esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Automated Data-Generation and Labeling
### Camera Simulation  
The two primary components of a digital camera are the optics (usually a combinations of lenses) and the sensor at the focal plane array, typically a CCD or CMOS array. In MAVS, the lens system is simulated using a radial distortion model [34; 35]. An example of the effect of distortion is shown in Figure 3. The corresponding distortion parameters are shown in Table 1, along with their values in this example.  
Figure 3: Example of the effect of distortion in the MAVS camera simulation. (**Left**) Distorted image. (**Right**) Undistorted image.  
Note that in the aerial camera simulated for this project, there was no simulated distortion.  
The incident model on the focal plane array is modeled using the Phong model with diffuse term  
\[I_{p}=k_{d}\bigg{(}\overset{\rightarrow}{L\cdot N}\overset{\rightarrow}{} \bigg{)}i_{d}+k_{s}\bigg{(}\overset{\rightarrow}{R\cdot V}\overset{\rightarrow} {}\bigg{)}i_{s}\]  
In this equation, \(I_{p}\) is the illumination at a given point, i\({}_{d}\) is the incident diffuse irradiance at that point, i\({}_{s}\) is the incident specular irradiance, \(R\) is the specular direction, \(N\) is the surface normal, \(L\) is the direction from the point to the light source, and \(V\) is the vector pointing to the viewer. The material properties k\({}_{d}\) and k\({}_{s}\) specify the diffuse and specular reflectances. These have the constaint that they must both be positive and sum to less than 1.0 (\(k_{d}+k_{s}<\) 1.0). In the MAVS simulator, the specular component is set to zero for most natural surfaces and the the diffuse component is estimated from field data.  
For each pixel, the total contribution of incident ration from all points in the scene is estimated using path tracing. The response of the focal plane array is then modeled using a power law model.  
\[I_{ij}=AL_{ij}^{\gamma}\]  
where \(I_{ij}\) is the output intensity of pixel (_i,j_), L\({}_{ij}\) is the irradiance on pixel (_i,j_), \(\gamma\) is the compression factor, and \(A\) is the gain. The compression factor (\(\gamma\)) is usually set to less than one. The effect of range compression is shown in Figure 4, which demonstrates how the dynamic range of the color and intensity of the image is reduced when decreasing \(\gamma\).  
In the experiments shown in this work, the radial and tangential distortion were set to zero and the compression factor was \(\gamma=0.6\).
# A Cognitive Environment Modeling Approach for Autonomous Vehicles: A Chinese Experience
## 3 Spatial-Time Environment Model Statement
### Dynamic Object Discretization  
As shown in Figure 5. Dynamic object discretization is a process that projects time-varying objects onto the spatial-time environment model. Dynamic objects are objects whose position and shape change over time, including solid dynamic objects and virtual dynamic objects. (1) Solid dynamic objects are real dynamic objects in the environment, including dynamic obstacles and objects whose shape changes over time. An example of a dynamic obstacle is shown as the blue dots in Figure 5, which is an accelerating vehicle, represented as a curve in the spatial-time domain. Non-stationary objects such as moving vehicles and walking people are typical solid dynamic objects. (2) Virtual dynamic objects are time-varying but stationary objects that appear and disappear over time. An example of a time-varying virtual dynamic object is shown as the black dots in Figure 5. It appears at \(t=2\) s, and disappears after \(t=3\) s. Virtual dynamic objects include traffic lights, speed limit signs, etc.
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 4 Experiments
### Dataset  
In the experiments, we adopt the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) [36] dataset released by the National Cancer Institute, NIH. The LIDC-IDRI dataset is the largest and most comprehensive publicly available lung nodule dataset. There are 1018 CT scans in total collated from 1010 patients. The malignant degree of each nodule in the LIDC-IDRI dataset was judged by four professional radiologists. The experts annotated nodules with diameters between 3 and 30 mm in each CT scan, but only a portion of the nodules was annotated by at least three-quarters of the radiologists. Therefore, we use a unified standard to define nodules for experiments. The four radiologists also annotated nodules on the malignancy suspiciousness of one to five, where one denotes that a nodule is clearly benign and five represents clearly malignant. As previous works [5; 15], we aggregate all of the malignancy ratings by experienced radiologists as the final level. Eventually, we obtain 848 nodules in total of which 442 are benign and 406 are malignant.
## 6 Comparative Performance Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**MPPT Algorithms**} & \multicolumn{3}{c}{**SPV Output Power at Fix 1000 W/m\({}^{2}\)**} & **Remarks** \\ \cline{2-4}  & **25 \({}^{\circ}\)C** & **30 \({}^{\circ}\)C** & \\ \hline P\&O Algorithm [28] & 90.13 kW & 86.73 kW & Conventional \\ I\&C Algorithm [28] & 94.52 kW & 90.91 kW & Conventional \\ GA-based optimized FLC [28] & 95.09 kW & 89.56 kW & Large training \\  & & & data required \\ PSO-based optimized FLC [28] & 96.03 kW & 90.1 kW & computational \\ PSO-GA-based optimized FLC [28] & 98.7 kW & 94.47 kW & complexity \\  & & & increases up to 8\% of \\  & & & output power with high \\ Proposed ICPSO & 97.3 W & 92.4 W & computational time \\  & & & computational time \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative performance analysis under variable temperature and constant irradiance level. Figure 11: Comparative analysis of I&C algorithm and proposed ICPSO algorithm.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 4 Sensor Deployment Files
### Sensor Model  
The Sensor Model is a generic SensorML description of a family of sensors having common characteristics. As shown in Figure 7, it should include the sensor's command set, its communications interface and other information applicable to all the sensors that share this model.  
The _DataInterface_ element is used to model the communication interface. As the interface parameters may depend on each deployment of the sensor, only the available communication protocols are defined at the Sensor Model stage.  
A key aspect of a sensor model is the description of the sensor's set of commands. Each command is described with a SensorML's _SimpleProcess_ element. At least one _SimpleProcess_ is required in a Sensor Model (a sensor should at least provide one operation to retrieve data). The inputs of these processes define the command that the sensor expects, while the output corresponds to the sensor response to that command. If a sensor command does not have a reply to a specific command, the _SimpleProcess_ will only have an input and no output. On the contrary, if the sensor streams data periodically the _SimpleProcess_ used to model it will not have any input, but will have an output representing this stream. To model a command where the sensor responds to a specific command, both input and output need to be included.  
Both input and output have an _encoding_ section which describes how their contents (e.g., parameters, output values) are encoded. The output also contains an encoding element alongside with an array of fields, which are used to model the sensor response. Each field has a name, a _description_ (reference to a controlled vocabulary) and a _type_, which corresponds to the SWE Common Data model encoding used to model this value [38]. When a physical magnitude is described, the units of measurement should be specified using the _nom_ (units of measurement) element.  
Figure 6: Sensor and SWE Bridge with their models (**left**) and Sensor Deployment Files (SDF) model (**right**). The Sensor, with its associated SDF, is interfaced by the SWE Bridge middleware, which also has its own Sensor Model Language (SensorML)-based description, the SWE Bridge Model.
## 5 Experiments
### Ablation Study  
Ablation experiments on trajectory space reconstruction were conducted. In this section, three ablation experiments are described for the time series prediction model. Specifically, three types of historical trajectories were used in the model. In the ablation experiments, one of the trajectories was removed to test its role in the trajectory sequence. For example, the historical trajectory was removed, and the same-period and same-state historical trajectories were input into the model; this model was named Attn-h. Similarly, the models without the same-period historical trajectory or the same-state historical trajectory were named Attn-c and Attn-s, respectively. Table 2 shows the results of the model ablation experiments. From the experimental results, it can be seen that all three types of historical trajectories are effective in the model. Removing any one of the historical trajectories has a negative impact on the overall performance of the model. Moreover, the experimental results show that when the same-period trajectory is removed, the model's performance is the most affected, with a 3.10% impact on the STLC metric and a 0.89% impact on the STLCSS metric. The same-state historical trajectory has a 1.99% and 0.62% impact on the two metrics, respectively. All historical trajectories have a 1.65% and 0.43% impact on the two metrics, respectively. This indicates that the same-period historical trajectory has the greatest impact on the results among the three trajectories, occupying the dominant position. Similarly, we can see that the same-state historical trajectory has a greater impact on the experimental results than all historical trajectories, and the deletion of the historical trajectories has a relatively small impact on the experimental results, but still has a certain impact.
## 4 Results and Discussion
### Modified Compact Tension Specim (MCTS)
#### 4.1.4 G5 Specimen  
This specimen clarifies the influence of the hole size on the crack growth direction, the diameter of the hole, d\({}_{1}\), was reduced to 2 mm in the G5 specimen. The \(h_{1}\) and \(v_{1}\) are identical to those of G2. The presence of a hole causes the crack to turn its growth path toward the hole to some extent. The crack in specimen G5 slightly turns the growth direction as shown in Figure 12, exhibiting good agreement with the experimental path reported by [32]. Figure 12d,e illustrate two further comparisons using the numerical data provided by [29] and the FRANC3D [28] findings, which showed a very short crack growth path as compared to the experimental and numerical results in the present study. As illustrated in Figure 13a, there were also similar results for \(K_{I}\) with the numerical results obtained by [29]. In addition, Figure 13b shows the predicted values of \(K_{II}\), which have a small amount and indicate that there were a slightly influence of \(K_{II}\) on the crack growth direction.  
Figure 11: Stress intensity factors with corresponding crack length for specimen G4, (**a**) \(K_{I}\), and (**b**) \(K_{II}\).  
Figure 10: Predicted crack growth path for G4 specimen (**a**) ANSYS Workbench, (**b**) FRANC2D/L and (**c**) Experimental [32], (**d**) numerical [33], (**e**) FRANC3D [32].  
The predicted values in both software for the first and second modes of SIF are shown in Figure 13. The range of KI calculated by FRANC2D/L was between 580 and 890 MPa.mm\({}^{0.5}\), whereas the ANSYS results were in a range from 550 to 860 MPa.mm\({}^{0.5}\). The difference in the SIF values is approximately 3.4%.
# Orientation of Liquid Crystalline Molecules on PDMS Surfaces and within PDMS Microfluidic Systems
## 3 Results
### LC Orientation in PDMS:PDMS and PDMS:Glass Microstructures
#### 3.2.1 3D Printing  
Despite our previous tests indicating the roughness of the SLA 3D-printed molds [9], studies on the LC molecular orientation were undertaken due to this fabrication method's simplicity. The 3D printing can be performed apart from additional equipment or initial preparations. The process does not require mask fabrication, precise equipment operation, or access to sophisticated laboratory facilities. Specifically, a microchannel structure with a cross-section of 200 \(\times\) 200 \(\upmu\)m was designed. Due to the type of resin applied, the mold was slightly deformed in the areas between the channels. Moreover, the structure measurements confirmed the increased dimensions of the microchannels to 240-260 \(\upmu\)m in width. After about 5 min from infiltration (with the volumetric flow rate varying in the range of 0.7-1.8 \(\upmu\)L/min), the orientation of the E7 NLC molecules was as shown in Figure 3a,b along the entire length of the 10-mm-long microchannel in the PDMS:PDMS structure. The area where the molecules could be oriented perpendicularly to the bottom layer of the PDMS channel was relatively narrow. This is related to the value of the channel aspect ratio (high/width, \(h/w\)) being close to one for which the anchoring contribution from the side walls matched up to the contribution from the top surface (please refer to the results of the numerical simulations shown in Figure 6(k1-k3) for guidance). Nevertheless, the firmly rounded edges on the upper part of the channel and the inclinations of highly imperfect sidewalls towards the substrate plane did not allow a perfect VA alignment at the PDMS-LC intersections. The polarizing optical micrographs in Figure 3a,b show high irregularities in the molecular arrangement obtained in the channel. In addition, the orientation became totally random three days after infiltration, which was manifested by the scattering of light by the defects and spatial variation in the transmitted light intensity (see Figure 3c). Replacing the substrate with a glass plate did not change the quality of the LC molecular arrangement.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 4 Clinical Problems
### Drug Resistance  
According to the WHO Global Tuberculosis Report, tuberculosis is a worldwide threat, causing more than 1 million fatalities each year. However, the standard TB treatment achieves only 85% success [9]. Mtb evolves resistance to drugs, resulting in patients developing MDR-TB [82]. Although no clinical evidence supports that mycobacterial biofilm leads to treatment failure so far, biofilm-related drug resistance has been found in in vitro experiments and animals. It was found that biofilm cells do not grow better than planktonic cells, but they show increased survival [83; 84]. Statistically, [PERSON] et al. found that approximately 10% of Mtb within a biofilm did not respond to INH even in a concentration beyond MIC; furthermore, high rifampicin exposure significantly attenuated Mtb biofilm viability in the first three days but failed to cause any damage in further treatment [4]. In another study, groups of mice infected with Mtb were given different therapies--INH and rifampicin oral treatment with or without nebulized cellulase. The results showed lower pulmonary tissue destruction and fewer live bacilli in the cellulase-treated group, indicating that biofilm can hinder the function of chemotherapeutic agents [6].  
As a peculiar form of bacteria colony, biofilm has unique mechanisms for developing drug resistance. The normal mechanisms of drug resistance, such as efflux pumps, modifying enzymes, and target mutations [85], do not contribute significantly to biofilm drug recalcitrance. Even sensitive bacteria that are susceptible to antibiotics genetically become recalcitrant once they form biofilm [86]. These mechanisms are illustrated as follows: First, the surrounding EPS serves as a diffusion barrier against large molecules [84]. As for smaller molecules, the blocking function of biofilm can be seen as it deactivates them before they enter cells. Since EPS is negatively charged, positively charged agents, for example, aminoglycos antibiotics, have retarded penetration because they are trapped by EPS [87]. Second, the efficiency of anti-TB drugs is challenged by the changing environment. Biofilm has microscale gradients comprising nutrients, wastes, and metabolic products that cooperatively influence its microenvironment [88]. A slight change in oxygen and pH, for example, can result in frustrated antibiotic activity [86]. Another important mechanism is that some cells in biofilm grow rather slowly or even stop growing due to nutrient limitation [71]. Because most of the current antimicrobials are designed to inhibit biosynthetic processes, they are ineffective at eliminating bacteria in a quiescent state, which are either slow-growing or dormant [87]. However, investigations of genetic mechanisms of drug resistance in biofilm focused mainly on _P. aeruginosa_ and _Escherichia coli_ so far; what happens in Mtb biofilm has not been clarified and needs to be explored.
## 2 Theoretical Derivations
### Calculation of Area of Prestressed Steel Strands
#### 2.2.1 Stress of PCCPs under Combined Loads
2.3 Checking Calculation of Prestressed Strands Considering the Concrete Core Compression of Serviceability Limit States  
The maximum bending moment of the pipe at the top or the bottom, \(M_{pms}\), is calculated as per Equation (28). The value of \(M_{pms}\) is negative, indicating that the outer surface of the concrete core is subjected to tension. The absolute value is taken when substituting the following equations. The axial tension of the pipe wall, \(N_{ps}\), is written as per Equation (29):  
\[M_{pms}=\gamma_{0}\Big{[}k_{vm}\Big{(}F_{svk}+\psi_{c}q_{vk}D_{1}\Big{)}+k_{hm }F_{epk}D_{1}+k_{vm}G_{vk}+k_{gm}G_{1k}\Big{]} \tag{28}\]  
\[N_{ps}=\psi_{c}P_{d}r\times 10^{-3} \tag{29}\]  
where \(k_{vm}\), \(k_{hm}\), \(k_{mm}\), and \(k_{gm}\) represent the bending moment coefficient of the bending moment at the top or the bottom of the pipe under the vertical earth pressure, lateral earth pressure, the weight of fluid inside the pipe, and the weight of the pipe, respectively. These factors can be determined according to Appendix E [27]. The \(k_{gm}\) of the arc-shaped soil bedding can adopted, according to the data of the bedding angle of \(20^{\circ}\). The maximum tensile stress at the edge of the pipe at the bottom, \(\sigma_{ss}\), is calculated as per Equation (30). \[\sigma_{ss}=\frac{N_{ps}}{A_{n}}+\frac{M_{pms}}{\omega_{c}W_{p}} \tag{30}\]  
where \(A_{n}\) is the conversion area of the pipe section (including the cylinder, steel strands, and the mortar coating) (m\({}^{2}\)/m). \(\omega_{c}\) is the conversion coefficient of the elastic resistance moment of the tensioned edge of the pipe -wall. \(W_{p}\) is the momentary elastic resistance of the unconverted tension edge of the rectangular section of the pipe wall (m\({}^{2}\)/m). The effective prestress of the prestressed steel strands after the prestress loss, \(\sigma_{\text{st}}^{\prime}\), can be written as \(\sigma_{st}^{\prime}=\sigma_{st}-\sigma_{st,l}\). Therefore, the area of prestressed strands of serviceability limit states should be calculated by Equation (31):  
\[A_{st}\geq\Big{(}\sigma_{ss}-K\gamma f_{ty}\Big{)}\frac{A_{n}}{\sigma_{st}^{ \prime}} \tag{31}\]  
where \(K\) is the influence coefficient of concrete in the tension area, \(\gamma\) is the plastic influence coefficient of concrete in the tension area, and \(f_{ty}\) is the standard value of concrete tensile strength. The area of prestressed strands needs to simultaneously meet the requirements outlined in Equations (27) and (31).
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 4 Experimental Study
### Experiment 1: Decomposing a Multi-Class Problem
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**IoU** & & & & & & & & \\ \hline \multirow{2}{*}{**City**} & \multicolumn{3}{c}{**Multi-Class**} & \multicolumn{3}{c}{**OVO**} & \multicolumn{3}{c}{**OVA**} \\ \cline{2-9}  & **Background** & **Building** & **Road** & **Background** & **Building** & **Road** & **Background** & **Building** & **Road** \\ \hline Bilbao & 0.6835 & 0.5080 & 0.3728 & 0.8047 & 0.6108 & 0.4976 & **0.8381** & **0.6367** & **0.5118** \\ Granada & 0.6361 & 0.3222 & 0.3472 & 0.8550 & **0.5745** & 0.5012 & **0.8841** & 0.5660 & **0.5554** \\ Leon & 0.6087 & 0.2508 & 0.3217 & 0.8485 & 0.6944 & 0.5355 & **0.8756** & **0.7020** & **0.5826** \\ Lugo & 0.6105 & 0.1333 & 0.3472 & 0.7859 & 0.5494 & 0.4605 & **0.8501** & **0.5932** & **0.4798** \\ Madrid S. & 0.6175 & 0.4189 & 0.3773 & 0.8195 & 0.5492 & 0.5970 & **0.8676** & **0.5705** & **0.6392** \\ Oviedo & 0.6565 & 0.4574 & 0.3272 & 0.8239 & 0.5160 & 0.4750 & **0.8604** & **0.5430** & **0.4894** \\ \hline Average & 0.6320 & 0.3994 & 0.3565 & 0.8279 & 0.5629 & 0.5427 & **0.8670** & **0.5789** & **0.5828** \\ \hline \hline \multirow{2}{*}{**F-Score**} & \multicolumn{3}{c}{**Multi-Class**} & \multicolumn{3}{c}{**OVO**} & \multicolumn{3}{c}{**OVA**} \\ \cline{2-9}  & **Background** & **Building** & **Road** & **Background** & **Building** & **Road** & **Background** & **Building** & **Road** \\ \hline Bilbao & 0.8119 & 0.6736 & 0.5431 & 0.8918 & 0.7574 & 0.6641 & **0.9119** & **0.7771** & **0.6764** \\ Granada & 0.7775 & 0.4874 & 0.5154 & 0.9218 & **0.7297** & 0.6677 & **0.9385** & 0.7228 & **0.7141** \\ Leon & 0.7568 & 0.4009 & 0.4866 & 0.9180 & 0.8196 & 0.6974 & **0.9337** & **0.8248** & **0.7362** \\ Lugo & 0.7578 & 0.2350 & 0.5143 & 0.8801 & 0.7083 & 0.6305 & **0.9189** & **0.7444** & **0.6484** \\ Madrid S. & 0.7635 & 0.5904 & 0.5479 & 0.9008 & 0.7088 & 0.7476 & **0.9291** & **0.7265** & **0.7799** \\ Oviedo & 0.7926 & 0.6276 & 0.4930 & 0.9034 & 0.6807 & 0.6439 & **0.9249** & **0.7037** & **0.6569** \\ \hline Average & 0.7742 & 0.5666 & 0.5252 & 0.9057 & 0.7193 & 0.7021 & **0.9287** & **0.7325** & **0.7347** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results obtained in terms of averaged IoU and F-score metrics, in the testing set for Experiment 1.