esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 7 Conclusions  
This study investigates the impact of various attacks on MANETs through simulations. Two scenarios were explored involving stationary nodes under attack by mobile malicious nodes. The first scenario employed a random node distribution, while the second used a uniform distribution. Both scenarios evaluated network performance under dropping, delay, sinkhole, and combined sinkhole-dropping (black hole) attacks using the end-to-end delay, packet delivery ratio, and throughput metrics.  
Our findings revealed that the network topology plays a crucial role in how MANETs are effected by attacks. While uniformly distributed networks may offer better resistance to individual attacks, they are more susceptible to sophisticated combined attacks like black hole attacks. Furthermore, our observations suggest that the movement patterns of attacking nodes within the network likely contribute to the observed effects.  
Future work should explore how variations in attacker movement patterns, such as initial positions, speed through the network, and potentially even 3D movement, influence the severity of attacks on different network topologies. Additionally, investigating the impact of varying node densities on attack effectiveness would provide valuable insights into network vulnerability. By examining these factors, more comprehensive security strategies and network configurations can be developed, ultimately enhancing resilience against diverse attack scenarios.  
Conceptualization, I.B.; Methodology, I.B.; Software, I.W.; Validation, I.B.; Investigation, I.B.; resources, I.W. and I.B.; data curation, I.B.; Writing--original draft preparation, I.B.; Writing--review & editing, I.W., B.G. and C.T.; Supervision, I.W.; project administration, I.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.  
The authors declare no conflict of interest.
## 4 Discussion and Conclusions  
This paper examines the relationships between the spatial distributions of six types of retail stores and their weighted centrality indices in the public transport flow network from the perspective of temporal dynamics. Three weighted node centrality indices were measured, e.g., degree, betweenness, and closeness. This study contributes to existing research on static physical street networks by analyzing the traffic flows of networks and their dynamic time processes.  
The findings illustrate that generally, the distribution patterns of six types of retail stores are influenced by weighted street centrality significantly. Except for building material stores, all types of stores are highly correlated with weighted centrality indices. Among the three weighted centrality indicators, weighted degree is the best for four types of retail stores in terms of correlation coefficients and is followed by closeness and betweenness.  
Temporal analysis can reveal more details and allow an inference of consumer behaviors. The correlation coefficients at different periods on weekdays and weekends vary over the time of day. For shopping malls, supermarkets, and convenience stores, the highest correlation coefficients on weekdays occur during the after-work period of 19:00 to 21:00. These may change on weekends for shopping malls, as shopping malls provide more than shopping services. Lower store levels correlate to degree centrality, that is, traffic volume itself, such as convenience stores and electronics stores. Higher store levels are correlated with the spatial characteristics of the flow network, such as closeness or betweenness. For specialty stores and electronics stores, people may visit these types of stores after work on weekdays and visit them at various times of the day on weekends.  
This research provides a more comprehensive understanding of retail location analysis from a static physical street network to a dynamic flow network. Further research can be conducted to examine the following topics. As consumers may travel in a variety of traffic modes, the flow network that is based on various travel modes is needed to more comprehensively describe traffic flow information. In addition, there is a significant characteristic of disparity of centrality in different cities. Thus, it is necessary to identify the differences among different cities by conducting more case studies.  
C.L.: Conceptualization, Methodology, Writing--Original draft; T.D. ([PERSON]): Writing--Review and editing, Supervision, Project administration, Funding acquisition; P.Z.: Software, Formal analysis, Data curation; T.D. ([PERSON]): Resources, Validation, Visualization. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The authors declare no conflict of interest.
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 4 Biofilm Formation Patterns in Some Clinically Important Uropathogens
### Klebsiella pneumoniae  
_K. pneumoniae_ are the second-most-common uropathogens after _E. coli_. The bacterium is known for its high adhesiveness and tendency to form biofilms on catheters and medical devices, making it a leading cause of nosocomial infections, mostly UTIs, pneumonia and septicemia, especially in immunocompromised individuals [73; 74]. Biofilm formation in _K. pneumoniae_ is strongly linked to a significant increase in multidrug resistance patterns, with many studies reporting a 10-to-1000-fold increase in the rate of multidrug resistance in mature bacterial biofilm as compared to planktonic cells [73; 74]. The most characteristic virulence factors of _K. pneumoniae_ are the thick capsular polysaccharide, in addition to type 1 and type 3 pili. The polysaccharide capsule in _K. pneumoniae_ can reach a thickness 16 times that of the capsule of _E. coli_[75]. It consists of two fibrous layers: the inner one consists of thick, densely-packed fibers, and the outer layer, in which the fibers are less densely packed and become finer as they go outwards, forming a fine network on the capsule surface [74]. This thick fibrous capsule grants the bacterium multiple benefits: it forms a protective barrier surrounding the bacterial cell, provides the bacterium with extra protection against phagocytosis and serum complement deposition, reduces the penetration ability of antibiotics and bacteriophages, and boosts the adhesiveness of the bacterium onto mucus membranes and inanimate surfaces [74; 76; 77].  
_K. pneumoniae_ pili are involved in the initial adhesion to abiotic surfaces, which is followed by quick coverage of the surface caused by the entangled fibrous polysaccharide capsule of adjacent bacterial cells [78]. The combination of the high adhesiveness and high biofilm-forming ability of _K. pneumoniae_, in addition to its protective fibrous capsule, results in high resistance to disinfection, making this pathogen very challenging to eradicate in health care facilities--hence, the high incidence of nosocomial infection associated with this pathogen, especially in catheterized patients, which also results in high incidence of recurrent urinary tract infections and bacteremia in high-risk individuals [17; 73].
# Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics
## 4 Evaluations
### Workload Characteristics  
In this section, we conduct the performance evaluation with the GraphBig benchmark suite and five selected representative graph processing kernels (workloads) including breadth-first search (BFS), connected component (CCOMP), degree centrality (DCentr), triangle count (TC), and PageRank (Prank). The BFS algorithm is widely used to traverse a graph's vertices for various purposes in graph-based computing, whilst the CCOMP is also implemented based on the BFS traversal operation. DCentr is a graph centrality algorithm which is used to analyze social network graphs. Furthermore, the PageRank algorithm is used to enhance the accuracy of the web search engine by Google Search to rank the priority and importance of web pages.  
Table 2 presents the basic usage of the graph processing workloads and the structure with memory access patterns. As shown in Table 2, irregular memory access patterns are prevalent in graph processing applications.  
To gain a better understanding of memory localities, we graphically analyzed the memory access patterns of each graph processing workload based on our memory access traces. Figure 6 illustrates the graph processing based on 1 million cache misses from the last-level cache (LLC) to the main memory system. The visualization on the x-axis,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Workload** & **Computation Type, Feature, Use Case** \\ \hline BFS & Graph traversing algorithm, indirect memory access patterns (e.g., irregular, read-intensive memory requests), similarity search and finding maximum flow \\ \hline Connected component (CCOMP) & Connectivity computation for graphs, irregular memory access with read-intensiveness, social graph analysis \\ \hline Degree centrality (Dcentr) & Connectivity computation for graphs, indirect and irregular memory accesses, social graph analysis \\ \hline Shortest (SPath) & Finding global optima algorithm for graph structure, indirect memory accesses with read-intensive characteristics, street navigation \\ \hline Page Rank (Prank) & Iterative computations for graph analysis, Compute-intensive with indirect memory requests, prioritizing web pages \\ \hline \hline \end{tabular}
\end{table}
Table 2: Workloads of Graph Processing.  
representing the data accessing sequence over time, and the y-axis, representing the memory address, shows that the access patterns of graph processing are heavily irregular, covering a wide range of memory space.  
In addition, the program for generating benchmarks is LDBC [4], which was used to generate the graph structure data including nodes, edges, and connection information, and the vertex and edge sizes were 1 and 28.82 MB each for generating data.
## 2 Visco-Elastic-Plastic Constitutive Model for Squeezing Rock  
### Derivation of the Komamura-Huang-Bingham Model  
The Komamura-Huang model is a visco-elastic-plastic model [33]. It consists of one Hooke body and two viscoelastic bodies (generalized Kelvin body) in series. It has been proven that it is useful for the simulation of the transient and the constant stages of the rheology of soft rock [34; 35]. The mathematical expression for the Komamura-Huang model is given as in [33]:  
\[\varepsilon(t)=\frac{\sigma_{0}}{E_{0}}+\sigma_{0}\bigg{\{}\frac{1}{E_{1}} \bigg{[}1-exp\bigg{(}-\frac{E_{1}}{\eta_{1}}t\bigg{)}\bigg{]}+\frac{1}{E_{2}} \bigg{[}1-exp\bigg{(}-\frac{E_{2}}{\eta_{2}}t\bigg{)}\bigg{]}\bigg{\}} \tag{1}\]  
where \(\varepsilon\) denotes the strain, \(t\) denotes the time, \(\sigma_{0}\) is the initial stress, \(E_{0}\) is the Hookean elasticity modulus, \(E_{1}\) and \(E_{2}\) denote the elastic moduli of the two generalized Kelvin body spring elements, respectively, and \(\eta_{1}\) and \(\eta_{2}\) are the viscosity coefficients of the two generalized viscous elements of the Kelvin body, respectively.  
The Komamura-Huang model excludes the accelerated regimes of rock rheology. Herein, the Bingham body, a nonlinear viscoplastic body, is combined with the Komamura-Huang model, resulting in a Komamura-Huang-Bingham model. At the level of the materials that determine rock mass, it has been demonstrated that the Komamura-Huang-Bingham model allows for all three regimes of the rock rheology to be described as in [33], and as shown in Figure 3. It is expressed as follows:  
\[\varepsilon(t)=\frac{\sigma_{0}}{E_{0}}+\sigma_{0}\Big{\{}\frac{1}{E_{1}} \Big{[}1-exp\Big{(}-\frac{E_{1}}{\eta_{1}}t\Big{)}\Big{]}+\frac{1}{E_{2}} \Big{[}1-exp\Big{(}-\frac{E_{2}}{\eta_{2}}t\Big{)}\Big{]}\Big{\}}+\frac{\big{(} \sigma_{0}-\sigma_{f}\big{)}t}{\eta_{3}}\ \ \ \sigma_{0}\geq\sigma_{f}, \tag{2}\]  
where \(\eta_{3}\) is the viscosity coefficient of the Bingham body for the viscous element, and \(\sigma_{f}\) and \(\sigma_{s}\) represent the long-term and instantaneous strength of the Bingham body, respectively. \(\eta_{3}\) is a function of time \(t\) and the stress \(\sigma_{0}\).  
\[\eta_{3}(t,\sigma_{0})=\frac{E_{0}}{A}\bigg{[}T+t\bigg{(}1-\frac{\sigma_{0}- \sigma_{f}}{\sigma_{s}}A\bigg{)}\bigg{]} \tag{3}\]  
where \(A\) is a dimensionless constant and \(T\) is the yielding time. At the condition of \(\sigma_{0}=\sigma_{f}+\sigma_{s}/A\), the nonlinear rheology degenerates into a linear rheology, \(\eta=E_{0}T/A\), and the strain increases at a constant rate of \(\dot{\varepsilon}=\sigma_{s}/E_{0}T\). At the condition of \(\sigma_{0}>\sigma_{f}+\sigma_{s}/A\), the value of \(\eta_{3}\) gradually decreases with time, reflecting the accelerated rheological process of the soft rock mass in a high-stress state. At the condition of \(\sigma_{0}<\sigma_{f}\), the rheological relationship for the nonlinear rheological model transforms into a linear expression. In more detail, the Komamura-Huang-Bingham model is simplified to the Komamura-Huang model.
## 3 Results: Medical History-Taking Challenges
### Observation 4: More than 70% Physicians Are Unable to Follow the Necessary History-Taking Steps  
To address the **RQ4**, this study revealed that in the process of history taking, **70.19%** could not follow all necessary steps (10 steps) during the consultation period. The step most frequently followed is \"inquiring about the chief complaint (CC)\" at a rate of 94.23%, while the least followed step is \"exploring the patient's ideas, concerns, and expectations (ICE)\" at 48.08%. Furthermore, 34.62% of doctors identified inconsistent responses as the primary challenge in obtaining a thorough medical history. Meanwhile, 30.77% attributed a lack of sufficient time due to a high volume of patients as a significant barrier to conducting comprehensive medical history assessments.  
Observation 5: More than 90% Physicians Expressed a Need to Use Digital Systems for Patients' Medical History Management  
Figure 4 shows the relationship between history-taking challenges and the willingness to adopt the proposed digital system. To answer **RQ5**, the study found that 64.42% expressed extreme interest, 16.35% were very interested, and 12.50% were moderately interested. In total, **93.27%** showed high to moderate interest, with an average Likert scale score of 4.37. The median score of 5.00 suggests that half rated themselves as \"extremely interested\". Surprisingly, among those facing history-taking challenges, willingness to adopt the system varied: 83% for constant challenges, 77% for almost every time, 53% for occasional challenges, and even 80% for those rarely encountering challenges. Notably, 50% of those who never faced challenges expressed interest in the system.  
Figure 3: The relationship between the history-taking approaches and encountered challenges. Physicians who do not store patient medical history or rely on paper-based records face history-taking challenges “almost every time” or “every time”. Conversely, those utilizing digital records experience challenges “never” or “almost never”.
These models provide two distinct bands to operate in the s- and x-band with resonances at 4.27 GHz, 5.42 GHz, and 12.4 GHz. A single-sided bowtie or a monopole bowtie in one layer with meander arms on the other layer was investigated in [18] to operate at three resonating bands. An isosceles triangular microstrip antenna coupled electromagnetically to unequal an arms V-shaped parasitic has been proposed in [19] to provide triple spectrums. This configuration can support six resonating bands at resonant frequencies of 2.88 GHz, 3.64 GHz, 3.95 GHz, 4.38 GHz, 4.81 GHz, and 5.6 GHz with five rejection bands between them where two of these rejection bands have a reflection coefficient level larger than 5 dB. Four spectrums for sub-6 GHz and mm-wave applications that have been radiated from a slotted patch of conical shape connected to a small triangular patch were proposed in [20]; the design resonant frequencies were 2.4 GHz, 5.2 GHz, 5.8 GHz, and 27.5 GHz. An antenna element of the 'F' shape placed above a truncated ground plane has been proposed in [21] to operate in four reconfigurable frequency modes. The design and exploration of a UWB/multi-band 5G antenna with Hexa-frequency stopping bands and seven passing bands are presented in this work, incorporating a meander line structure that can be also integrated into a designing coupler [22] or power divider [23]. This design incorporates both multiple resonant modes and frequency rejection strategies to provide seven operational bands. The antenna was designed in the style of a bowtie, with each side made up of a meander line. The antenna exhibits a directional pattern with a major lobe and back lobe at lower frequencies as an alternative to the normal omnidirectional radiation pattern that would be formed from a similar limited ground arrangement. The proposed antenna has been further investigated compared to [24] by addressing an equivalent circuit model, and the circuit components' value effect is parametrized in addition to the time-domain analysis of the output signal. The suggested antenna has been manufactured, and the similarity between simulation and measured results were observed. Finally, an expansion of the designed antenna has been extended to form a four-port MIMO antenna to investigate the mutual coupling along with other MIMO characteristics at all resonating bands.
## 2 Cross-Domain Collaborative Task Allocation Model
### Mathematical Model of Control Allocation
#### 2.4.3 Mathematical Model Construction of Control Allocation  
The radar detection capability, control, and backhaul data link connectivity were the key indicators used to determine the success or failure of the control allocation task. Since the three capability indicators are probability-based and share similar dimensions and mag nitudes, the radar detection capability advantage in Equation (8) and the control/backhaul data link connectivity advantage in Equations (13) and (14) were combined using the method outlined in Equation (15). Subsequently, the mathematical model and constraint conditions for control allocation in load agents were derived as presented in Equation (15):  
\[\begin{split}\max\biggl{\{}\prod\limits_{i=1}^{3}Q_{i}\biggr{\}}& =\min\biggl{\{}-\prod\limits_{i=1}^{3}Q_{i}\biggr{\}}=\min\{-Q_{1} \times Q_{2}\times Q_{3}\}\\ \text{s.t.}\Biggl{\{}&\begin{array}{l}\text{1}\sum \limits_{i=1}^{d}\sum\limits_{k=1}^{b}y(i,k)\leq d\\ \text{2}\sum\limits_{k=1}^{b}y(i,k)=1,i=1,2,\cdots,d\\ \text{3}\sum\limits_{i=1}^{d}y(i,k)\leq n,k=1,2,\cdots,b\end{array} \tag{15}\]  
where 1 indicates that the total number of load agents in each control allocation scheme is limited to no more than \(d\); 2 indicates that each load agent can only be controlled by one air relay control agent in a distribution; 3 indicates that each air relay control agent can stably control at most \(n\) load agents at the same time. In the actual calculation process, combined with the data communication processing capacity of our air relay control agent, \(n\) was set to 25.
## 4 Results and Discussions
### Experimental Results and Comparison
#### 4.3.1 Results Comparison on the Single Diode Model
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{\(V_{\text{L}}\) **(V)**} & \multirow{2}{*}{\(I_{\text{L}}\) **Measured (A)**} & \multicolumn{6}{c}{\(I_{\text{L}}\) **Calculated (A)**} \\ \cline{3-10}  & & & **ANS** & **BLPSO** & **CSO** & **CTLA** & **LWOA** & **SOS** \\ \hline \(1\) & \(-\)0.2057 & 0.7640 & 0.7639 & 0.7617 & 0.7614 & 0.7679 & 0.7631 & 0.7641 \\ \(2\) & \(-\)0.1291 & 0.7620 & 0.7625 & 0.7609 & 0.7606 & 0.7667 & 0.7618 & 0.7627 \\ \(3\) & \(-\)0.0588 & 0.7605 & 0.7613 & 0.7602 & 0.7598 & 0.7655 & 0.7607 & 0.7614 \\ \(4\) & 0.0057 & 0.7605 & 0.7601 & 0.7595 & 0.7591 & 0.7645 & 0.7597 & 0.7602 \\ \(5\) & 0.0646 & 0.7600 & 0.7590 & 0.7589 & 0.7585 & 0.7635 & 0.7587 & 0.7591 \\ \(6\) & 0.1185 & 0.7590 & 0.7580 & 0.7584 & 0.7579 & 0.7626 & 0.7578 & 0.7581 \\ \(7\) & 0.1678 & 0.7570 & 0.7571 & 0.7578 & 0.7573 & 0.7618 & 0.7570 & 0.7572 \\ \(8\) & 0.2132 & 0.7570 & 0.7561 & 0.7572 & 0.7567 & 0.7609 & 0.7561 & 0.7562 \\ \(9\) & 0.2545 & 0.7555 & 0.7551 & 0.7564 & 0.7559 & 0.7559 & 0.7552 & 0.7551 \\ \(10\) & 0.2924 & 0.7540 & 0.7537 & 0.7552 & 0.7546 & 0.7585 & 0.7538 & 0.7537 \\ \(11\) & 0.3269 & 0.7505 & 0.7514 & 0.7530 & 0.7524 & 0.7561 & 0.7517 & 0.7514 \\ \(12\) & 0.3585 & 0.7465 & 0.7473 & 0.7489 & 0.7484 & 0.7519 & 0.7477 & 0.7473 \\ \(13\) & 0.3873 & 0.7385 & 0.7400 & 0.7414 & 0.7412 & 0.7443 & 0.7406 & 0.7399 \\ \(14\) & 0.4137 & 0.7280 & 0.7273 & 0.7282 & 0.7288 & 0.7310 & 0.7280 & 0.7271 \\ \(15\) & 0.4373 & 0.7065 & 0.7068 & 0.7072 & 0.7091 & 0.7099 & 0.7076 & 0.7065 \\ \(16\) & 0.4590 & 0.6755 & 0.6751 & 0.6750 & 0.6789 & 0.6774 & 0.6759 & 0.6748 \\ \(17\) & 0.4784 & 0.6320 & 0.6306 & 0.6303 & 0.6369 & 0.6322 & 0.6315 & 0.6303 \\ \(18\) & 0.4960 & 0.5730 & 0.5719 & 0.5715 & 0.5812 & 0.5727 & 0.5726 & 0.5716 \\ \(19\) & 0.5119 & 0.4990 & 0.4994 & 0.4991 & 0.5119 & 0.4997 & 0.4999 & 0.4991 \\ \(20\) & 0.5265 & 0.4130 & 0.4134 & 0.4134 & 0.4288 & 0.4133 & 0.4137 & 0.4133 \\ \(21\) & 0.5398 & 0.3165 & 0.3173 & 0.3175 & 0.3342 & 0.3169 & 0.3173 & 0.3172 \\ \(22\) & 0.5521 & 0.2120 & 0.2122 & 0.2126 & 0.2292 & 0.2116 & 0.2120 & 0.2122 \\ \(23\) & 0.5633 & 0.1035 & 0.1029 & 0.1032 & 0.1181 & 0.1021 & 0.1026 & 0.1029 \\ \(24\) & 0.5736 & \(-\)0.0100 & \(-\)0.0091 & \(-\)0.0091 & \(-\)0.0025 & \(-\)0.0101 & \(-\)0.0094 & \(-\)0.0091 \\ \(25\) & 0.5833 & \(-\)0.1230 & \(-\)0.1243 & \(-\)0.1249 & \(-\)0.1180 & \(-\)0.1255 & \(-\)0.1245 & \(-\)0.1244 \\ \(26\) & 0.5900 & \(-\)0.2100 & \(-\)0.2092 & \(-\)0.2104 & \(-\)0.2078 & \(-\)0.2105 & \(-\)0.2092 & \(-\)0.2094 \\ \hline \multicolumn{2}{c}{SIAE} & \multicolumn{2}{c}{0.0182} & \multicolumn{2}{c}{0.0275} & \multicolumn{1}{c}{0.1347} & \multicolumn{1}{c}{0.0739} & \multicolumn{1}{c}{0.0191} & \multicolumn{1}{c}{0.0181} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fitting results for the single diode model. Figure 4: Convergence curves for the single diode model.
## 3 Results
### Model Predictions on the Image
#### 3.4.1 Spectral Resampling
When evaluating the performances of the more competent sensors (in this study), EnMap seems to give a less satisfactory classification map in Panel B, but a much better one in Panel C. Judging by the two panels (B and C), AVIRIS appears to yield the best results. Nevertheless, this is a qualitative evaluation of two scenes. Overall, when observing Panel A, the results obtained by the hyperspectral sensors (excluding CASI) and WV3 seem to capture well the contamination pattern and the uncontaminated zones. Figure 7 also nicely illustrates what was previously stated (in Section 3.3) regarding the interpretation of the quantitative validation results. The validation Kappa results indicate that WV3 has the best score. We stated that this score should not be interpreted as WV3 is better than the hyperspectral sensors, rather that it has great potential. Figure 7B, C proves that by showing that WV3 does not produce the best classification maps. It is more noticeable in Figure 7C which shows a scene with zero contamination, meaning that all red pixels (oil according to the model) are false-positive. There, WV3 seems to have a larger number of false-positives than the hyperspectral ones.