esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Thermal Properties  
The DSC thermograms of the analyzed blends are shown in Figure 7, while the most important results are summarized in Table 1. Concerning PA6, double melting phenomena can be observed at 219.8 and 226.3 \({}^{\circ}\)C, while its \(T_{g}\) can be detected in the proximity of 60 \({}^{\circ}\)C, even though the signal is particularly weak. Considering neat PCL, it exhibits a \(T_{g}\) approximately at \(-60\)\({}^{\circ}\)C and melting phenomena at 60 \({}^{\circ}\)C, which are in perfect agreement with the values found in the literature [61]. It is immediately apparent how, as expected, signal intensity changes as a function of the relative composition of the samples. Specifically, melting and crystallization signals of PA6 experience an intensity loss with an increase in PCL content, while the intensity of the PCL melting and crystallization signals increases with the PCL amount. Moreover, it can be observed that there is no interaction between PA6 and PCL, due to the fact that the thermal properties of both PA6 and PCL phases are not substantially affected by the presence of another phase in the blends. The presence of double melting phenomena in the PA6 phase can be ascribed to its ability to form polymorphic crystals. Two different types of crystals can be formed; the first and most stable is labeled \(\alpha\)-phase (with monoclinic cell), which is formed due to low under-cooling, whilst the second and the less stable is labeled \(\gamma\)-mesophase (with pseudo-hexagonal cell), which can be generated due to high under-cooling [62]. Through a peak deconvolution procedure, it was possible to discriminate the melting temperatures of both the \(\alpha\)-phase (\(T_{m2}\)) and the \(\gamma\)-phase (\(T_{m1}\)). By adding PCL to the PA6 matrix, it can be observed that the generation of double melting phenomena tends to be enhanced, and the addition of PCL hinders the formation of more stable PA6 \(\alpha\)-crystallites. However, both the melting and crystallization temperature of PA6 do not have significant variations with the PCL amount in the blends. It is extremely interesting to notice the change in the melting temperature of PCL between the first and second heating scans, as the second scan \(T_{m}\) is significantly lower than that observed in the first heating scan. This experimental evidence can be attributed to the fact that  
Figure 6: ATR\(-\)FTIR spectra of the prepared PA6/PCL blends. (**a**) Full spectra, (**b**) spectra in the region 3500–2700 cm\({}^{-1}\), and (**c**) spectra in the region 1800–650 cm\({}^{-1}\). the blend was kept in a controlled atmosphere before testing, and PCL was thus able to reach a high degree of crystallinity, with a consequent increase in the melting temperature. During the DSC analysis, PCL did not have enough time to reach the same crystallinity degree, with a decrease in melting temperature in the second heating scan [63]. In any case, the immiscibility of the produced blends was demonstrated also through DSC analysis.
# Impact of Evaluation of Freeze-Thaw Cycles on Collapse Zone at Entrance and Exit of Loess Tunnel
## 5 Results  
The entrance and exit of a loess tunnel are easily affected by freezing and thawing, which leads to collapse in cold regions. According to the results of freeze-thaw tests and the monitoring data on a loess tunnel, the computational parameters are listed in Table 2.  
On this basis, our program computes the area of the collapse zone and the variation of the bearing capacity (P) for the entrance and exit section of a loess tunnel under three different working conditions of Case 1-1, Case 2-1, Case 3-1, and corresponding to the suffering from the freeze-thaw cycles under three different working conditions of Case 1-2, Case 2-2 and Case 3-2. The calculation results are given in Table 2.  
Comparative analysis of the bearing capacity (P) of the loess tunnel before and after freezing and thawing is shown in Figure 15. The bearing capacity (P) of the loess tunnel was degraded by 49.13%, 40.66%, and 37.56% in Case 1-2, Case 2-2, Case 3-2, respectively. The results reveal the significant degradation effect of freeze-thaw cycles on the bearing capacity (P) of loess tunnel. With the increase in water content of the surrounding rock, the bearing capacity (P) of the section of loess tunnel decreases. This is mainly the soil mass in the entrance and exit section that witnesses a continued decline in shear strength of loess suffered from the freeze-thaw cycles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Loess**} & \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Collapse Zone Parameters**} \\  & & & & **Bearing** \\ \(\rho_{\text{d}}\)**/(g/cm\({}^{3}\))** & & & & **Capacity** \\  & & & **(P)/kN** & \\ \hline Case 1-1 & & & Undisturbed loess tunnel & 2599.45 & 12.52 \\ Case 1-2 & & & After freeze–thaw cycles & 1534.97 & 13.66 \\ Case 2-1 & & & Undisturbed loess tunnel & 1893.07 & 12.67 \\ Case 2-2 & & & After freeze–thaw cycles & 1123.37 & 14.00 \\ Case 3-1 & & & Undisturbed loess tunnel & 1104.46 & 12.88 \\ Case 3-2 & & & After freeze–thaw cycles & 561.74 & 14.95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational parameters and results.  
Figure 14: Trend of internal friction angle after freeze–thaw cycles.  
According to the data of Case 3-1 and Case 3-2, in order to vividly reveal the change of the collapse area in the loess tunnel area, we have drawn the schematic diagram of the collapse area before and after freezing and thawing in Figure 16. Comparative analysis of the collapse zone area at the entrance and exit section of the loess tunnel before and after freezing and thawing are shown in Figure 16a,b, and the excess area (Figure 16c) of the collapse zone could be achieved by a subtraction between the collapse zone area Case 3-1 and the collapse zone area Case 3-2. It is worth noting that the ratio of excess area is up to 16.13% in Case 3 during the freeze-thaw cycles. As a result, the effect of the freeze-thaw cycle will significantly increase the area of the collapse zone of the loess tunnel.
# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 3 Sub-GHz Passive Localization
### Constructing a Single Fingerprinting Database Based on All Training Data
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & \\ \hline \multirow{3}{*}{**\begin{tabular}{} \end{tabular}**} & \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & \multicolumn{1}{c}{**868 MHz**} & & & & & & & & & & & & & & \\ \cline{2-13}  & **\# of Nodes** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline \multirow{3}{*}{**
\begin{tabular}{} \end{tabular}**} & Participant 1 & 0.16 & 0.29 & 0.50 & 0.69 & 0.81 & 0.91 & 0.97 & 0.99 & 0.99 & 1 & 1 & 1 & 1 & 1 & 1 \\ \cline{2-13}  & Participant 2 & 0.19 & 0.40 & 0.65 & 0.85 & 0.95 & 0.98 & 0.99 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \cline{2-13}  & Participant 3 & 0.19 & 0.41 & 0.67 & 0.83 & 0.93 & 0.97 & 0.99 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \cline{2-13}  & Participant 4 & 0.18 & 0.39 & 0.64 & 0.81 & 0.92 & 0.98 & 0.99 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \cline{2-13}  & Participant 5 & 0.17 & 0.33 & 0.54 & 0.70 & 0.85 & 0.91 & 0.96 & 0.98 & 0.99 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \cline{2-13}  & Participant 6 & 0.16 & 0.32 & 0.51 & 0.69 & 0.83 & 0.91 & 0.95 & 0.98 & 0.99 & 0.99 & 1 & 1 & 1 & 1 & 1 \\ \cline{2-13}  & All Participants & 0.17 & 0.36 & 0.59 & 0.76 & 0.88 & 0.94 & 0.97 & 0.99 & 0.99 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Median accuracy rates obtained by using a naive Bayesian classifier on data from the 868 MHz network. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & \\ \hline \multirow{3}{*}{** \begin{tabular}{} \end{tabular} **} & \multirow{3}{*}{\ \begin{tabular}{} \end{tabular}  } & \multicolumn{1}{c}{**433 MHz**} & & & & & & & & & & & & & & & \\ \cline{2-13}  & **\# of Nodes** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline \multirow{3}{*}{** \begin{tabular}{} \end{tabular} **} & Participant 1 & 0.15 & 0.32 & 0.52 & 0.74 & 0.82 & 0.93 & 0.96 & 0.99 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \cline{2-13}  & Participant 2 & 0.21 & 0.41 & 0.70 & 0.84 & 0.94 & 0.98 & 0.99 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \cline{2-13}  & Participant 3 & 0.21 & 0.43  
differences more minor. Additionally, it can also be seen quite clearly in the tables that participants 2, 3, and 4 require fewer nodes to obtain a perfect median accuracy rate of 1. Potential reasons for these differences could lie in the fact that no training measurements for participants 5 and 6 were used in the construction of the fingerprint database. Additionally, participants 1 and 5 respectively have the lowest and second lowest height and weight out of all the participants. This is a clear indication that different individuals can have different impacts on a purely RSS based DFL system.
# Modelling Pollutant Dispersion in Urban Canyons to Enhance Air Quality and Urban Planning
## 4 Discussion
### Limitations  
In this study, the complex dynamics of the street canyon are reduced to a bidimensional model. This introduces inaccuracies when the results are applied in real-world applications due to the chaotic nature of airflow in three-dimensional environments because of the many factors altering the ideal case, for instance, trees, vehicles, and balconies. This complexity leads to corner recirculations and unexpected airflow patterns, which can significantly affect air movement within or around the street. Despite these limitations, the use of bidimensional simplification has been tested by some authors, concluding that its use is acceptable for elongated streets, as evidenced by minimal errors in the results [2; 20]. Consequently, this method is appropriate in scientific research where valuable illustrative results are sought. Furthermore, this simplification adds another uncertainty which is the effect of crosswinds, because the wind profile is assumed to be perpendicular to the street. However, this case has proved to be the one with minimum street ventilation and therefore minimum pollution dispersion, so it is the one tested in most of the studies in this field.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 2 Background
### Process Logs and Models
#### 2.5.2 Workflow Net (WF-Net)  
A workflow net (WF-net) is a specialized form of Petri net tailored specifically for representing workflow processes. Workflow nets have a defined start place (source) and end place (sink), ensuring that every process has a clearly defined beginning and end. A key property of WF-nets is their ability to represent complex business processes that follow a structured and often hierarchical flow, making them useful for business process analysis and improvement.  
**Definition 5**.: _Formally, a WF-net is a Petri net \(WF=(P,T,F)\) with the following additional properties:_  
* _There is a single source place_ \(i\in P\) _such that for any transition_ \(t\in T\) _there is a directed path from_ \(i\) _to_ \(t\)_._
* _There is a single sink place_ \(o\in P\) _such that for any transition_ \(t\in T\) _there is a directed path from_ \(t\) _to_ \(o\)_._
* _Every node (place or transition) is on a path from_ \(i\) _to_ \(o\)_._
## 3 Application of Proposed Method on Nanchang Subway Line 3
### Comparison between Analytical and Monitoring Results  
Figure 9a shows the longitudinal profiles of the ground surface deformation induced by different force patterns during the shield tunnelling. The deformation induced by the torsional force is not drawn because the value is minimal at \(x=0\). A superposition of all the deformation components is shown in Figure 9b as well as the monitoring data of the ground surface. The analytical deformation develops rapidly near the tunnel face, then eventually increases to a stable value of 9.11 mm far away from the tunnel face. The maximum monitoring data of the ground surface is about 8.0 mm, which is 12% less than the analytical results proposed by our method.  
Figure 7: Monitoring section I-I.  
Moreover, Figure 10a illustrates the longitudinal profiles of the ground deformation at the pipeline's elevation (\(z_{p}\) = 4 m) induced by the ground volume loss, support pressure, and frictional force. According to the subgrade reaction analysis in Section 2.3, the pipeline deformation is calculated and drawn in Figure 10b, as well as the monitoring data of the pipeline. The maximum value proposed by our method is 9.6 mm, 11.5% more than the maximum value of the monitoring data. Based on Figures 9 and 10, the analytical results agree well with the monitoring data, confirming that our proposed method is applicable.  
Figure 8: Ground deformations induced by different forces: (**a**) Displacement induced by ground volume loss; (**b**) displacement induced by support pressure; (**c**) displacement induced by frictional force; (**d**) displacement induced by torsional force.  
Figure 9: Longitudinal profiles of ground surface deformation during shield tunnelling: (**a**) surface deformation during tunnelling; (**b**) analytical results and monitoring data.  
Figure 11 shows the pipeline deformation and monitoring data with shield tunnel advance. The shield tunnel is excavated ahead from y = \(-\)20 m to \(-\)10 m and to 0 m. The longitudinal profiles of the pipeline deformation are similar and move forward as a whole. The predicted deformation before the tunnel face matches well with the monitoring data. In contrast, the deformation is larger than the stable value of the monitoring data after the tunnel face. The differences between the analytical results and the monitoring data mainly come from two aspects: (a) the soil in the multiple layers has different types instead of a uniform and elastic medium. (b) the grouting implementation enhances the soil properties and reduces the pipeline deformation. Although the analytical results diverge from the monitoring data, the proposed method gives a relatively feasible estimation of the pipeline deformation considering the tunnel advancing.
## 3 Proposed Scheme: MC-TIRP
### Trust Route Update and Maintenance Component  
Path update and maintenance is the process of deciding how to update or maintain routes if network status changes (e.g., data usage, link status, or the event of link disconnection). If the maintenance process identifies nodes that cannot function, it transmits an error message to the source node. If the source and intermediate nodes have active multipaths, they are updated to alternate paths, and transmission takes place. If there is no trust path, the source node can execute a route discovery routine to discover a new trust route. The path maintenance process validates the trusted path at certain time intervals. When a trust route cache entry exceeds its validity time, a new trust route retrieval process is started. Figure 2 shows a comprehensive flowchart of the trust-based intelligent routing protocol scheme. Each sensor node calculates _EQTV_ using flexible weights. To learn a reliable path, the trust values of nodes and paths are periodically checked and updated using a hello message with _EQTV_. After that, if the Epsilon value is above a certain level, we check the importance of the mission data. The reason for choosing the Epsilon criterion is that selecting random actions too often can degrade network performance. If the data to be transmitted has high mission criticality, the max-Q value path is selected; otherwise, a random path is selected. Finally, sensor nodes update the local trust value in the routing table and learn as a reward.  
The gateway node updates the PTV and end-to-end (E2E) delay of the paths and receives a global reward when the mission-critical requirements are satisfied. Finally, the gateway node updates the global trust value in the routing table and learns as a reward. As a result, the Q table of the path that satisfies the requirements is learned to enable efficient path management.  
Figure 1: Example of data transmission based on mission-critical data priority.
## 1 Introduction
A lightweight network structure based on the RIMformer architecture is proposed to perform real-time interference suppression tasks for reliable target detection. By incorporating self-attention mechanism with linear complexity and depthwise separable convolution into the baseline model, the computational complexity and memory overhead are reduced while preserving the interference mitigation performance. 2. A two-stage KD approach is used to facilitate the training of the compressed model. The knowledge based on both the feature and the response from the teacher model is transferred to the student model. The incorporation of a staged training methodology and dynamic KD weight adjustments effectively improves the performance of the student model. Through a comprehensive analysis of time consumption and resource usage, the performance of the proposed method is thoroughly evaluated on both simulated and measured interference mitigation tasks, which can provide a valuable reference for real-world applications.
## 4 Positioning Performance of GPS/QZSS and BDS/QZSS in the Asia-Pacific Region
### ADOP, Success Rate and Baseline Accuracy
In the next section, therefore, another indicator called the actual success rate will be used to mainly evaluate the correctness of ambiguity resolution. The actual success rate (Asrt) is defined as [5]  
\[Asrt=\frac{NOCE}{NOTE}\times 100\% \tag{14}\]  
where \(NOCE\) is the number of correct fixed-ambiguity epochs and \(NOTE\) is the number of total epochs. It is decided that the fixed integer ambiguities are correct and successful if the east, north, and up deviations of the baseline calculated by the optimal ambiguities are not more than 4 cm, 4 cm, and 10 cm, respectively.
## 4 Methodology
### Calibration of the AR Environment
#### 4.4.2 Visual Drone Pose Estimation  
Instead of relying on user information (air-taps) a drone's relative pose (position and orientation) may be inferred from visual data, captured live from the HoloLens's onboard camera. To that end, we propose a method that exploits the latest advances in deep learning to automatically retrieve the 6 DoF pose of a drone from a single image. An early version of this architecture, described in more detail in [23], uses a CNN encoder backbone followed by three fully connected layers that output two predictions, one for the translation components and one for the rotation. The translation prediction uses an \(L_{2}\) loss, while the rotation prediction aims to minimize the angular difference \(L_{R}\). The two loss functions are weighted by a balancing hyperparameter \(\lambda\) and combined to form the training loss function.  
The newest update employs a state-of-the-art architecture, HRNet [38], as a landmark regression network to predict the eight landmark image positions. Then, we retrieve the pose using the predicted 2D-3D correspondences. However, this approach does not permit end-to-end training, as the pose retrieval step is not differentiable. Towards this end, we exploited the recently introduced BPnP algorithm [39], which has been proved to be effective for the pose estimation task [40].  
For training our models and evaluating their performance, we have compiled and made publicly available the UAVA dataset (also in [23]), an extensive set of synthetic, photorealistic images of drones in indoor and outdoor environments, which provides ground truth annotations of 2D keypoints, pose, depth maps, normal maps, and more.  
Accurate visual drone pose estimation can automate and simplify the calibration process, as both distance (step 1) and orientation (step 2) can be estimated by a single image, automatically, without user input. Visual recognition of the drone and its pose by HoloLens is robust at distances of a maximum of 5-10 m, depending on drone model and size. In addition, this method can be used to periodically correct the position computed by the aggregated IMU readings, offsetting IMU drift and any other errors. With the current version specifications of HoloLens, such an algorithm cannot run continuously in real time. However, even sparse inferences, performed every 5-15 s, can keep IMU drift at a minimum and improve tracking.
## 4 Conclusions  
Laser ablation in liquid is a powerful and efficient technique to synthesize TiO\({}_{2}\) NPs with a spherical shape and various sizes suspended in deionized distilled water. UV-Vis characterizations of the prepared solutions confirmed the purity of TiO\({}_{2}\) NPs and show absorption spectra below 360 nm. FTIR and XRD results confirm the formation of metal oxide NPs. The particles have a spherical shape and mild aggregation was noticed. With the activity of TiO\({}_{2}\) NPs against the bacterial strains, the effect of NPs was found to increase with NP concentrations due to the smaller particle sizes of NPs and the best effect was found against _E. coli._ TiO\({}_{2}\) NPs and amoxicillin have a synergic effect on the bacterial cells and the nanoparticles can efficiently improve the permeation and uptake of amoxicillin into the bacterial cells. TiO\({}_{2}\) NPs showed an inhibition zone of 11.6 \(\pm\) 0.57 mm against _E. coli_, while 13.3 \(\pm\) 0.57 mm was observed against _S. aureus_. Further study is needed to find out the exact reason for the enhancement of the activity of amoxicillin in the presence of TiO\({}_{2}\) nanoparticles.  
Conceptualization and methodology, K.S.K., G.M.S. and F.A.A.; formal analysis, S.A., M.A.I. and T.A.-M.; investigation and data curation, K.S.K., G.M.S., F.A.A. and A.A.A.; validation, M.A.I, T.A.-M. and A.A.A.; visualization, all authors; writing--original draft preparation, K.S.K., G.M.S., F.A.A. and S.A.; writing--review and editing, K.S.K., G.M.S., S.A. and M.A.I.; supervision, K.S.K. and G.M.S.; project administration, K.S.K., G.M.S. and T.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This study did not report any data.  
The authors extend their appreciation to the \"Research Center of the Female Scientific and Medical Colleges\", Deanship of Scientific Research, King Saud University, for supporting this project. Additionally, the authors extend their appreciation to the University of Technology and the University of Misan for their technical support.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Organisms** & **TiO\({}_{2}\) NPs Along with Amoxicillin** & **IZ (mm) Mean \(\pm\) S.D.** \\ \hline \multirow{4}{*}{_E. coli_} & Control (amoxicillin) & 8.0 \(\pm\) 0.57 \\  & 400 \(\mu\)g mL\({}^{-1}\) & 10.3 \(\pm\) 0.57 \\  & 600 \(\mu\)g mL\({}^{-1}\) & 11.3 \(\pm\) 0.57 \\  & 1000 \(\mu\)g mL\({}^{-1}\) & 11.6 \(\pm\) 0.57 \\ \hline \multirow{4}{*}{_S. aureus_} & Control (amoxicillin) & 9.3 \(\pm\) 1.15 \\  & 400 \(\mu\)g mL\({}^{-1}\) & 12.3 \(\pm\) 0.57 \\ \cline{1-1}  & 600 \(\mu\)g mL\({}^{-1}\) & 11.6 \(\pm\) 1.15 \\ \cline{1-1}  & 1000 \(\mu\)g mL\({}^{-1}\) & 13.3 \(\pm\) 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inhibition zone in mm (mean \(\pm\) standard deviation) of TiO\({}_{2}\) NPs with amoxicillin.