esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### General Aspects  
This section will conduct a sensitivity analysis and parametric study to investigate the influence of various parameters on the seismic behavior of rectangular semi-buried tanks. Understanding the sensitivity of the tank's response to these parameters is crucial in optimizing the design and enhancing the safety of such structures. The following parameters will be considered in the study:  
1. Water Level: We will examine the effect of varying water levels on the tank's seismic response. Different water levels, from empty to complete, will be analyzed to understand how the tank behaves under various loading conditions;  
Figure 5: Combine 14 figure (the element is defined by two nodes, I and J, a spring constant (k) and damping coefficient (Cv)) [23].  
Soil Properties: The seismic behavior of the tank is greatly influenced by the surrounding soil properties. We will investigate the impact of different soil types on the tank's response, including stiffness, shear modulus, bulk modulus, and Poisson's ratio;
* Tank Wall Thickness: The thickness of the tank wall plays a significant role in its structural integrity. We will explore how different wall thicknesses affect the tank's seismic performance;
* Expansion Joint Type: Comparing the effects of different expansion joint types on the tank's response will provide valuable insights into the importance of expansion joints in mitigating seismic stresses;
* Roof Presence: We will study the influence of having a roof on the tank's behavior during seismic events. A roof's presence or absence can affect the tank's overall stiffness and response;
* Damping Coefficients: The damping coefficients \(\alpha\) and \(\beta\) play a crucial role in dissipating energy during seismic events. We will analyze the sensitivity of the tank's response to different damping values;
* Earthquake Ground Motion: Variations in earthquake ground motion characteristics will also be considered. Different earthquake records with varying frequency content and intensity will be used to evaluate the tank's response under different seismic scenarios.  
The sensitivity analysis and parametric study will involve running multiple dynamic simulations with different parameter combinations. The results will be compared and analyzed to identify the most influential parameters and their effect on the tank's behavior. This information will provide valuable insights into the design and seismic performance optimization of rectangular semi-buried tanks, ensuring their safety and reliability in earthquake-prone regions.  
By conducting this comprehensive sensitivity analysis and parametric study, we aim to enhance further the understanding of the seismic response of rectangular semi-buried tanks, considering fluid-structure and soil-structure interaction. The findings of this study will contribute to better design guidelines and risk assessment methodologies for liquid storage tanks, improving the overall resilience of critical infrastructure in earthquake-prone areas.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 5 Ongoing R&D
### Putting an IoT Gateway in Space
#### 5.4.2 Laboratory Test Results  
This section shows laboratory test results for the IURA use case (UC#3) which is representative of a regenerative on-board processing (OBP) scenario, typical for LEO regenerative satellites. The terminal is battery- and low-powered, and the on-board processing resources are very limited compared to the on-ground demodulation. Hence, a small channelization, small spreading factor values and few SIC iterations shall be considered for this case. Figures 22 and 23 depict the throughput and PLR performance, respectively, considering terminals transmitting an IURA waveform with a chip rate of \(R_{c}=220\) kchip/s, an SF = 16, a minimum C/N = \(-15\) dB and 6 dB of available power randomization. Such range, for instance, can be due to the path loss difference between two terminals, one that observes the satellite with the minimum elevation angle (e.g., at about 30 degrees) and another one with the maximum elevation angle (i.e., 90 degrees). The throughout and PLR performance are shown for different numbers of SIC iterations, specifically, from one SIC to four SIC iterations. The result is that, depending on the OBP capabilities, the performance can vary from about 0.1 (one SIC iteration) up to about 0.55 bit/chip (four SIC iterations), at PLR = \(10^{-2}\). A summary of the results is shown in Table 4. For the sake of clearness, the generic SIC iteration consists of the sequence of the following processing blocks: acquisition, demodulation, regeneration and cancellation. Hence, the demodulation performance with zero SIC iterations does not benefit from cancellation, which is carried out only at the end. Additionally, it is worth to stress the reason why the throughput performance of UC#3 is lower than UC#1: UC#3 results were obtained with a much less powerful hardware, as the UC#3 demodulator is on board the satellite, and hence shall have low power consumption with a reduced number of SICs. Conversely, on-ground receivers in UC#1 may employ a much more powerful demodulator hardware and a higher number of SICs, thus providing a superior throughput performance.
# Prime Time Tactics--Sieve Tweaks and Boosters
## 2. Sieving Buffer-Related Techniques  
### 2.1. Sieve Buffer Structure  
As explained in [22], the sieving is made against a set of prime candidates that are represented in what we call the **sieving buffer**. One of the most important elements influencing the performance of a sieve is the structure of this sieving buffer. There are two goals here:  
* to reduce the space occupied by the buffer; historically, the aim here was to be able to process as big a number of primes as possible using the significantly lower resources of the computers decades ago, and nowadays, maintaining the requirement for a small memory footprint, the main driver is to obtain a better cache intensity. * to minimize the set of candidates, in order to have the smallest number of computations possible. For the first objective, the established solution is to represent the candidates not as a set of values per se, but as a vector of booleans; the value of the candidate is indicated by its position in the vector, while the status (prime or composite) of that candidate is codified through its value. At first the size of the vector element was one interger, as in [5;6], or byte, but very soon the bit representation described in [8] gained more and more traction. The bit compression technique involves bit manipulation using code like the following or similar. Modern compilers will optimize division and modulo with powers of two operands into very fast bitwise operations (division will be replaced with a bitwise right shift, and modulo with a bitwise AND operation) so it is better to leave them as such for clarity; nevertheless, when the compiler is less advanced (like when writing the kernel for GPGPU), it is indicated to write directly in the code the bit operations. ```
1 conateval autoBullMaskArray(void){
2 std::array-unsignedchar,8>arr{};
3 for(unsignedi=0;i<8;1++)
4 arr[i]=(1<<(i));
5 returnarr;
6}
7 inlineinline:



Nevertheless, both strategies--bit and byte representations--are currently in use. The bit representation offers clear advantages in terms of memory footprint and especially cache efficiency. However, on lesser CPUs, the additional overhead can sometimes negate the performance gains. Furthermore, when processing the same buffer in parallel, such as with GPGPU, synchronization issues may render the bit representation impractical. The second objective, minimizing the set of candidates, was also of interest from the start; even the very early algorithms skipped over even values [5] or used the \(6k\pm 1\) pattern [6]. The \(30k+i\) pattern (where \(i\in\{1,7,11,13,17,19,23,29\}\)) was also established quite a long time ago; coupled with the bit compression of the buffer, this method is able to represent an interval of 30 in only 1 byte and is the preferred technique for practical sieves today. We have to mention that the first record of this idea dates to as early as 1969 [8]. The problem with such patterns is that they involve complex logic to address the exact position in the vector, often using if..else or switch.case constructions, which are quite detrimental to performance.
# Weather Radar in Complex Orography
## 1 Introduction
### Outline  
This paper presents a review of the challenges and possible solutions for radar applications in mountains. The focus is on operational radars and operational applications. The material presented is related, to a great extent, to the decades of experience of the authors in the usage of weather radars in the Swiss Alps. The paper starts from the practical needs and the requirements of the users (Section 2). An overview of challenges and problems pertaining to the operation and usage of radars in a mountainous environment is given in Section 3. Section 4 presents a selection of proven solutions, performance indicators and the current research frontiers. Concluding remarks can be found in Section 5.
# Asynchronous Quantum-Resistant Blockchain for Secure Intelligence Sharing
## 1 Introduction
In March 2025, NIST selected the HQC (Hamming Quasi-Cyclic) algorithm as an additional post-quantum encryption standard, serving as a backup to ML-KEM due to its reliance on different mathematical foundations. As quantum computing technology continues to progress, migrating blockchain systems to these standardized post-quantum cryptographic mechanisms is essential to ensure long-term security. Aside from quantum threats, there exist other concerns related to blockchain technology, such as efficiency and the substantial consumption of computational resources. Blockchain generates a new block approximately every ten minutes, resulting in delayed data storage efficiency. Moreover, the proof-of-work (PoW) consensus mechanism used to mine new blocks requires nodes to compete for mining rights. The node that successfully solves the cryptographic puzzle first receives the reward, with nodes possessing higher computational power having a greater likelihood of winning. However, other nodes still expend computational resources despite having a nonzero probability of finding a valid solution. This inefficiency and wastage of computational resources present significant challenges in blockchain research. To address these issues, we aimed to design Asynchronous Quantum-Resistant Blockchain for Secure Intelligence Sharing (AQRB), adopting the concepts of the SPHINCS+,CRYSTALS-KYBER, and the dual blockchain to tackle quantum computing, blockchain efficiency, and computational power problems. Based on the PQCRYPTO project endorsed by the European Union [11] and NIST, AQRB picks the SPHINCS\({}^{+}\) algorithm as the post-quantum digital signature algorithm and CRYSTALS-KYBER for constructing AQRB. In conjunction with the implementation of a dual blockchain to tackle efficiency challenges, the new research utilizes the Libra [12] blockchain framework to achieve the following objectives:  
* Quantum-resistant computation: All data stored on the blockchain remains secure and immune to quantum computing attacks or tampering. * Data storage efficiency: Reduces synchronization delays by enabling an asynchronous transaction validation mechanism. * Blockchain network balance: Encourages fair participation of nodes in the consensus process to prevent computational power centralization. The rest of this research is organized as follows: Section 2 presents the literature review covering prior work. Section 3 provides a review and discussion of the relevant literature. Section 4 outlines the methodology of this research, detailing the system design, cryptographic integration, and threat model assumptions. Section 5 presents the results of the experiments, including performance benchmarks and implementation metrics. Section 6 provides security analyses, addressing confidentiality, integrity, and quantum resilience. Finally, Section 7 concludes the contributions of AQRB and outlines future research directions.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 4 Discussion
### Analysis of Skeleton Extraction and Positioning of Pruning Point Results  
After evaluation, we find that the average processing time \(\bar{X}\) of the Zhang-Suen refinement algorithm is reduced by 57.14% compared to the Hilditch refinement algorithm. Meanwhile, the average refinement rate \(\bar{R}\) of the Zhang-Suen refinement algorithm is also slightly higher than that of the Stentiford refinement algorithm, specifically 1.93% higher. In this paper, we improve the decision condition of the Zhang-Suen thinning algorithm, which makes the skeleton more focused on the main stem and branches and excludes the influence of other things such as secondary branches or buds growing on branches on the skeleton of the branches, which makes the skeleton of a grapevine obtained after the segmentation more in line with the actual shape and the skeleton line is also in the middle of the branch.  
We achieved a detection rate of 82.35% for the location of the pruning point, which is the probability obtained in the case shown in Figure 15. For the specific case of buds growing on the back of a branch, we can improve the detection rate of the pruning point by the detection of the back side of the branch as well. Despite the complexity of the real-world environment, this paper provides an efficient pruning-point-localization method with a high detection rate.  
In terms of model running efficiency, we compared the method proposed in this paper with the method used by [PERSON] et al. [36]. According to the results of the comparison, in [PERSON] et al.'s study, the total time required for the system to process 80 scan points to generate a 3D model during the 3D reconstruction phase of the process was between 10 min and 15 min, and subsequently, their vision system processed individual scan points in 15-35 s. In contrast, the model in this paper processes the image and determines the positions of 102 pruning points in only 157.19 s, with each taking 1.54 s. In terms of efficiency, there is a significant difference between them and the method in this paper significantly improves the processing speed.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## Materials and Methods  
### Multi-GNSS Positioning Models  
The simplest positioning method, SPP is where pseudo-range observations made by a receiver are processed to acquire its position with the orbits of GNSS satellites and their clock offsets, as computed from the broadcast navigation message. Point positioning based on the pseudo-range and phase observations as well as precise orbit and clock products is known as PPP. The quad-constellation GNSS raw pseudo-range and carrier phase observation on the \(j\) frequency can be expressed as [14]:  
\[p_{r,j}^{s}=\rho_{r}^{s}+dt_{r}-dt^{s}+T_{r}^{s}+I_{r,j}^{s}+d_{r,j}-d_{j}^{s} +\varepsilon_{p} \tag{1}\]  
\[\varphi_{r,j}^{s}=\rho_{r}^{s}+dt_{r}-dt^{s}+T_{r}^{s}-I_{r,j}^{s}+\lambda_{j} w_{r}^{s}+\lambda_{j}N_{r,j}^{s}+\lambda_{j}(b_{r,j}-b_{j}^{s})+\varepsilon_{\varphi} \tag{2}\]where the superscript \(s\) denotes a GNSS satellite; the subscript \(r\) denotes the receiver; \(p_{r,j}^{s}\) denotes the observed pseudo-range; \(\varphi_{r,j}^{s}\) is the corresponding carrier phase; \(\rho_{r}^{s}\) denotes the computed geometrical range; \(dt_{r}\) is the receiver clock offset; \(dt^{s}\) is the satellite clock offset; \(T_{r}^{s}\) is the slant tropospheric delay; \(\lambda_{j}\) is the wavelength of carrier phase; and \(I_{r,j}^{s}\) is the slant ionospheric delay on \(j\)th frequency. It is possible to convert the first order ionospheric delays at different frequencies by \(I_{r,j}^{s}=(\lambda_{j}^{2}/\lambda_{1}^{2})I_{r,1}^{s}\); \(d_{r,j}\) and \(d_{j}^{s}\) are the uncalibrated code delays (UCDs) of the receiver and the satellite on \(j\)th frequency; \(w_{r}^{s}\) is the phase wind-up delay; \(N_{r,j}^{s}\) is the integer ambiguity; \(b_{r,j}\) and \(b_{j}^{s}\) are the uncalibrated phase delays (UPDs) for the receiver and satellites, respectively. \(\varepsilon_{p}\) and \(\varepsilon_{\varphi}\) are the pseudo-range and carrier phase observation noises including multipath, respectively.  
For the following statement, we define the notations as:  
\[\left\{\begin{array}{l}\alpha_{1,2}^{s}={(f_{1}^{s})}^{2}/{({(f_{1}^{s})}^{2 }-{(f_{2}^{s})}^{2})},\beta_{1,2}^{s}=-{(f_{2}^{s})}^{2}/{({(f_{1}^{s})}^{2}-{( f_{2}^{s})}^{2})},\\ \text{DCB}_{1,2}^{s}=d_{1}^{s}-d_{2}^{s}\text{, DCB}_{r,1,2}=d_{r,1}-d_{r,2},\\ d_{IF}^{s}=\alpha_{1,2}^{s}\cdot d_{1}^{s}+\beta_{1,2}^{s}\cdot d_{2}^{s},d_{r, 1F}=\alpha_{1,2}^{s}\cdot d_{r,1}+\beta_{1,2}^{s}\cdot d_{r,2},\\ b_{IF}^{s}=\alpha_{1,2}^{s}\cdot\lambda_{1}\cdot b_{1}^{s}+\beta_{1,2}^{s} \cdot\lambda_{2}\cdot b_{2}^{s},b_{r,IF}^{s}=\alpha_{1,2}^{s}\cdot\lambda_{1} \cdot b_{r,1}+\beta_{1,2}^{s}\cdot\lambda_{2}\cdot b_{r,2},\end{array}\right. \tag{3}\]  
where \(f_{1}^{s}\) and \(f_{2}^{s}\) are the signal frequency; \(\alpha_{1,2}^{s}\) and \(\beta_{1,2}^{s}\) are the frequency-dependent factors; \(\text{DCB}_{1,2}^{s}\) and \(\text{DCB}_{r,1,2}\) are the satellite and receiver differential code bias (DCB).
## 3 Results
### Association between the Protein Expression and Patient Survival in Own Cohort  
Next, we constructed Kaplan-Meier curves to determine the impact of individual expression patterns of the studied proteins on the overall survival (OS) of PDAC patients. A suggestive association was observed between SATB1\({}^{\text{n}}\) expression and OS, but without statistical significance (297 days vs. 561 days, \(p\) = 0.118; Figure 3A). Notably, the presence of SATB1\({}^{\text{c}}\) expression in PDAC was associated with a significantly shorter OS rate (median OS: 117 days) than the absence of SATB1\({}^{\text{c}}\) expression (median OS: 458 days; \(p\) = 0.036; Figure 3B). Combining these two staining patterns of SATB1 did not improve the survival prediction (Figure S1, Tables S1 and S2). Furthermore, Kaplan-Meier survival analysis indicated that PDAC patients with a high level of SMAD3 expression (median OS: 290 days) had lower OS rates (\(p\) = 0.004) than those with SMAD3 low expression level (median OS: 531 days, Figure 3C). Analysis of patient survival data also showed that low Ezrin expression correlated with a higher OS rate (median OS: 450 days) than high expression (median OS:118 days). This survival difference was of borderline significance (\(p\) = 0.055, Figure 3D). Low \(\beta\)-catenin expression tended to be associated with longer survival (median OS: 450 days) than high (median OS: 274 days; \(p\) = 0.088, Figure 3E).  
Figure 3: Kaplan–Meier survival curves stratified by SATB1\({}^{\text{n}}\) (**A**), SATB1\({}^{\text{c}}\) (**B**), SMAD3 (**C**), Ezrin (**D**) and \(\beta\)-catenin (**E**) expression in pancreatic adenocarcinoma. Protein expression was measured using immunohistochemistry, and the calculated IRS scores were divided into two categories as specified in the Material and methods.
# Long-Range Multispectral Thermal Imager for Simultaneous Measurement of Distance and Temperature
## 2 Proposed Method
Input a set of matching points to \(\left\{(m_{i},m_{i}^{\prime})\right\}_{i=1}^{N}\), where \(m_{i}=(x_{i},y_{i},1)^{\mathrm{T}}\) and \(m_{i}^{\prime}=(x_{i}^{\prime},y_{i}^{\prime},1)^{\mathrm{T}}\) represent the normalized homogeneous coordinate points between the two images, respectively. 2. In the given maximum number of iterations, 4 pairs \(\left\{(m_{i},m_{i}^{\prime})\right\}_{i=1}^{4}\) are randomly selected from the matching point pairs to calculate the homography matrix \(H\). 3. For all matching points pairs \((m_{i},m_{i}^{\prime})\), calculate the consistency error \(e_{i}\): \[e_{i}=\sum\parallel m_{i}^{\prime}-H\cdot m_{i}\parallel^{2}\] (14) If \(e_{i}<\delta\), the pair of feature points is the correct matching feature point (inner point). 4. Calculate the current number of inside points. If the number of inside points is greater than the maximum number of inside points, update the model and the corresponding set of inside points. 5. Repeat steps (2)-(4) to calculate the homography matrix \(H\). 6. If the maximum number of iterations is reached or the proportion of the number of internal points to the total points exceeds the threshold, the iteration is stopped and the optimal homography matrix \(H\) and the collection of internal points are output. The value of offset \(H\) can be obtained by \(H_{32}\) in the homography matrix \(\Delta x\), and then the distance of the measured target can be obtained by Formula (4).
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 2 Problem Formulation  
By capturing the track information of traffic vehicles and their environment information through vision sensors, we can accurately describe the historical motion path of the vehicle to be predicted and the interaction with other vehicles and the surrounding environment in a two-dimensional coordinate system. The vehicle trajectory information is encapsulated by the encoder as \(\mathbf{S}^{Nt+Nh}\), where \(N_{t}\) represents the number of future time steps and \(N_{h}\) represents the number of features per time step (e.g., the longitudinal and lateral position of the vehicle, the direction of travel, etc.). Specifically, for the \(\mathbf{i}\)-th vehicle out of all \(\mathbf{n}\) traffic vehicles currently captured, we can obtain the status input \(\mathbf{S}_{i,t}^{Nt*Nh}=[\mathbf{x}_{t},\mathbf{y}_{t},\cdots,\mathbf{v}_{t}, \mathbf{o}_{t}]\), \(i\in[0,n-1]\), and model vehicle state information within a certain time range, where time \(0-t\) represents the time period of known historical trajectory, \(t-T\) is the time period of future trajectory to be predicted, \((\mathbf{x},\mathbf{y})\) is the two-dimensional coordinate value centered on the base vehicle at a certain time. The purpose of this study is to predict the future trajectory information of the vehicle \((\mathbf{x},\mathbf{y})_{t:T}\). There is also the vehicle speed \(v\), and the interaction with the surrounding environment \(o\)(such as traffic lights, lane lines), etc. Given information about the current environment, the past state of the vehicle, and the state of other vehicles, we can define the probability distribution function of the trajectory spread as \(p_{\theta}((\mathbf{x},\mathbf{y})_{t:T}\mid(\mathbf{x},\mathbf{y})_{t:T},S_{ T})\) for the future state of the vehicle. During the diffusion process, random noise is gradually introduced into the real trajectory through the \(\mathbf{k}\)-step Markov chain, so the posterior distribution of the diffusion process in step \(k\) can be written as \(p_{\theta_{k}}\).