\) \\ \hline
15 & Ngp & neutrophil granule protein & \(-1.9\) & \(0.0133\) \\ \hline
16 & Serpina3c & serine (or cysteine) peptidase inhibitor, clade A, member 3C & \(-1.89\) & \(0.0033\) \\ \hline
17 & Scn4b & sodium channel, type IV, beta & \(-1.89\) & \(0.0045\) \\ \hline
18 & Slc17a7 & \multicolumn{3}{c}{solute carrier family 17} \\  & & (sodium-dependent inorganic phosphate & \(-1.88\) & \(5.44\times 10^{-6}\) \\  & & cotransporter), member 7 & & \\ \hline
19 & II33 & interleukin 33 & \(-1.87\) & \(0.0309\) \\ \hline
20 & Bdh1 & 3-hydroxybutyrate dehydrogenase, type 1 & \(-1.84\) & \(0.0003\) \\ \hline
21 & Vsig4 & V-set and immunoglobulin domain & \(-1.84\) & \(0.0009\) \\  & & containing 4 & & \\ \hline
22 & Ahsg & alpha-2-HS-glycoprotein & \(-1.83\) & \(0.0002\) \\ \hline
23 & Nm1 & neuritin 1 & \(-1.83\) & \(0.0063\) \\ \hline
24 & Ly6g & lymphocyte antigen 6 complex, locus G & \(-1.83\) & \(0.0242\) \\ \hline
25 & Zfp697 & zinc finger protein 697 & \(-1.82\) & \(0.0179\) \\ \hline
26 & Cd300 ld & CD300 molecule-like family member d & \(-1.79\) & \(0.0018\) \\ \hline
27 & Ptgds & prostaglandin D2 synthase (brain) & \(-1.78\) & \(0.0001\) \\ \hline
28 & Mgst1 & microsomal glutathione S-transferase 1 & \(-1.75\) & \(0.0087\) \\ \hline
29 & Aox1 & aldehyde oxidase 1 & \(-1.73\) & \(0.0004\) \\ \hline
30 & Scarb2 & scavenger receptor class B, member 2 & \(-1.72\) & \(3.49\times 10^{-6}\) \\ \hline \end{tabular}
\end{table} 
Table 2: List of selected downregulated genes.