esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Adherence to the Host  
Uropathogenic _Escherichia coli_ (UPEC) encode at least nine types of fimbiae including type 1, P, S-fimbriae, and afimbrial adhesin I, which are encoded by the _fim_, _pap_, _sfa_, and _afa_ operons, respectively [66; 67] (Table S1). Among these, type 1 fimbiae are the most prevalent, with more than 90% of both commensal and pathogenic _E. coli_ strains harboring this adhesin [68]. In contrast, P-fimbriae, S-fimbriae, and afimbrial adhesin I are present in approximately 40-60%, 30-60%, and 0-12.5% of _E. coli_ isolates, respectively [69]. Type 1 fimbiae play a crucial role in UPEC pathogenesis, as they facilitate adherence to and invasion of bladder epithelial cells, allowing intracellular replication and the formation of intracellular bacterial communities (IBCs) [12; 13; 14]. These fimbiae, encoded by the _fimAICDFGH_ operon, play a crucial role in bacterial colonization, particularly in murine models of urinary tract infection (UTI). The expression of _fim_ is regulated by multiple transcription factors, including Histone like Nucleoid Structuring protein, H-NS, Factor for inversion stimulation, Fis, and Leucine responsive regulatory protein, Lrp. Additionally, a 314-base pair inverted repeat element located upstream of _fimA_ functions as a phase-variable switch (_fimS_), existing in either an ON or OFF state, controls the expression of the fimbial operon. The inversion of the _fimS_ switch is mediated by site-specific recombinases such as FimB and FimE, as well as IpuA, IpuB, and IpbA [70]. P-fimbriae enhance the virulence of UPEC by facilitating adhesion to uroepithelial cells in both in vitro and in vivo settings. Uropathogenic _Escherichia coli_ (UPEC) is the primary causative agent of urinary tract infections (UTIs); enters the host through the urethra, ascends to the urinarybladder causing cyystitis [71], and if left untreated, may progress to the kidneys, leading to pyclonephritis [72]. UPEC employs diverse virulence factors, including fimbial adhesins, to colonize host tissues and evade immune defenses. In various Gram-negative bacteria, the chaperone-usher pathway (CUP) functions as a molecular machine that facilitates the assembly of fimbiae on the bacterial outer membrane; the adhesins located at the fimbial tips play a crucial role in bacterial attachment to host epithelial receptors. UPEC strains encode multiple CUP operons. Several CUP-encoded fimbiae, including Auf, Dr, FIC, S, type 9, type 3, type 1, and P-fimbirae, have been identified in UPEC pathotypes. However, among those broadly conserved in UPEC isolates, only type 1 and P-fimbirae have been strongly associated with UTI pathogenesis [73]. Among these, P-fimbirae play a critical role in kidney colonization by binding to globoserine glycosophiloids on uroepithelial cells [74]. Unlike type 1 fimbiae, which mediate mannose-sensitive adhesion, P-fimbirae facilitate mannose-resistant adherence, promoting persistent infection, resistance to urinary flow, and immune evasion [66]. A recent study has elucidated the role of small RNA PapR in the regulation of P-fimbirae [75], as discussed below.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 1 Introduction  
While technological innovation has yielded substantial advancements, it has contemporaneously amplified security vulnerabilities, particularly concerning malicious software (malware) such as viruses, worms, Trojans, rootkits, and ransomware [1, 2, 3]. Ransomware, emerging as a significant threat since the 1980s, is engineered to impede access to critical data and disrupt operational continuity [4, 5]. Distinct from other malware, ransomware employs data encryption or locking mechanisms to extort victims, impacting both individual users and enterprise operations [6, 7]. Its capacity to compromise operational data and system resources renders it particularly pernicious. The proliferation of accessible development toolkits has facilitated the exploitation of unpatched systems and software vulnerabilities by malicious actors [8, 9]. The WannaCry attack, leveraging the EternalBlue vulnerability, serves as a stark illustration, compromising 230,000 systems across 150 nations and incurring losses exceeding USD 8 billion [2]. Ransomware's reach into Industrial IoT and Supervisory Control and Data Acquisition (SCADA) systems now most certainly threatens critical infrastructure [10]. This expansion unveils profound vulnerabilities and systemic risks, foreshadowing societal catastrophes without robust mitigation strategies and methodologies.  
Notable ransomware attacks on Industrial Control Systems (ICSs) critical infrastructure, such as the Colonial Pipeline attack in 2021, show the extensive disruption these attacks can cause to essential services and the economy [11]. These incidents highlight the need for stronger cybersecurity measures, particularly for Operational Technology (OT) systems, which manage critical infrastructure. The National Institute of Standards and Technology (NIST) has developed guidelines to improve OT security to emphasize the importance of performance, reliability, and safety in defending against any threats [11].
## 3 Results and Discussions  
### Initial Valve Opening of Mass Flow Controller  
Figure 2 shows the initial valve opening rate when charging helium gas in the order of 20%, 60%, and 100% instead of hydrogen gas. The x-axis of the graph indicates the measuring time, while the y-axis shows the initial valve opening rate.  
As shown in Figure 2, at a valve opening of 20%, the gas charging time was about 8.2 min. During the charging of helium gas, the input data remained stable. When the valve is 60% open, the gas charging time is reduced by 3.3 min due to increased gas induction from the back pressure and flow. Significantly, when the valve is fully open at 100%, the gas charging time is reduced by 2.2 min compared to other valve positions.  
By analyzing the three different rates, one can see that the time loss is represented by the shaded area in Figure 2. As valve opening increases, the charging time of helium exhibits a nonlinear trend. The reason for the nonlinear characteristics is verified that as density increases, the compression pressure is also increased from the charging times. More specifically, Gas particles collide and interact with each other. When compression occurs, the average distance between gas particles decreases, leading to an increase in the frequency of collisions between particles. These collisions convert the kinetic energy of the particles, causing compression. The temperature and pressure of a gas are closely related. When the temperature rises, the kinetic energy of gas particles increases, resulting in more collisions and interactions between particles. Consequently, the pressure also increases, leading to compression. In other words, it can be seen that hydrogen gas can generate more loss depending on the MFC valve opening amount and charging time than the characteristics of helium gas.  
The loss of compression due to valve opening is generally known to follow a linear relationship in theoretical terms. These linear findings can provide valuable insights when visualizing gas flow patterns rather than focusing solely on loss issues using the ideal gas equation of the state. However, as observed in this experiment, it was confirmed that the transient loss of gas during compression, losses through the orifice, and losses arising from temperature differences among gases already present inside the reservoir are all variables. Instead of considering these losses as problems, the authors were able to analyze them as characteristics of gas expansion resulting from thermal changes, a phenomenon that commonly occurs in various environments and gas charging facilities.
# Intelligent Compaction Measurement Value in Variability Control of Subgrade Compaction Quality
## 3 Methodology
### Numerical Simulation of Modulus of Vibration Compaction \(E_{vc}\)
#### 3.2.1 Computing Principle of \(E_{exc}\)  
In order to make indexes accurate and scientific, some assumptions are as follows: (1) the subgrade is composed of homogeneous and isotropic linear elastic materials; (2) the subgrade structure is a semi-infinite body which extends infinitely in horizontal direction and infinitely downward in the Z direction, and the stress and displacement in infinite depth are zero.  
The above assumptions regard the subgrade as an elastic semi-infinite space body. When a vertical concentrated force \(Q\) acts on the surface of subgrade, the deflection of any point in the semi-infinite body can be obtained by Bussinick's solution, as shown in Figure 3a. In the rectangular coordinate system, the vertical deformation of any point can be calculated as Equations (2) and (3) [17; 29].  
\[\omega=\frac{Q(1+\mu)}{2\pi E}\bigg{[}\frac{z^{2}}{R^{3}}+2(1-\mu)\frac{1}{R} \bigg{]} \tag{2}\]  
\[R=\sqrt{x^{2}+y^{2}+z^{2}} \tag{3}\]  
where \(\omega=\) vertical deformation at calculation points (m); \(\mu=\) [PERSON]'s ratio of material; \(E=\) modulus of material (Pa); \(R=\) distance between action point and calculation point (m); and \(x\),\(y\),\(z=\) coordinates of calculation points.  
Figure 2: Information composition of continuous compaction control index: (**a**) normal condition; (**b**) larger strength in underlying layer.  
When calculating the top deflection of the subgrade, the vertical coordinate \(z\) is zero, which is placed into Equations (2) and (3), and can be determined as in [24].  
\[s=\omega=\frac{Q(1-\mu^{2})}{\pi Er} \tag{4}\]  
\[r=\sqrt{x^{2}+y^{2}} \tag{5}\]  
where \(s=\) top deflection of subgrade (m); \(r\) = horizontal distance between action point and calculation point (m).  
When the roller acts on the subgrade, it can be regarded as an absolutely rigid object (EI\(\rightarrow\infty\)) because of its great stiffness and there would be no deflection, and the deflection of each point on the top surface of subgrade is the same as width of the steel wheel. According to elastic theory, the load distribution on the top surface of the subgrade is saddle-shaped, with a small center and large edge, as shown in Figure 3b. When calculating the deflection of the top surface of the subgrade, the element area \(dF=d\xi d\eta\) can be taken in the contact range, and the distributed load acting on the element area can be expressed by the concentrated load \(dQ=p(\xi,\eta)d\xi d\eta\). At this time, the deflection of the subgrade surface point can be obtained by [27].  
\[s=\iint_{F}ds=\frac{1-\mu^{2}}{\pi E}\iint_{F}\frac{dQ}{r}=\frac{1-\mu^{2}}{\pi E }\iint_{F}\frac{p(\xi,\eta)d\xi d\eta}{\sqrt{(x-\xi)^{2}+(y-\eta)^{2}}} \tag{6}\]
## 3 Experiments and Results  
### Datasets  
There are limited categories of objects in existing publicly available remote sensing datasets. Specially, GF satellites are of great significance to research of RSIs in China, but there are few annotated GF datasets of PSSs to the best of our knowledge.  
We firstly constructed a primary-and-secondary-schools dataset of GF images for object detection, with the resolution of 2 m. The GF satellite slice images used in our study are the fusion data of GF-1 and GF-6 from \"the Strategic Priority Research Program of the Chinese Academy of Sciences\". The crop size was set to 512 \(\times\) 512 pixels and samples were extracted from the GF slice images in Beijing-Tianjin-Hebei region. Finally, we obtained 1685 images. The split ratios of training, validate, and test datasets are 70%, 20%, 10%, respectively. Additionally, the objects are annotated with horizontal bounding boxes. In order to improve the generalization ability of the model, we used three augmentation methods such as color change, flip, and rotation, to extend the training samples. The study area is shown in Figure 6.  
Figure 6: Display of study area: (**a**) the study area in China; (**b**) the Beijing–Tianjin–Hebei region; (**c**) the datasets used in our study. Note: the secondary and primary schools are plotted by the red circles and blue triangles, respectively.
## 2 Materials and Methods
### Model  
\[Energycons_{pc}\ =\ \Big{(}GPPpc,\ GPPpc^{2},Pop\_dens,\ Roaddens\Big{)} \tag{1}\]  
All the variables used in the analysis are presented in Table 3. In this study, energy consumption per capita (_Energycons_pc_) is the dependent variable. The independent vari ables include gross provincial product per capita (_GPPpc_), population density (_Pop_dens_), and road density (_Roaddens_). Land surface temperature at night (_Landtemp_night_), rainfall (_Rainfall_), and wind speed (_Windspeed_) are used as instrumental variables. Gross provincial product per capita squared (_GPPpc\({}^{2}\)_) is included to capture the nonlinear relationship between _GPPpc\({}^{2}\)_ and energy consumption per capita, which may take the form of a turning point, typically represented by an inverted U-shape or U-shape curve. Three underlying effects explain the inverted U-shape: the scale effect (as the economy grows, energy consumption increases, leading to more pollution), the composition effect (a structural shift from low-tech to high-tech industries as the country develops), and the technical effect (benefits from high-tech and cleaner technologies following industrial transformation, resulting in reduced pollution during later stages of economic growth). Economies that exhibit all three effects are expected to follow the EKC pattern, which is characterized by an inverted U-shape [26].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types** & **Variables** & **Descriptions** & **Years** & **Sources** & **Expected Sign** & **References** \\ \hline \multirow{7}{*}{Dependent Variable} & \multirow{7}{*}{_Energy consumption per capita_} & \multirow{7}{*}{A variable that represents a proximity economic activity using _capita_} & \multirow{7}{*}{2015-2022 activity} & Metropolitan Electricity \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table}
Table 3: Data description.  
Figure 8: Overview of the steps of the methods. Source: Authors.  
The results of the panel unit root tests used to determine whether the variables in the model are stationary are shown in Appendix A, Table A1. If the variables are not stationary, it can lead to spurious regression results. The Levin, Lin, and Chu (LLC) test and the Im, Pesaran, and Shin (IPS) test were applied. The results indicate that most variables are stationary at the level, i.e., I(0). However, some variables, such as _Pop_dens_ of the northeastern (NE) region and _Landtemp_night_ of the southern (SO) region, are stationary at the first difference, i.e., I(1), after differencing. Additionally, the Breusch-Godfrey test was conducted to identify the presence of serial correlation, and variance inflation factors (VIFs) were calculated to detect multicollinearity issues (Appendix B, Table A2).
## 3 Results and Discussion
### Near-Surface Properties
[39] pointed out, the increased depth effect can be attributed to the generally decreased resistance of the material against plastic deformation [63]. As is to be expected in this context, the corresponding residual stress profiles depicted in Figure 3a,b also show lower absolute values, with peak longitudinal compressive stresses being approximately \(-475\) MPa for deep rolling at \(550\) \({}^{\circ}\)C compared to \(-700\) MPa for  
Figure 3: Residual stress profiles and integral width values in longitudinal ((**a**) + (**c**) and circumferential ((**b**) + (**d**)) directions plotted as a function of deep rolling temperatures at constant deep rolling force. Data of the condition deep rolled at RT using a deep rolling force of 855 N were recompiled from [39]. Figure 2: Hardness distribution in the near-surface area after deep rolling at \(550\) \({}^{\circ}\)C using a deep rolling force of 855 N. Data of the initial state condition and of the condition deep rolled at RT using a deep rolling force of 855 N were recompiled from [39]. the dr_855_RT condition. Interestingly, residual stress depth profiles of both conditions considered are characterized by a point of intersection at a similar depth as the hardness depth gradients, i.e., between 400 and 500 \(\upmu\)m. At this point, it has to be emphasized that according to the literature [64], XRD residual stress depth profiles recorded applying the layer removal method can be considered as valid when the removal depth is less than one tenth of the diameter of a cylindrical specimen. Considering the diameter of the specimen of 6 mm, as well as the successive effect of layer removal, reliable statements about the residual stress state at large depths can be regarded as doubtful. Thus, in the present work, evaluation of residual stresses and corresponding integral width has only been performed up to a depth of 500 \(\upmu\)m. In line with the discussions provided so far, a similar trend can be seen for the circumferential direction, revealing peak values of about \(-\)375 and \(-\)625 MPa for the dr_855_550 \({}^{\circ}\)C and the dr_855_RT conditions, respectively. Compared to the investigations by [PERSON] et al. [39], both residual stress and hardness profiles are negatively impacted by an increase of the deep rolling temperature from 200 to 550 \({}^{\circ}\)C. As already discussed above, it can be assumed that this indicates the turnaround point, from where on recovery processes and lowered YS outweigh the benefits of higher plastic deformations at elevated temperatures, at least when trying to induce the highest possible residual stress values in the direct vicinity of the surface.
## 4 Simulation and Results
### More Scenarios  
We also conduct more experiments to verify the flexibility and efficiency of the ROA. Based on Scenario 2, we set the same six UAVs having fixed start points and destination points in this experiment. We also add other UAVs having random start points and destination points in the section. Other variables are the same as the scenarios above. We conduct the experiments with the number of UAVs ranging from 7 to 20. The algorithm could generate the collision-free trajectories for all cases. Table 4 lists all the cases and time spent during ten iterations. From the table, we can find that the computational cost of the ROA is acceptable.
# Dual Enhancement Network for Infrared Small Target Detection
## 3 The Proposed Dual Enhancement Network
### Loss Function  
We adopted the binary cross-entropy (BCE) loss \(\ell_{\text{bce}}\) and the soft intersection over union (Soft-IoU) loss \(\ell_{\text{iou}}\) to supervise the final prediction during the network training, and the total loss function \(\ell_{\text{all}}\) is expressed as follows:  
\[\ell_{\text{all}}\,=\lambda\ell_{\text{bce}}\,+(\lambda-1)\ell_{\text{iou}} \tag{4}\]  
The combined use of the BCE loss function and the Soft-IoU loss function offers significant advantages for infrared small target detection. The BCE loss function aids in learning target boundaries and features effectively, while Soft-IoU enhances stability and robustness by considering pixel-level matching, crucial for tasks with blurry boundaries or uncertainty. By integrating these two loss functions, the model can comprehensively address both object detection and pixel-level segmentation requirements, leading to improved segmentation accuracy and robustness. In summary, this combined approach represents a rational and effective strategy, leveraging the strengths of both loss functions to enhance model performance and generalisation ability in infrared small target detection tasks.  
By adjusting the equilibrium parameter \(\lambda\) in the loss function, DENet has the capability to dynamically adjust the model's emphasis on accuracy and false alarm rate in accordance with diverse task requirements, thus optimising the overall performance, where \(\lambda\)= 0.8 denotes the balance parameter.
## 3 Experiments
### Comparison  
The performance of the proposed distilled lightweight RIMformer is compared with that of two other advanced signal processing methods and one deep learning-based method, i.e., the WD [8], ANC [7], and BiGRU-based method [19]. The pre-trained model is converted to the ONNX format and deployed with TensorRT to further reduce inference time.  
Visual comparisons are shown in Figure 11. It can be seen that the WD method and the ANC method retain many interference signals. Both the proposed lightweight RIMformer and the BiGRU-based method effectively remove the interference. Quantitative results are listed in Table 3. The inference time of the proposed method decreases notably, with a consistent performance upon conversion to the ONNX format and deployment with TensorRT. The lightweight RIMformer significantly outperforms WD and ANC in terms of PSINR and SINR, with comparable inference times. After interference suppression,  
Figure 10: The performance of models trained by the three different ways at different SNRs.  
Figure 9: Comparison of the output of different models trained by different methods in the form of RD maps.  
WD and ANC have similar PSINR values, but ANC exhibits a considerably lower SINR due to more residual interference. The PSINR and SINR of the BiGRU-based method are slightly lower than those of the lightweight RIMformer, but it requires more inference time. The RD maps visually illustrate the effectiveness of the proposed method in interference suppression, as shown in Figure 12.  
Figure 11: Comparison of time-domain waveforms and time-frequency plots after interference suppression.  
The simulated experiments demonstrate the effectiveness of the proposed lightweight design improvements and the two-stage KD process for reducing the RIMformer network's inference time. The incorporation of a linear attention mechanism and a deep separable convolution significantly reduces the network size and inference time. The two-stage KD process further optimizes the model by reducing complexity without substantial loss in performance, which makes the proposed approach highly efficient and practical for real-time interference mitigation applications.