esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Result Analysis of Road Intersection Extraction  
Figure 10 shows the results of internal turning mode recognition and road intersection structure extraction. The red lines in Figure 10a are the structural models of road intersection extracted by our method. Although the study area has various kinds of road intersections with different shapes and structures, the proposed method can correctly extract complex and diverse road intersection structures based on density homogenization and hierarchical segmentation. In particular, Figure 10b, Figure 10d and Figure 10c, Figure 10e illustrate the enlarged views of the generated geometric models and extracted turning modes of the two road intersections in the green and blue circles in Figure 10a, respectively. It can be seen that the generated geometric models and extracted turning modes have high consistency with the actual remote sensing images, demonstrating the high efficiency and accuracy of the hierarchical segmentation method on extracting road intersection information for routable road map construction.  
Furthermore, turning mode segmentation is the key step of road intersection extraction. To quantitatively evaluate the effectiveness of turning mode segmentation, we randomly selected 16 road intersections (including five cloverleaf intersection, five cross intersection, three T-shaped junctions and three Y-shaped junctions) and manually counted the internal turning modes within them for the ground truth. By comparing with the ground truth of manual recognition, the accuracy statistics results based on formulas (8) are also listed in the last row of Table 3, showing that the _Precision_ indicator of turning modes recognition is about 85.7%, the _recall_ indicator is about 88.4%, and F value is about 87.0%. That further indicates that the proposed method by combining trajectory density homogenization and hierarchical trajectory segmentation can efficiently deal with spatio-temporal heterogeneity and uneven density distribution of crowdsourced trajectory data and achieve good performance on generating structural road intersection map.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **True Positive** & **False Positive** & **False Negative** & **Precision** & **Recall** & **F1-Value** \\ \hline \multirow{3}{*}{Object-level segmentation} & Without & & & & & & \\  & homogenization & & & & & & \\  & With & & & & & & \\  & homogenization & & & & & & \\ \hline \multicolumn{2}{c}{Turning-modes segmentation} & 229 & 38 & 30 & 85.7\% & 88.4\% & 87.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy statistics of road intersections and turning modes identification.
# Embedded Federated Learning for VANET Environments
## 5 Results
The complete sequence of tests was as follows:  
* One Jetson Nano with only one client (a total of one client);
* One Jetson NX Xavier with only one client (a total of one client);
* Four Jetson Nanos with one client and one Jetson NX Xavier with one client (a total of five clients);
* Four Jetsons Nano with five clients and one Jetson NX Xavier with five clients (a total of twenty-five clients);
* Four Jetsons Nano with ten clients and one Jetson NX Xavier with ten clients (a total of fifty clients);
* Four Jetsons Nano with fourteen clients and one Jetson NX Xavier with fourteen clients (a total of seventy clients). Figure 5 illustrates the testbed configuration with all devices and shows the configuration details (IP, ports, etc. ), where each federated learning (FL) client was deployed in a container. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **CNN** & & & **LSTM** & \\ \cline{2-7}
**Client** & **MSE** & **MAE** & \(R^{2}\) & **MSE** & **MAE** & \(R^{2}\) \\ \hline
1 & 0.004 & 0.033 & 0.782 & 0.006 & 0.045 & 0.644 \\ \hline
2 & 0.011 & 0.058 & 0.742 & 0.013 & 0.062 & 0.710 \\ \hline
3 & 0.012 & 0.069 & 0.862 & 0.018 & 0.083 & 0.793 \\ \hline
4 & 0.013 & 0.065 & 0.789 & 0.018 & 0.086 & 0.680 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CNN versus LSTM. Figure 5: FL client distribution and its connection with server. Figure 6 depicts the strategic points where measurements were taken. In the figure, signaled by the letters from a to e, all the stages of a federated learning round (FLR) are shown. The following times were evaluated during one round: training the local model (arrow a), sending that model to the server (arrow b), aggregating the local models (arrow c), downloading the aggregated model to the clients (arrow d), and finally, evaluating the global model with the client's local data (arrow e). As mentioned earlier, the data for each test were averaged from the 10 results obtained from 10 repetitions for the same test. The next sub-sections discuss several tests and results. We start with an overview of the aggregation process, followed by an evaluation of the training time and communication delay of the machine learning (ML) model. After evaluating the performance of the solution based on federated learning (FL), we analyze the direct consumption of resources. In this way, we assess the server and Jetsons resource overloads.
## 5 Experiments
### Creating Compact Maps through Clustering
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Values** \\ \hline \multirow{2}{*}{Environment} & Quorum V \\  & Freiburg (COLD) \\  & Saarbrücken (COLD) \\ \hline \multirow{2}{*}{Descriptor} & FS \\  & HOG \\  & _gist_ \\ \hline \multirow{3}{*}{Descriptor parameters} & FS: \(k_{1}\) = 4, 8, 16, 32, 64, 128, 256 \\  & HOG: \(k_{2}\) = 2, 4, 16, 32, 64, 128 \\  & _gist_: \(k_{3}\) = 2, 4, 8, 16, 32, 64 \\  & _gist_: \(n_{masks}\) = 2, 4, 8, 16, 32, 64 \\ \hline \multirow{3}{*}{Number of clusters} & Quorum V: \(n_{c}\) = 15, 25, 40, 60, 80, 100 \\  & Freiburg: \(n_{c}\) = 10, 20, 30, 40, 50, 60, 70 \\ \cline{1-1}  & Saarbrücken: \(n_{c}\) = 10, 20, 30, 40, 50, 60, 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the parameters that have been varied to carry out the clustering experiments. FS, Fourier Signature. \(N\) is the number of instances (images), and \(s_{w}\) is the silhouette of each instance; it is calculated as:  
\[s_{w}=\frac{b_{w}-a_{w}}{max(a_{w},b_{w})} \tag{9}\]  
where \(a_{w}\) is the average distance between the capture point of the instance \(\overrightarrow{d}_{w}\) and the capture points of the other instances in the same cluster, and \(b_{w}\) is the minimum average distance between the capture point of the instance \(\overrightarrow{d}_{w}\) and the capture point of the instances in the other clusters. Differently, the average silhouette of descriptors is traditionally obtained through:  
\[S_{descr}=\frac{\sum_{k=1}^{N}s_{k}}{N} \tag{10}\]  
where \(N\) is the total number of instances and \(s_{k}\) is the silhouette of each instance. This value is calculated as:  
\[s_{k}=\frac{b_{k}-a_{k}}{max(a_{k},b_{k})} \tag{11}\]  
where \(a_{k}\) is the average distance between the descriptor \(\overrightarrow{d}_{k}\) and the descriptor of the rest of the entities contained in the same cluster, and \(b_{k}\) is the minimum average distance between \(\overrightarrow{d}_{k}\) and the instances contained in the other clusters. The silhouette of descriptors has been traditionally used to measure the compactness of the clusters. However, it does not measure the geometrical compactness. This is why we introduce the silhouette of points, which can provide more proper information since we are interested in knowing whether the clusters have grouped images captured nearby.
## 2 Antenna Arrays
### Linearly Arranged Circular Antenna Array (LCAA)  
In Figure 1a, the LCAA is shown where 108 elements are used. The beamformer consists of three circular arrays, each with 36 elements, for a total of 108 elements. The spacing between the two adjacent elements is \(0.5\beta\). There are three rings with identical structures; each circle contains the same number of elements and maintains the same distance between two elements.  
Figure 1: (**a**) Linear circular antenna array (LCAA); (**b**) linear rectangular antenna array (LRAA); (**c**) linear circular planer antenna array (LCPAA); (**d**) linearly arranged concentric circular antenna array (LCCA).
## 5 Performance Evaluation of Classification Algorithms  
To assess the performance of classifiers and to select the most appropriate classifier for a given task and image type (here, the recognition of built-up areas from MR and VHR satellite images), several experiments were carried out on the reference datasets. For performance evaluation, commonly used accuracy measures (sensitivity, specificity and overall accuracy) were estimated using a ten-fold cross-validation. Overall accuracy is defined as the total number of correctly classified segments divided by the total number of test segments. Sensitivity, also referred to as the true positive rate or producer's accuracy, means the proportion of actual positives, which are classified as positives. Specificity, also referred to as true negative rate or user's accuracy, is the proportion of actual negatives, which are classified as negatives. A random stratification procedure was used on the reference datasets to produce ten unique datasets for training (80%) and testing (20%) for each image type and image scene. Accuracy measures were averaged for each classification and the same training and testing datasets were used to train and test the four classifiers under different conditions, thus making it possible to assess the relative performance and consistency of the algorithms for a given task. The conditions for training and testing the classifiers were varied depending on the evaluation criteria which include the impact of feature selection, the impact of number and choice of training instances, the transferability of trained learning machines between different image types and image scenes and the effect of image segmentation on the classification results.  
The following experiments have been carried out for each image subset on the basis of the same segmentation result that has been used to create the according reference dataset. In this way, only the labeling performance of the classifiers is evaluated and not the real map accuracy, which can be considered a product of both the segmentation and classification results. The real map accuracy would include also geometrical accuracy of extracted objects originating from the segmentation stage. The influence of the segmentation on the classification performance is evaluated separately in Subsection 5.5. Since reference labels are based on visual image interpretation, derived accuracy measures give indication on the labeling performance of learning machines relative to the performance of an experienced human operator. In the context of this work, this is desirable as it allows to isolate thematic labeling performance from geometric segmentation performance and information content of the image from real-world objects.
## 4 Formation of Normal or Low Pressure Patterns
### Large Uplift and Small Subsidence Low Pressure Pattern
Based on these calculations, its uplift height is approximately 600 m higher than it is now. In summary, the formation of overpressure may be influenced by factors such as hydrocarbon generation, aquathermal pressurization, and undercompaction, but these factors are not determinative. Instead, overpressure is primarily caused when the rates of uplift and subsidence exceed the permeation rate of formation water. The impact of overpressure on hydrocarbon reservoirs should also be adjusted accordingly. For instance, overpressured compartments can facilitate the accumulation of hydrocarbons, promote hydrocarbon expulsion, episodic hydrocarbon expulsion, episodic charging, and episodic accumulation. These processes can be summarized into the following four models (Table 2 and Figure 6):  
1. The active overpressure model of rapid subsidence is known as the Nanpu Model, as shown in Figure 6a. 2. The passive overpressure model of rapid tectonic uplift is known as the Sichuan Model, as shown in Figure 6c. 3. The long-term static normal pressure model is known as the Songliao Model, as shown in Figure 6b. 4. The model of significant uplift followed by minor subsidence and low pressure is known as the Sulige Model (low pressure), as shown in Figure 6d. Figure 5: Burial history, thermal evolution history, and inclusion trapping time of well Su 16 in the Sulige gas field [47]. These four models can determine the formation pressure state within a unified theoretical framework, eliminating the need to adopt different hypotheses for formation pressure in different situations (tension, compression, uplift, subsidence, and with or without oil and gas involvement) (Table 3). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Models** & **Factor** &
\begin{tabular}{c} **Characteristics of** \\ **Tectonic Movement** \\ \end{tabular} & **Pressure State** & **Examples** \\ \hline
1 & Nanpu Model & Active overpressure & Rapid subsidence & Overpressure & Nanpu Oilfield \\
2 & Sichuan Model & Passive overpressure &
\begin{tabular}{c} \\ denudation \\ \end{tabular} & Overpressure & Kuga Depression \\  & & & & Junggar Basin \\
3 & Songliao Model & Long-term static & Slow settling & Normal pressure &
\begin{tabular}{c} \\ Erlian Basin \\ Baise Basin \\ \end{tabular} \\
4 & Sulige Model & \begin{tabular}{c} Large uplift and \\ small subsidence \\ \end{tabular} & \
\begin{tabular}{c} Large uplift + small \\ settlement \\ \end{tabular} &
\begin{tabular}{c} Abnormally \\ low pressure \\ \end{tabular} & Sulige Gas Field \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Four models of formation pressure conditions. Figure 6: Four models of formation pressure.
## 4 Experiments and Discussion
### Experiment Setup  
Our experiments involve the design of six distinct scenarios, which are categorized as either familiar or unfamiliar. The familiar group comprises four scenarios, as presented in Figure 4. During the training period, UGVs are trained in these environments before evaluating the performance of mapless navigation. Therefore, UGVs are familiar with the scenarios in this group. Conversely, the unfamiliar group (Figure 4(a),b) consists of environmental configurations that the UGVs have never encountered before. This enables the evaluation of the knowledge transfer and generalization capabilities in unknown and unfamiliar environments. As illustrated in Figure 4, each UGV will be initialized at the center of each scenario during the training period, with a randomly assigned target location at one of the corners. The obstacle locations are also randomly generated for each run. Specifically, the positions of the obstacles are uniformly distributed across the map, and their orientations are randomly sampled from the interval \([0,\pi]\) to introduce variability in obstacle configurations. The target location is represented by the white square, the green objects depict the obstacles, and the blue lines indicate the laser beams of each robot.  
For the creation of simulation environments used in training and evaluation, we utilize ROS Melodic and Gazebo 9. The workstation used for these tasks is equipped with an Intel Xeon 3.8 GHz processor and an Nvidia GeForce RTX 3060 GPU. PyTorch is employed for the implementation of neural networks, while the hyper-parameters used in this work are listed in Table 1.  
To evaluate the performance of the approaches, we consider several metrics, namely the success rate, collision rate, timeout rate, and average successful steps. The success rate indicates the percentage of total rounds in which the robot successfully reaches its target location without experiencing any collisions. On the other hand, the collision rate measures the percentage of total rounds where the robot collides with an obstacle. The timeout rate indicates the percentage of total rounds in which the robot cannot reach its target  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Symbol** & **Definition** & **Value** \\ \hline \(\alpha\) & learning rate & 0.0004 \\ \(\gamma\) & discount factor & 0.99 \\ \(\delta\) & adjustment coefficient of the collision & \(-15\) \\ \(\mu\) & adjustment coefficient of the distance reward & 500 \\ \(E\) & number of the local episodes & 10 \\ \(M\) & number of the global update periods & 40 \\ \(\alpha\) & adjustment coefficient of the growth value & 0.5 \\ \(\Omega\) & threshold of the growth value & 0.1 \\ \(\lambda\) & adjustment coefficient of the soft update & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyper-parameters.  
Figure 5: Trained models are evaluated in unfamiliar environments.  
location within a specified time but remains functional. Finally, the average successful steps measure the average path length taken by the robot to reach the target location successfully.
## 3 Results and Discussion
### ADME In Silico Modelling of DIT-SAL  
ADME (absorption, distribution, metabolism, and excretion) properties play a crucial role in determining the pharmacokinetics and bioavailability of a drug, using, e.g., [PERSON]'s rule of five, which states a guideline to assess the drug-likeness of a compound based on its physicochemical properties [72]. The bioavailability radar obtained using SwissADME (Figure 20) is a picture of six physicochemical parameters, such as size, lipophilicity, polarity, solubility, saturation, and flexibility. The middle pink hexagon shows the optimal bioavailability zone for oral administration. Based on these calculations, the molecule of interest, SAL-DIT, has good qualities, as shown in Supplementary Table S1, similar to those of dithranol (Supplementary Table S2), obtained using ADMETLab 2.0.  
Figure 19: Molecular structure of DIT-SAL in dichloromethane optimized with B3 LYP/6-311++G(d,p) using the PCM model.  
For effective permeability, the optimal molecular weight is between 200 and 500 Da. The total molecular weight of the DIT-SAL molecule is 346 Da, which is in the range needed for optimal transport to the skin. The octanol-water partition coefficient (log P) found for DIT-SAL is 4.58, as compared with 3.56 for dithranol. The conjugates with higher log P values than the parent drugs indicate increased lipophilicity, while reduced aqueous solubility is observed. The hydroxyl moiety in dithranol and salicylic acid molecules plays a crucial role in aqueous solubility through hydrogen bonding. Esterification hinders this hydrogen bonding process. This interference in hydrogen bonding is likely responsible for the decreased solubility of prodrugs. Essentially, when esterification occurs, it disrupts the ability of the drug molecules to form hydrogen bonds with water molecules, leading to lower solubility in aqueous environments. This information can be valuable in understanding the physicochemical properties of drugs and their derivatives, which can impact factors like absorption and bioavailability in pharmaceutical applications. The lipophilic nature of the stratum corneum means that it has an affinity for lipid-soluble (lipophilic) substances. Permeants that are more lipophilic tend to partition more easily from the vehicle (the formulation applied to the skin) into the stratum corneum. This process is crucial for enhanced skin absorption because lipophilic substances can penetrate the lipids of the stratum corneum more readily. Once these lipophilic substances have penetrated the stratum corneum, they may further pass through into the underlying layers of the epidermis. This enhanced absorption can lead to the formation of a cutaneous reservoir within the whole skin, allowing for a sustained release of the substance over time. Hydrophilic substances may have difficulty penetrating the skin and may require specific formulation strategies to enhance their absorption.
# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 3 Results  
### Ablation Study  
The results of the evaluation metrics for the ablation study are shown in Table 1, and reconstructed images are shown in Figure 4. We first investigate the impact of the different post-processing layers. Compared to using the U-Net for post-processing, using no post-processing and three convolutional layers performs generally worse, especially for severe artifacts. When using no post-processing, clear streak and extinction artifacts are present. Using the three convolutional layers for post-processing improves SSIM and PSNR and the streak and extinction artifacts disappear. However, the geometry of some soft tissue organs such as the liver is not reconstructed correctly, which is particularly evident for severe metal artifacts. Using the U-Net as the final layers of the network substantially improves the evaluation metrics, completely eliminates artifacts, and reconstructs organs more accurately. For no artifacts, the iCTU-Net underperforms compared to the FBP, especially in terms of PSNR. As shown by the arrows in the zoomed regions in Figure 4, the iCTU-Net is not capable of resolving small structures of only a few millimeters in size. From now on, we will only use the U-Net for post-processing as it yields the best results.  
Finally, we train the iCTU-Net with the MSE loss. For no artifacts and moderate artifacts, the SSIM and PSNR evaluation metrics for the SSIM and MSE losses are similar. However, the SSIM metric for the MSE loss is considerably worse for severe artifacts and the reconstructions of the MSE iCTU-Net in Figure 4 look grainy. Thus, the network with U-net post-processing layers combined with SSIM loss performs best. Only this network configuration is referred to as iCTU-Net in this work.  
Figure 4: Results of the ablation study, where the ground truth and FBP are compared to different iCTU-Net settings. All networks are trained with pre-corrected NMAR sinograms and the SSIM loss, except for the MSE iCTU-Net, which is trained with the MSE loss. A slice without metal artifacts, with moderate metal artifacts, and with severe metal artifacts is shown. The scans are windowed to [\(-300\) HU, \(300\) HU] to increase the visibility of the artifacts. The arrows in the zoomed regions indicate small structures that the iCTU-Net cannot resolve accurately.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 3 NP Properties and Characterization Methods
### Physical Properties and Their Characterization
#### 3.1.3 Atomic Force Microscopy (AFM)  
AFM provides high-resolution topographical images of MNPs by allowing the visualization of surface morphology and size distribution. Moreover, this technique can be employed to map the magnetic properties of nanoparticles across a surface. This includes measurements of magnetic force, coercivity, and remanence, providing insights into the magnetic behavior of the nanoparticles.  
When it comes to functionalizing MNPs, AFM can be engaged to assess the surface modifications involved in the interaction between nanoparticles and various molecules. The method can be used in conjunction with other imaging techniques for a rigorous characterization [100]. It must be mentioned that sample preparation is crucial to ensure accurate and reliable results.