esa-sceva/satcom-chunk-collection huggingface dataset:
# Global Dynamics and Implications of an HBV Model with Proliferating Infected Hepatocytes
## 4 Discussion
The existence of a significant uninfected population implies the severely impaired proliferative potential of infected cells. It has been observed that the fraction of hepatocytes infected in a chronic HBV case progresses from nearly to 100% to only a few percent [50]. This may be explained by a combination of gradual immune attrition of infected hepatocytes, possibly impaired viral replication, and weak proliferation. [PERSON] et al. [50] recently presented evidence that hepatocytes refractory to infection are selected over the course of chronic infection, with healthy clonal populations prevalent. Thus, it is likely that a strong proliferative advantage for healthy over infected cells can keep infected hepatocyte populations low through both Darwinian mechanisms and via the intrinsic population dynamics of the disease system. Our minimal representation of the immune response is also a clear limitation, but by minimizing model dimensionality, we can more clearly elucidate the effect of comparative proliferation rates on the model dynamics. An essential component of the immune response,at least in acute HBV infection, may be noncytotoxic curing of infected cells with subsequent \"immunity\" to re-infection, considered in modeling works by [PERSON] et al. [16; 37]. This dynamic cannot be considered in our current framework and may have further implications for the role of proliferation among different hepatocyte populations. Future work will also focus on using the model to explore the impact of treatments. Current antiviral therapies include standard interferon and PEGylated interferon therapies for short-term use and nucleoside/nucleotide analogues for long-term use [51; 52; 53], but there are still open questions about the optimum timing and ordering of the treatments [54; 55]. Models that have made slightly different assumptions have fit their models to existing datasets showing complex virus dynamics resulting from clinical trials [22; 27; 29; 37; 38; 41; 43; 56; 57; 58]. One should take care, however, not to push this model, and therefore the hypothesis just presented, too far. The BVIM has typically been applied to infections of the blood (e.g., malaria and HIV). However, unlike blood, the tissues of the liver are not well mixed, which raises legitimate questions about the validity of an ODE description. Therefore, an important future project is to study a spatially explicit version of this model. The most obvious approach would be a partial differential equation model in which the basic dynamical properties modeled here are coupled with terms describing virion diffusion through the interstitium and perhaps a form of advection representing the passage of virions through the hepatic vasculature. At any rate, the significance of spatial effects is an open question that needs to be addressed before long. The authors declare no conflict of interest.
# A Novel ASIC Implementation of Two-Dimensional Image Compression Using Improved B.G. Lee Algorithm
## 3 ASIC Implementation of 2D DCT/IDCT Based on B.G Lee DCT Algorithm  
The [PERSON] algorithm [24] architecture with a novel hardware implementation is used to compute the 2D DCT for each 8 \(\times\) 8 image segment. As 2D DCT is a separable transform, each 8 \(\times\) 8 image block can be implemented using the row-column decomposition method. The 2D IDCT computation reconstructs the original image using the same method.  
The digital image compression using the [PERSON] algorithm was implemented on digital images of 256 \(\times\) 256 pixels with a resolution of 8 bits. The structure of the 2D DCT/IDCT using the [PERSON] algorithm with the proposed error-tolerant adders, the  
Figure 4: Proposed Signed Fixed-Point Multiplier.  
proposed fixed-point multiplier, and shifters is illustrated in Figure 5. The 256 \(\times\) 256 digital image is divided into 1024 image segment blocks of size 8 \(\times\) 8. Each 8 \(\times\) 8 image segment block is then converted to [64:1] column matrices. Eight-point 1D DCT is performed on each [64:1] column matrix. This column matrix is once again converted to an 8 \(\times\) 8 matrix, and then transposed and converted back to a [64:1] column matrix. Once again, eight-point 1D DCT is performed on this transposed column matrix. Now the compressed image is obtained.  
The reverse process is applied to recover the digital image from the compressed form. Eight-point 1D IDCT is performed on each [64:1] column matrix. The column matrix is rearranged to form 8 \(\times\) 8 image segments, the transpose of each image segment is taken, and then each 8 \(\times\) 8 image segment is converted to a [64:1] column matrix. Finally, eight-point 1D IDCT is applied on this transposed matrix. The resultant column matrix is converted to the original 8 \(\times\) 8 image block. The original image is recovered by merging all of the 8 \(\times\) 8 image blocks.
## 1 Introduction  
### Definition and Motivation  
A graph is _unipolar_ if for some \(k\geq 0\) its vertices admit a partition into \(k+1\) cliques \(\{C_{i}\}_{i=0}^{k}\) so that there are no edges between \(C_{i}\) and \(C_{j}\) for \(1\leq i<j\leq k\). A graph \(G\) is a _generalised split graph_ if either \(G\) or its complement \(\overline{G}\) is unipolar. All generalised split graphs are perfect; and [PERSON] and [PERSON] [1] show that almost all perfect graphs are generalised split graphs. Perfect graphs can be recognised in polynomial time [2, 3], and there are many NP-hard problems which are solvable in polynomial time for perfect graphs, including the stable set problem, the clique problem, the colouringproblem, the clique covering problem and their weighted versions [4]. If the input graph is restricted to be a generalised split graph, then there are much more efficient algorithms for the problems above [5]. In this paper we address the problem of efficiently recognising generalised split graphs, and finding a witnessing partition.  
Previous recognition algorithms for unipolar graphs include [6] which achieves \(O(n^{3})\) running time, [7] with \(O(n^{2}m)\) time and [5] with \(O(nm+nm^{\prime})\) time, where \(n\) and \(m\) are respectively the number of vertices and edges of the input graph, and \(m^{\prime}\) is the number of edges added after a triangulation of the input graph. Note that almost all unipolar graphs and almost all generalised split graphs have \((1+o(1))n^{2}/4\) edges (see our paper \"Random perfect graphs\", which is under preparation). Further, by testing whether \(G\) or \(\overline{G}\) is unipolar, each of the mentioned algorithms above recognises generalised split graphs in \(O(n^{3})\) time. The algorithm in this paper has running time \(O(n^{2})\).  
This leads to polynomial-time algorithms for the problems mentioned above (stable set, clique, colouring, and so on) which have \(O(n^{2.5})\) expected running time for a random perfect graph \(R_{n}\) and an exponentially small probability of exceeding this time bound. Here we assume that \(R_{n}\) is sampled uniformly from the perfect graphs on vertex set \([n]=\{1,2,\ldots n\}\).
## 5 Intelligent Diagnostic Technology for Pile Inspection  
[PERSON] first proposed the concept of AI at the Dartmouth Conference in 1956, which marked the formalization of AI as an independent discipline. Over subsequent decades, AI technology has evolved in a spiral progression, progressing through key developmental stages including neuron models, rule-based systems, expert systems, machine learning, neural networks, and large models [93; 94; 95].  
Traditionally, PIT comprised two distinct phases: (1) signal acquisition through field testing, followed by (2) integrity assessment via qualitative or quantitative interpretation of the collected signals. Although field testing operations are relatively straightforward, the complexity of signal interpretation presents significant challenges for integrity assessment. Low-strain integrity testing is influenced by numerous factors affecting wave propagation and signal reflection, including pile material properties, soil-pile interaction characteristics, defect typology and location, impact energy magnitude, transducer installation quality, ambient noise conditions, and environmental factors (temperature and humidity). Furthermore, successful signal interpretation demands substantial engineering expertise and experience. Moreover, the inherent subjectivity in expert signal interpretation introduces measurable uncertainty in test results [96].  
The rapid advancement of AI technology has led to its growing significance across multiple disciplines, particularly in enhancing operational efficiency, measurement accuracy, and intelligent decision-making [97; 98; 99]. Over recent decades, extensive PIT has accumulated substantial datasets. These databases continue to grow with the increasing implementation of pile testing programs worldwide. However, these comprehensive datasets remain largely underutilized despite their potential value. These datasets represent valu  
Figure 10: Measurement system for fiber optic sensing-based thermal integrity test [7].  
able resources for training and validating AI models. AI technologies demonstrate robust feature extraction and pattern recognition capabilities, enabling effective processing of high-dimensional data and complex structural relationships [100]. This provides a means of identifying and analyzing complex waveforms. Table 4 summarizes recent research findings on the application of AI to PIT. As shown in Table 4, AI technologies applied to PIT can be broadly divided into three paradigms: (1) feature-based machine learning, (2) end-to-end deep learning, and (3) hybrid methods. The following subsections review these methods through representative studies.
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 4 Performance Evaluation
### Experimental Setup  
To evaluate the performance of the proposed algorithm in topology inference and estimate the link loss rates and jitter, we constructed several binary (i.e., all internal nodes have exactly two children) and general (i.e., internal nodes have two or more children) physical routing trees, following the network model described in Section 2, over bare-metal hardware in the Virtual Wall [18] testbed of the Fed4 FIIREPlus [19] federation. Routing was realized using the _Quagga_ software suite and network impairment (i.e., loss and jitter) on links were configured on software using the _netem_ kernel module and the _tc_ utility. Inparticular, we employed the _ospfd_ and _pimd_ quagga routing daemons, implementing the OSPF, PIM-SSM, and IGMP protocols, while the links' loss rates and jitter values were chosen in the ranges 0-20% and 0-400 ms, respectively. The _iperf_ traffic tool was used for performing multicast active probing and the required end-to-end measurements were obtained at the outgoing interface of the source node and the incoming interfaces of the destination nodes using the _tcpdump_ command-line packet analyzer. For every routing tree, three different numbers of probes were examined, namely probes 2128, 5105, and 10,207.
# Optimizing Rural Highway Maintenance Scheme with Mathematical Programming
## 2 A Bi-Level Mathematical Programming Model
Lower level:  
\[\min\sum\limits_{a\in A}\int_{0}^{x_{a}}t_{a}(x)dx \tag{4}\]  
subject to  
\[\sum\limits_{p\in R_{w}}f_{p}^{w}=q_{w},\ \forall w\in W \tag{5}\]  
\[x_{a}=\sum\limits_{w\in W}\sum\limits_{p\in R_{w}}\delta_{w,p}^{w}f_{p}^{w},\ \forall a\in A \tag{6}\]  
\[f_{p}^{w}\geq 0,\ \forall p\in R_{w},\ \forall w\in W \tag{7}\]  
The lower level manages the traffic flow resulting from the maintenance schemes, ensuring the optimal distribution of traffic and minimizing delays. Equations (4)-(7) constitute the lower-level model, which is the well-known user equilibrium (UE) model. This model predicts the traffic flow pattern of the entire network under the given road maintenance scheme. At the user equilibrium, each user attempts to choose the optimal route to their destination, ultimately leading to a situation where the travel cost for each route is equal due to the Nash equilibrium principle [37]. The user equilibrium state is characterized by the fact that no individual user can reduce their travel expenses by switching to a different route. This state is achieved as users continuously adjust their routing choices in response to changes in the network conditions, ultimately converging to a stable state. However, it is important to note that the user equilibrium state is not necessarily unique and can be influenced by various factors, such as the network topology, the travel demand, and the distribution of user knowledge and awareness. In the context of rural road networks, the scale of the network and the large number of users can lead to complex traffic conditions at the user equilibrium state. This complexity is further compounded by the presence of paradoxes, such as the Braess paradox, which suggests that adding capacity to a network can sometimes result in longer travel times for users. Therefore, the goal of our proposed programming paradigm is to minimize any potential delays caused by maintenance activities while considering the broader implications for the network as a whole. The bi-level model presented above fits within the framework of Stackelberg games [38], where decisions made at the upper level (e.g., maintenance schedule) influence the behavior and outcomes at the lower level (e.g., traffic flow patterns). However, bi-level models are inherently complex and are known to be NP-hard, meaning that finding optimal solutions for large-scale problems within a reasonable time frame is generally not feasible. To address this challenge, we reformulate the bi-level model into a single-level mathematical programming model that can be effectively solved by commercial solvers. This reformulation involves the introduction of binary variables and linearization techniques, which transform the problem into a mixed-integer linear programming (MILP) problem.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 3 Results
### Quantitative Experiments  
In order to verify the effectiveness of the algorithm based on ensemble learning, the experiments compare the algorithm in this paper with several current algorithms with better performance, the comparison algorithms include U-Net [26], Mask-RCNN [27], SOLOv2 [28], UCTransNet [29], Attention U-Net [30] and the Swin Transformer. Evaluation metrics included mIoU and pixel accuracy. To ensure the validity of the experimental comparisons, the same experimental environment is used for all experimental methods.The experimental results are shown as Tables 2 and 3.  
Meanwhile, this paper makes statistics on the cage breeding density of Xincun Port, Potou Port and Li'an Port from 2017 to 2021, and makes a vertical comparison of the cage breeding density of different ports at the same time, and a horizontal comparison of the breeding density of the same port at different times. The experimental result is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c} \hline
**Methods** & 256 \(\times\) 256 & 512 \(\times\) 512 \\ \hline
**U-Net** & 0.6425 & 0.8061 \\ \hline
**Mask-RCNN** & 0.7024 & 0.7833 \\ \hline
**SOLOv2** & 0.7142 & 0.7891 \\ \hline
**UCTransNet** & 0.7423 & 0.6951 \\ \hline
**Attention U-Net** & 0.6897 & 0.8142 \\ \hline
**Swin Transformer** & 0.7627 & 0.7976 \\ \hline
**Ensemble** & 0.8234 \\ \hline \end{tabular}
\end{table}
Table 2: mIoU for different neural network segmentation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Methods** & 256 \(\times\) 256 & 512 \(\times\) 512 \\ \hline
**U-Net** & 0.9858 & 0.9934 \\ \hline
**Mask-RCNN** & 0.9851 & 0.9879 \\ \hline
**SOLOv2** & 0.9879 & 0.9902 \\ \hline
**UCTransNet** & 0.9842 & 0.9851 \\ \hline
**Attention U-Net** & 0.9886 & 0.9947 \\ \hline
**Swin Transformer** & 0.9896 & 0.9967 \\ \hline
**Ensemble** & 0.9971 \\ \hline \end{tabular}
\end{table}
Table 3: Pixel accuracy for different neural network segmentation.
## 4 Experimental Verification and Discussion
### Measurement Results
Thus, they confirm the possibility of reasonably accurately estimating the resonant cavity-based WPT system's behavior in terms of high PTE region location, the PTE versus the Tx-to-Rx distance curve shape, and the maximum achievable PTE. Additionally, the frequency sweep data unambiguously demonstrate that despite the WPT system's quite high PTE sensitivity to variations in the system's operating frequency, one can still ensure acceptable PTE over multiple frequency bands, not only around the desired operating frequency. Figure 15:0 The measured PTE against the frequency for the Rx antenna located at \(x_{\rm RX}=40\) mm and \(z_{\rm RX}=80\) mm. Figure 15:1 The calculated and measured PTE against the separation between the receiving and transmitting antennas at \(x_{\rm RX}=40\) mm and \(z_{\rm RX}=80\) mm.
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 4 Characterization Techniques for Fouling Analysis
### Contact Angle  
In general, contact angle measurement is considered to quantify the hydrophobic character of the membranes used in MD, OD and OMD. It can be used to determine the hydrophobicity reduction of the membrane caused by fouling. To carry out this measurement, the sample does not require any previous preparation. The tip of a syringe is placed near the sample surface and then depressed so that a constant liquid drop volume (i.e., distilled water) of about 2 \(\upmu\)L is deposited on it. Images of the drop are taken by a camera and a specific software permits to determine the liquid contact angles. A mean value together with its standard deviation are finally calculated from more than ten to fifteen readings [2]. In addition to the water contact angle, the same system can be used to measure the surface tension of the membrane and then figure out its change depending on the adsorbed foulants compared to new membrane. More details on this technique can be found in [2; 110].  
Various authors have used this membrane surface characterization technique before and after carrying out the separation process. According to [PERSON], et al. [111] a clear decrease of the water contact angle value from an average of 129\({}^{\circ}\) for the virgin PTFE membrane to 108\({}^{\circ}\) for the fouled one (i.e., scaling) was observed. [PERSON], et al. [112] also detected smaller water contact angles of -fouiled PTFE membranes (TF200 and TF450) than those of new ones. The membrane having higher values of crystallization fouling factor exhibited the lower value of the water contact angle. [PERSON], et al. [58] measured the water contact angles of new and fouled PP membranes used in MD. Figure 4 shows the water drops on the surface of these membranes. A clear water contact angle reduction from 142\({}^{\circ}\) for the clean PP membrane to 91\({}^{\circ}\) for the fouled membrane. It was claimed that the loss of the membrane hydrophobicity was attributed to partial wetting of the membrane due to fouling deposit (i.e., inorganic ions, proteins and/or peptides and microorganisms present in pig manure).
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 2 Fixed Wireless Access
### Planning Tools for FWA Designs
#### 2.2.1 Industrial Focused Tools  
Most of the industrial focused planning tools work under a licensed software model, as described in Table 1. The ASSET suit is one of the most complete 5G network planners in the market able to validate several of the 5G business models. The 5G New Radio (5G NR) modelling is provided by enhanced propagation models, complex antenna arrays, comprehensive multi-technology 3D coverage and capacity simulations, including human exposure analysis [26]. Similarly, the Atoll 5G NR module offers operators a flexible and evolving framework for designing and deploying 5G networks [27]. This is a wireless network design and optimisation software that includes multi-radio access network (RAN) modelling for 5G networks, including outdoor and mmWave propagation based on ray tracing techniques that include up to 60 GHz frequencies. It is based on Monte Carlo simulations that offer accurate planning with the capability of revising planning data based on measure campaigns. Still, it lacks FWA and hierarchical topology capabilities. Both ASSET and Atoll tools are the only ones that evaluate (measure) human exposure, but none of them optimise the network planning towards exposure. Cappemini Engineering delivers a 5G planning solution which consists of several 5G tasks to create a continuous optimisation framework for operators working in sub 6 GHz and mmWave bands aiming to support the operators' key performance indicators (KPIs) [28]. The Cell Designer tool [29] provides network modelling and coverage analysis for 5G and other wireless technologies, using multi-vendor specific equipment modelling. On the other hand, the Hanna tool provides a cloud-based robust network planner for WiFi, 5G private networks (PN) and Bluetooth low energy (BLE). The web-based technology provides fast network designs for sub-6 GHz networks with accurate 3D planning [30]. Hardware vendors, such as Huawei, provide a 5G network planner for its partner operators to solve 5G challenges such as precision propagation models for urban and rural scenarios, accurate coverage prediction, automatic site planning and planning for novel services [31]. iBwave design enterprise is a 3D network design tool for both Wi-Fi and small cell cellular private networks that can be merged with a detailed site aesthetic to perform network design.
# A New Online Service for the Validation of Multi-GNSS Orbits Using SLR
## 2 Materials and Methods
### System Overview and Description of Processing Flow
Additionally, the angular characteristic of each laser measurement is included, thus we can investigate the residuals in very broad spectrum of geometrical dependencies: (1) two angles as seen from SLR station point of view: azimuth and elevation; (2) two angles as seen from the satellite: azimuth and nadir (Figure 1a); and (3) two angles describing the relative location of the Sun, the satellite, and the Earth in the time of observation: satellite argument of latitude with respect to the argument of the Sun (\(\Delta\)u) and the Sun elevation angle above the orbital plane (\(\beta\)) (see Figure 1b). The computational schema is hard-wired  
Figure 2: Flow scheme for processing of GOVUS daily campaign. Abbreviations used: CDDIS, Crustal Dynamics Data Information System; PCF, Processing Control File; BPE, Bernese Processing Engine. for fully automated data reprocessing using BPE [39]. The web-application is prepared for conducting the automated validation process with a daily routine. For this purpose, a time-based job scheduler, based on cron, combined with dedicated UNIX shell and Python scripts, was prepared. The workflow is presented in Figure 2. The main limitation in delivering near-real time SLR validation products comes from the frequency of the CODE MGEX orbit products being available. CODE MGEX products are being made public with the latency of about two weeks.