esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended SSH Model in Non-Hermitian Waveguides with Alternating Real and Imaginary Couplings
## 2 Geometry and Theoretical Model
This is different from the original SSH model and PT-symmetric SSH model, where the band touch points take place at the boundary of the Brillion zone as \(\varphi=\pi\). At the exceptional points, the two eigenvalues are zero, and the eigenstates will coalesce. Apart from these points, the eigenvalues are complex. There is no special condition for real nor imaginary eigenvalues. This is also in contrast to PT symmetric systems. The topology invariant of the system is generally characterized by the Berry phase. The Berry phase for individual bands is identified by  
\[\varphi_{\rm B}^{\pm}=\oint_{k}i(\mu_{\pm}|d/dk|\lambda_{\pm})dk, \tag{4}\]  
where \(\pm\) marks two Bloch bands, and \(k\) represents Bloch wavevector. \(|\lambda\rangle\) and \(\langle\mu|\) are the right and left eigenvectors of Hamiltonian H, respectively. We numerically calculated the Berry phase, and the result is presented in Figure 2. As imaginary coupling was introduced, we obtained H # H\({}^{+}\), and the system was non-Hermitian. However, the Berry phase of two individual bands remained quantized,  
Figure 1: The geometry of the proposed Su–Schrieffer–Heeger (SSH) chain. (**a**) The suggested chain with assistant lattice. (**b**) The effective chain after adiabatic elimination. (**c**) The geometry of proposed planar waveguide. which was zero for \(c_{2}>c_{1}\) and \(\pi\) for \(c_{2}<c_{1}\). The quantized Berry phase aids in chiral symmetry [42]. Chiral symmetry is the result of vanished onsite energy, which is broken with non-zero onsite energy. The condition of the topological phase transition is similar to that in the general SSH model and PT-symmetric systems.
## 3 Results
### Effects of EC on TPH-1, TPH-2, and SERT mRNA Expression  
To determine the cytotoxicity effect of EC, we used treatments with various concentrations of TC ranging from 1-1000 ug/mg for 48, 72 h. As shown in Figure 4A, cell proliferation did not decrease in the presence of the TC. Figure 4B-D shows that the TPH1 mRNA expression level increased by about two times in the group treated with 50 and 100 ug/mL of EC extract compared with the untreated group. Phloroglucinol, eckol, dieckol, 6,6'-bieckol, and 8,8'-bieckol, the main components of EC, were found to be significantly higher in the treatment with 10 ug/mL concentration in all groups, except dieckol; however, in the case of phloroglucinol, depending on the concentration, the treatment concentration of 20 ug/mL also resulted in significantly higher cell proliferation. Similar to TPH1 mRNA expression, TPH2 mRNA expression showed an increase at a concentration of 10 ug/mL. Phloroglucinol and 8,8'-bieckol tended to show significantly increased expression. The mRNA expression of SERT was significantly higher in the 100 ug/mL of EC extract treatment group and a notable increase was observed with phloroglucinol and 8,8'-bieckol based on the concentration (Figure 4).  
Figure 3: Effects of EC extract on the serum bone biomarkers and bone mineral density. Bone metabolism markers (**A**) CTX, osteoclastcin, and PINP. (**B**) Bone mineral density (BMD), (**C**) percent bone volume (BV/TV), (**D**) trabecular separation (Tb.Sp), (**E**) trabecular thickness (Tb.Th), and (**F**) trabecular number (Tb.N) were photographed by micro-CT, and data were evaluated using analyzing software (CTan). a \(p\) < 0.05 vs. vehicle group. b \(p\) < 0.05 vs. OVX. c \(p\) < 0.05 vs. OR.  
Figure 4: Cytotoxicity effect of EC on RBL-2H3 cells and the effect of EC and its major constituents on the mRNA expression of serotonin synthesis enzymes and serotonin transporter. **(A)** Cell viability of RBL-2H3 treated with various concentrations of each component for 48 and 72 h. A total of \(1\times 10^{3}\) cells were plated into 96-well plates and then EC was added at different concentrations. After 48 and 72 h, the cells were incubated with MTS for 3 h. Absorbance was recorded at 490 nm. **(B–D)** Comparison of serotonin-related mRNA expression levels between control condition (non-treated) or treated with EC for 48 h. **(B)** TPH-1, **(C)** TPH-2, and **(D)** SERT mRNA expressions were tested using RT-PCR in the RBL-2H3 cell line. Experiments were conducted in triplicate and data are expressed as means \(\pm\) SD. \({}^{a}\)\(p<0.05\) versus the control group.
## 2 Materials and Methods
### Feature Engineering
#### 2.1.2 Frequency-Domain Signal Feature Engineering
There is a sequence of steps to achieve wavelet scattering coefficients as shown in Figure 1. First, the signal is convolved with the scaling function to obtain the zero-order scattering coefficient of the signal, \(S_{0x}\). For the second layer, the signal is convolved with the wavelet function, \(\psi\) (first-order filter), and the modulus is determined. This is then convolved with the scaling function to obtain the first-order wavelet scattering coefficient. When determining the next-order wavelet scattering coefficient, the original sequence information must be restored because averaging loses the high frequency information. Therefore, the next wavelet function (second-order filter) needs to be convolved to the original sequence information to ensure that information is not lost. For high-frequency information, in order to obtain a locally deformed and fixed-scale wavelet scattering coefficient, it is necessary to convolve the scaling function to obtain a stable wavelet scattering coefficient. \[S_{2}x[\lambda_{1}\lambda_{2}]=\big{|}\big{|}x(t)\star\psi_{\lambda_{1}}(t) \big{|}\star\psi_{\lambda_{2}}(t)\big{|}\star\varnothing_{I}(t) \tag{15}\]  
If the multiple wavelet functions (filters) are set, the network architecture can continue to iterate continuously on the next wavelet function:  
\[\psi_{(a,b)}(t)=\frac{1}{\sqrt{a}}\psi\left(\frac{t-b}{a}\right) \tag{9}\]0  
A series of wavelet scattering coefficient characteristics can then be obtained:  
\[\psi_{(a,b)}(t)=\frac{1}{\sqrt{a}}\psi\left(\frac{t-b}{a}\right) \tag{9}\]1  
The wavelet filter banks in multiple wavelet filter libraries are used to analyze the low- and high-frequencies of the complete signal information to obtain frequency domain information. In this study, we used a scaling function (low-pass filter) and two wavelet functions (high-pass filter) to obtain the wavelet scattering coefficients of the signal. Note that, normally, second-order wavelet scattering transform is sufficient because the energy attenuates at each level.
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
## 4 Performance Evaluation and Results Discussion
### Scenario and Parameters' Values  
The simulator used for conducting the experiments is OMNeT++ simulator [39] with the INET Framework. The algorithm was coded and implemented on top of the INET Framework. The edge routers are running the OSPFv2 routing protocol, while the core routers are running the BGP routing protocol. At the application layer, two kinds of traffic are generated, Constant Bit Rate (CBR) acting as a background traffic and HTTP used for testing and evaluating the RAPT algorithm. Two sets of experiments were run on the implemented RAPT algorithm over the INET Framework. Each simulation scenario test was run for 15 times in a random distribution of traffic generation. The simulation results are averaged, and the results are based on a transient state system. The following metrics have been recorded:1. _Detection time_: This is the time during which the edge router detects the possibility of an attack, and it is the period from the start of the simulation to the beginning of the tracing process;
2. _Number of SYN packets destined to the victim_: This represents the number of SYN packets destined to the victim from the beginning of the simulation until the start of the tracing process for each edge router;
3. _Tracing time_: The time it takes to trace the attack;
4. _Total processing time_: The total time elapsed by the routers on the attack path to carry out the tracing process;
5. _Convergence time_: The time taken by the algorithm to detect, trace and stop the attacks from all incoming paths until the victim receives legitimate SYN packets.
## 4 Results and Discussion
### Simulation Results  
#### 4.1.1 Installation Status Results  
Figure 7a shows the von Mises stress of the O-ring in the installation state. In the installation state at 15 \({}^{\circ}\)C and 0.1 MPa, two areas of high stress are observed in the middle of the O-ring, with the left side experiencing more severe stress concentration. This phenomenon is attributed to the compression pressure and shear force generated on contact surface I during axial propulsion. The results show that there will be stress concentration in the O-ring during work, and it is necessary to consider the stress characteristics of the O-ring.  
Figure 7: O–ring in the installation state: (**a**) von Mises stress; (**b**) contact pressure.  
Figure 7b shows the contact pressure of the O-ring in the installation state. In the installation state at 15 \({}^{\circ}\)C and 0.1 MPa, contact surface I and contact surface II generate contact pressure that ensures sealing of the contact surfaces. This shows that the rubber material can be closely fitted with the contact surface to achieve a good sealing effect.
## 3 Results
Figure 7a shows log power values in the range of 38.5 \(\upmu\)V\({}^{2}\) to 41.5 \(\upmu\)V\({}^{2}\), while Figure 7b shows log power values in the range of 37 \(\upmu\)V\({}^{2}\) to 39.5 \(\upmu\)V\({}^{2}\). Figure 6: (**a**) Log power spectrum of attention within continuous noise; (**b**) Log power spectrum of attention within intermittent noise. Figure 7: (**a**) Log power spectrum of stress caused by continuous noise; (**b**) Log power spectrum of stress caused by intermittent noise. The cognitive aspect of mental workload is measured based on the theta-to-alpha ratio, which was calculated by the alpha band strength over the frontal channels AF3, AF4, F3, F4, F7, F8, FC5, and FC6 [35; 63; 68; 69]. The calculation of the theta-to-alpha ratio is performed using the mean value of the PSD alpha band and the average PSD value of the theta band [69]. The mental indices of continuous and intermittent noise on workload are shown in Figure 8.  
Figure 8 shows that the increase in mental workload index value in continuous and intermittent noise is linear to the noise level. The greater the noise, the greater the mental workload index value. Based on Figure 8, the continuous noise mental workload index value is greater than the intermittent noise mental workload index. In this study, topographic mapping results are shown based on the frontal areas of participants at each noise level in Figure 9a,b. Topography in the study is based on the power spectrum density value scores from \(-2\) uV\({}^{2}\) to \(2\) uV\({}^{2}\) which are represented in blue as a score with a negative value and red as a score with a positive value. The electrode placement on participants is the AF3 and AF4 electrodes placed on the frontal area located just above the eye area, the F3 and F4 electrodes at the front of the head around the prefrontal area, and the F7 and F8 electrodes in the lateral frontal area. Figure 8: Relationship between the mental workload index and continuous and intermittent noise levels. Figure 9: (**a**) Topographic mapping of the relative power of frequency bands during continuous noise exposure; (**b**) Topographic mapping of the relative power of frequency bands during intermittent noise exposure. The topography at electrodes AF3, AF4, F3, F4, F7, and F8 marked in red at the frontal lobe position shows high power spectrum density values indicating the strong activation of frontal brain areas associated with attentional processing, stress, and mental workload. Topographic results marked in green show less active brainwave activity with low power spectrum density values.
# Design of Positioning Mechanism Fit Clearances Based on On-Orbit Re-Orientation Accuracy
### Principle of Kinematic Positioning  
Figure 1 shows the kinematic positioning method with three positioning balls (\(a\), \(b\), and \(c\)). Ball \(a\) was located in the cone, which limited three translational degrees of freedom (DOF) of the rigid body and released three rotational DOF. Ball \(b\) was located in the V-groove that limited the rotational DOF around the \(z\)-axis and the line perpendicular to \(ab\). Ball \(c\) was located on the flat that limited the rotational DOF around \(ab\). Thus, the rigid body was limited without any over-constraint. When the temperature changed, ball \(b\) moved along the \(y\)-axis and ball \(c\) moved on the \(x\)-\(y\) plane (Figure 2). Thus, the uniform deformation of the rigid body was guaranteed without harmful thermal stress.  
Figure 1: Kinematic positioning method. The three positioning balls are on the \(x\)–\(y\) plane.
# Biology and Regulation of Staphylococcusococcal Biofilm
## 4 Activity of the _ica_ Operon Encoded Enzymes  
In the same year that the structure of PIA was published, the corresponding biosynthesis genes, clustered in the _ica_ operon, were also identified in _S. epidermidis_[39; 40]. With time, it turned out that PIA/PNAG and the corresponding _ica_ orthologous genes were not only found in _S. epidermidis_ but also in _S. aureus_[60], _Bacillus subtilis_[85], and in many Gram-negative bacteria such as _Acinetobacter baumannii_[86], _Burkholderia_ spp. [87], and _Escherichia coli_[88], to name a few. In all these bacteria, PIA/dPNAG contributes to biofilm formation.  
The corresponding _icaADBC_ orthologous genes were named _epsHIJK_ in _B. subtilis_ or _pgaABCD_ in _E. coli_. The staphylococcal _icaADBC_ operon encodes all the enzymes required for the biosynthesis of PIA/dPNAG, as illustrated in Figure 2. The substrate for PIA/dPNAG biosynthesis is UDP-N-acetylglucosamineamine, which is oligomerized by IcaA [89]. IcaA represents the catalytic enzyme that exhibits only a low \(N\)-acetylglucosamine)transferase activity, which is possibly enhanced significantly when _icaA_ and _icaD_ are co-expressed. However, IcaAD reached only a maximal length of 20 residues. When _icaAD_ is co-expressed with _icaC_, longer chains are synthesized that react with PIA-specific antiserum. At that time, IcaAD represented a novel protein combination among \(\updelta\)-glycosyltransferases [89].  
IcaB is a surface-attached protein that is responsible for the deacetylation of the poly-\(N\)-acetylglucosamine molecule [90]. Most likely due to the loss of its cationic character, non-deacetylated poly-acetylglucosamine in an isogenic _icaB_ mutant strain that is devoid of the capacity to attach to the bacterial cell surface. It is essential for PIA virulence, such as biofilm formation, colonization, and resistance to neutrophil phagocytosis and human antibacterial peptides [90; 91].  
IcaC is a transmembrane protein containing 18 helices. It is therefore membrane-bound. It demonstrates O-succinyltransferase activity, which is involved in PNAG-O-Succinate addition [92]. This O-succinylation motive constitutes 6% of the succinate molecules of the structure and provides anionic charges to dPNAG as previously described for polysaccharide II, a molecule which demonstrates a lower content in non-N-acetylated D glucosaminyl residues and contains phosphate and ester-linked succinate, which confer anionic properties [83].
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Initial Access  
#### 2.3.1 Basic Inital Access Procedure  
The initial access [59] of 5G is a sequential procedure between the UE and gNB to acquire UL synchronization and obtain identification information for the radio access communication; the process is also known as the Random Access Channel (RACH) process. This process is initiated in several cases. The initial access is called when the UE moves from the RRC idle state, which is related to the low-power mode, to the RRC connected state, or during the RRC connected state when the UL synchronization status is marked as non-synchronized, or during the RRC re-establishment procedure, or during the transition from the RRC inactive state. The procedure is also called when the UE demands system information that is not included in broadcast packets, or when the UE establishes a time alignment at the secondary cell to improve data rates or make a more reliable connection. The initial access procedure is also called in the case of beam failure or handover between cells. The initial access procedure is depicted in Figure 5. The primary distinction between LTE and 5 GNR RACH occurs right before the RACH preamble transmission. This difference arises from the default support for beamforming in NR, particularly in mmWave scenarios.  
Figure 5: Initial access procedure and its messages. Parameters \(\Delta\) and K2 specified in [60].
## 1 Introduction  
Collateral vessels can be found around renal cell carcinoma (RCC). These vessels are enlarged renal capsular veins, which become macroscopically visible in computed tomography (CT) or magnetic resonance imaging (MRI) studies (Figure 1).  
Collateral vessels adjacent to RCC are poorly investigated, and the significance of this finding is not fully understood.  
G gonadal vein recruitment is more frequently associated with RCC located at the lower renal pole [1]. On the other hand, tumors of the upper renal pole usually have a different drainage route, flowing into the adrenal and lower phrenic veins [1].  
A CT study performed on 58 RCC patients showed that 26 patients had peritumoral collateral vessels, of which 18 had outflow through the gonadal vein [1]. This finding was present in tumors greater than 5 cm in diameter (18/18), more frequently in males (15/18), in tumors of the right kidney (12/18) and at the lower pole (15/18) [1].  
Moreover, collateral vessels were found to be associated with greater blood loss during surgery for RCC (mean volume of 1078 mL vs. 304 mL) [1] and have been linked to cancer staging in RCC patients, possibly reflecting a sign of locally advanced disease (i.e., T stage > T3a) [2].  
The most frequent grading system in clear cell RCC (ccRCC) and papillary RCC is the four-tiered Fuhrman system (I, II, III and IV) [3]. A simplified two-tiered Fuhrman grade system has been proposed by dividing the ranks into low grade (I and II) and high grade (III and IV) [4]. It has been shown to have the same specificity in predicting mortality as the traditional four-tiered Fuhrman grade system but with greater reproducibility and less variability for pathologists [4]. High-grade tumors have a greater risk of post-operative recurrence [5].  
The possible association between peritumoral collateral vessels adjacent to RCC and the Furman grade has never been explored to date.  
Herein, we test the hypothesis that the presence of peritumoral collateral vessels in perinephric adipose tissue adjacent to ccRCC is indicative of a high Furman grade (i.e., III and IV).
###### Abstract  
Background: Air pollution is a persistent and critical challenge for Almaty, Kazakhstan's largest city. The city's unique topographical and meteorological conditions--being located in a mountain basin with dense urban development--restrict natural ventilation and contribute to frequent exceedances of air quality standards. These factors make accurate assessment and management of atmospheric pollution particularly urgent for the region. Aim: This study aims to develop and apply a novel, high-resolution three-dimensional numerical model to analyze the spatial distribution of key atmospheric indicators--air velocity, temperature, and pollutant concentrations in Almaty. The goal is to provide a comprehensive understanding of how meteorological and urban factors influence air quality, with a focus on both horizontal and vertical stratification. Methods: A three-dimensional computational model was constructed, integrating real meteorological data and detailed urban topography. The model solves the compressible Navier-Stokes, energy, and pollutant transport equations using the finite volume method over a 1000 \(\times\) 1000 \(\times\) 500 m domain. Meteorological fields are synthesized along all spatial axes to account for vortex structures, urban heat islands, and stratification effects. This approach enables the simulation of atmospheric parameters with unprecedented spatial resolution for Almaty. Results: The simulation reveals significant spatial heterogeneity in atmospheric parameters. Wind velocity ranges from 0.31 to 5.76 m/s (mean: 2.14 m/s), temperature varies between 12.03 \({}^{\circ}\)C and 19.47 \({}^{\circ}\)C (mean: 16.12 \({}^{\circ}\)C), and pollutant concentrations fluctuate from 5.02 to 102.35 \(\mu\)g/m\({}^{3}\) (mean: 44.87 \(\mu\)g/m\({}^{3}\)). Notably, pollutant levels in the city center exceed those at the periphery by more than two-fold (68.23 \(\mu\)g/m\({}^{3}\), 29.14 \(\mu\)g/m\({}^{3}\)), and vertical stratification leads to a marked decrease in concentrations with altitude. These findings provide, for the first time, a comprehensive and quantitative picture of air quality dynamics in Almaty. Conclusion: The developed model advances the scientific understanding of urban air pollution in complex terrains and offers practical tools for city planners and policymakers. By identifying pollution hotspots and elucidating the influence of meteorological factors, the model supports the optimization of urban infrastructure, zoning, and environmental monitoring systems. This research lays the groundwork for evidence-based strategies to mitigate air pollution and improve public health in Almaty and similar urban environments.  
numerical modeling; atmospheric pollution; wind speed field; temperature field; urbanization; meteorological model
## 2 Materials and Methods
### Characterization  
Nitrogen adsorption\(-\)desorption isotherms at 77 K were measured on a Micrometrics Gemini V2.0 (Micromerities Instrument Corp.) system. All samples were pre-treated overnight at 473 K under nitrogen before measurements. The pore size was calculated from desorption branches of isotherms by the Barrett-Joyner-Halenda (BJH) method. Surface areas were calculated by the Brunauer-Emmett-Teller (BET) method.  
FTIR-ATR spectra were made in a Perkin Elmer UATR Spectrum Two spectrometer with 72 scans/min with a resolution of 4.0 cm\(-\)1 over the 4000 to 400 cm\(-\)1 range for the initial materials and after nickel adsorption in a 25 mg L\({}^{-1}\) solution. The samples were dried in an oven at 100 \({}^{\circ}\)C before FTIR-ATR testing. The spectrum was obtained after performing the background, placing the sample powder over the crystal and pressing it against the crystal. The average of three spectra was used.  
Powder X-ray diffraction (PXRD) data were collected at ambient temperature on an Empyrean PANalytical diffractometer, with a working wavelength of \(\lambda\)1 = 1.540598 A and \(\lambda\)2 = 1.544426 A (Cu K\(\alpha\)1,2 X-radiation), equipped with a PIXcel 1D detector and a flat-plate sample holder in a Bragg-Brentano para-focusing optics configuration (45 kV, 40 mA). Intensity data were collected by the step-counting method (step 0.01\({}^{\circ}\)), in continuous mode, in the 3.5\({}^{\circ}\)\(\leq\) 20\({}^{\circ}\)\(\leq\) 50\({}^{\circ}\) range.  
Scanning electron microscopy (SEM) was carried out on a high-resolution Hitachi SU-70 (Monocomp) working at 15 kV. Samples were prepared by deposition on aluminum sample holders followed by carbon coating using an Emitech K950X carbon evaporator.