esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Based Airway Segmentation Using Key Point Prediction
###### Abstract  
The purpose of this study was to investigate the accuracy of the airway volume measurement by a Regression Neural Network-based deep-learning model. A set of manually outlined airway data was set to build the algorithm for fully automatic segmentation of a deep learning process. Manual landmarks of the airway were determined by one examiner using a mid-sagittal plane of cone-beam computed tomography (CBCT) images of 315 patients. Clinical dataset-based training with data augmentation was conducted. Based on the annotated landmarks, the airway passage was measured and segmented. The accuracy of our model was confirmed by measuring the following between the examiner and the program: (1) a difference in volume of nasopharynx, oropharynx, and hypopharynx, and (2) the Euclidean distance. For the agreement analysis, 61 samples were extracted and compared. The correlation test showed a range of good to excellent reliability. A difference between volumes were analyzed using regression analysis. The slope of the two measurements was close to 1 and showed a linear regression correlation (\(t^{2}=0.975\), slope \(=1.02\), \(p<0.001\)). These results indicate that fully automatic segmentation of the airway is possible by training via deep learning of artificial intelligence. Additionally, a high correlation between manual data and deep learning data was estimated.  
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inefficient and time-consuming procedure [6]. In particular, the evaluation of the airway is difficult to analyze due to its anatomical complexity and the limited difference in gray scale between soft tissue and air [7; 8; 9]. Airway analysis is essential for diagnosis and assessment of the treatment progress of obstructive sleep apnea patients and for predicting the tendency of airway changes after orthognathic surgery [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21].  
In most previous studies, the airway was segmented semi-automatically using software systems for volumetric measurements using cone-beam computed tomography (CBCT) images [21; 22; 23]. These studies evaluated the reliability and reproducibility of the software systems on the measurement of the airway [7; 24; 25; 26; 27] and compared the accuracy between the various software systems [9; 24; 25; 27]. However, in all cases, the software systems require manual processing by experts.  
In this study, a regression neural network-based deep-learning model is proposed, which will enable fully automatic segmentation of airways using CBCT. The differences between the manually measured data and data measured by deep learning will be analyzed. Using a manually positioned data set, training and deep learning will be performed to determine the possibility of a fully automatic segmentation of the airway and to introduce a method and its proposed future use.
# Design of a Predictive RBF Compensation Fuzzy PID Controller for 3D Laser Scanning System
## 5 Simulation
### Robustness Simulation  
In practice, mismatch could occur between the established system model and the actual process, and the system may be affected by some unknown disturbance. In order to verify whether PRFPID can improve the robustness of the system, simulation analysis is carried out on the three controllers in the case of model mismatch and large disturbance.  
In order to simulate the model mismatch in the actual process, the Monte Carlo method is used to randomly generate four kinds of mismatch models for the parameters that may have deviations in the system mathematical model. The maximum uncertainty is 30%. As shown in Table 9, the three parameters are gravity of 2D Lidar G, the distance from the center of Lidar to the center of rotation r and the moment of inertia of Lidar around the system rotation center J.  
Figure 10 shows the response curves of the four models under mismatch conditions. It can be seen that the PRFPID controller can still achieve favorable control results in the case of four random model mismatches. Compared with RFPID and FPID, it can track input settings more quickly. When the output curves of RFPID and FPID are not stable, the output curve of PRFPID is stable and does not have large oscillations or fluctuations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Response Time (s)** & **Overshoot (\%)** & **Steady-State Error (\({}^{\circ}\))** & **Setting Time (s)** \\ \hline
**FPID** & 15.298 & 0.38\% & 0.19 & 17.658 \\
**GFPID** & 2.03 & 0 & 0.3 & 5.171 \\
**PGFPID** & 1.957 & 0.14\% & 0.07 & 3.146 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Response index of control.  
Figure 9: (**a**) FPID without friction; (**b**) step responses; (**c**) step response amplification; (**d**) steady-state error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **G (N)** & **r (m)** & **J (kg*m2)** \\ \hline
**1** & 13.0772 & 0.044 & 0.0085 \\
**2** & 13.6782 & 0.053 & 0.0053 \\
**3** & 8.5381 & 0.0596 & 0.0066 \\
**4** & 11.8736 & 0.0739 & 0.0048 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Mismatch model parameters.  
In order to simulate the response curves of the three controllers under the effect of large disturbance, a step disturbance of \(-40\) degrees is added to the system output at 25 s. The flexibility coefficient \(\mu\) is changed to 0.8 to enhance the anti-interference ability of PRFPID. Figure 11 shows the comparison results of the three controllers under the step disturbance. PRFPID can quickly return to the target setting value while RFPID and FPID return slowly. Therefore, PRFPID has strong anti-interference ability and can deal with the case of model mismatch properly.
# Quantum Key Distribution in Kubernetes Clusters
## 6 Test and Validation
### Routing  
The routing protocol with the underlying algorithm, described in Section 3.3, has been tested as a separate component. This strategy allows us to evaluate the routing protocol in large QKD networks. In order to test its behaviour, we used the virtual machine described at the beginning of Section 6. We did not deploy whole QSSs in this case, but we ran only  
Figure 10: Execution time of the getKey and getKeyWithId functions for Point-to-Point and Multi-hop exchanges.  
Figure 9: Success rate of the getKey requests to the QKS varying the underlying exchange rate.  
the routing module inside a Docker container. Therefore, the QKD network becomes a set of Docker containers running on a specific host and communicating over a virtual TCP/IP network. We simulated a scenario in which events such as SAEs and QKDMs registrations and removals were randomly triggered every 10 s. These events continuously initiated the update process to send LSA packets over the network and update neighbours. The first results proved that in our module, each node sees the same network topology for every topology represented by a connected graph and can reach every other network node.  
Moreover, Figure 11 shows the convergence time of the algorithm depending on the number of nodes within the network. We tested it for a network of up to 50 nodes where this time is close to 50 ms. We performed the test on a virtual network, suggesting that in a real scenario, with physical links, the convergence time will grow according to the delay introduced by the network to transmit the packets. In that case, it is essential to update the route invalidation parameter to allow convergence. The computational complexity, according to [PERSON], is \(\mathcal{O}(|E|+|V|\log|V|)\) where \(|E|\) is the number of links and \(|V|\) is the number of nodes. This may affect the future large QKD networks and require support for different autonomous systems [23].
## Data and Methods
### MODIS Data Products and Calculation of VIs  
In this study, we used the MODIS Terra MOD09A1 (Version 6) product from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) (Global Subsets Tool: MODIS/VIIRS Land Products: [[https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl](https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl)]([https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl](https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl))). This product provides surface spectral reflectance of Terra MODIS bands 1-7 corrected for atmospheric conditions (e.g., gases, aerosols, and Rayleigh scattering) with 8-day intervals and 500 m spatial resolution. We also used the MODIS/Terra and Aqua nadir BRDF-corrected reflectance product (MCD43A4; Version 6). The MCD43A4 provides 500 m BRDF-adjusted reflectance data of bands 1-7 from MODIS on board both Terra and Aqua satellites with daily time step. These BRDF-adjusted reflectance values are modeled as if they were acquired from the nadir view using the BRDF. Besides the MODIS surface reflectance products, we also used the MODIS GPP product (MOD17A2H; Version 6) [38] to help evaluate the performance of the VIs for estimating GPP. This product consists of 8-day GPP maps with 500 m resolution estimated using MODIS data, meteorological data, and a light use efficiency algorithm.  
For each of the 121 flux towers, surface reflectance values for both MOD09A1 and MCD43A4 products were extracted for the 500-m grid cells within the 1 km \(\times\) 1 km window surrounding the tower for each time step (8-day for MOD09A1 and daily for MCD43A4) over the period 2000-2014. For each site and each time step, the surface MOD09A1 reflectance values averaged within the window were used to calculate NDVI, EVI, and EVI2. For each site and each time step, the BRDF-adjusted reflectance values (MCD43A4) averaged within the window were also used to calculate NDVI, EVI, EVI2, and NIR\({}_{\text{V}}\). The VIs based on reflectance without BRDF correction (MOD09A1) were referred to as NDVI, EVI, EVI2, and NIR\({}_{\text{V}}\), respectively, while the VIs based on BRDF-adjusted reflectance values (MCD43A4) were referred to as NDVI\({}_{\text{BRDF}}\), EVI\({}_{\text{BRDF}}\), EVI\({}_{\text{2 BRDF}}\), and NIR\({}_{\text{V, BRDF}}\) respectively. The BRDF-adjusted reflectance values were also used to calculate NIR\({}_{\text{V}}\). For each site and each 8-daytime step, we also calculated the average of the MODIS GPP values within the 1 km \(\times\) 1 km window surrounding the tower.
## 4 Verification of the Model
### Visualization Tool
#### 4.2.1 Classifying Exceptional Rule and Detecting Anomaly  
Based on the rule comment, the verification data were classified into 79 general rules, 160 exceptional allowance rules, and 569 exceptional denial rules. There were too many exceptional rules especially exceptional denial rules. After checking the results, most of the exceptional denial rules had policies to block harmful IPs and policies to block access to sites not related to work. Most of the exceptional allowance rules were permissive rules for service administrators, temporary permissive rules for vulnerability checks, or permissive rules for updates or patches.  
As a result of the analysis of verification data before classifying exceptional rules based on rule comment, a total of 53,404 anomalies were detected, and for each type, 251 cases of shadowing, 51,658 cases of correlation, 221 cases of generalization, and 1274 cases of redundancy were detected. The results of anomaly detection before and after considering the exceptional rules are summarized in Table 11.  
As a result of the analysis of verification data after classifying exception rules, a total of 11 anomalies were detected, and there was a reduction of 99.97% compared to before the classification of exceptional rules. As for the types of anomalies detected, 11 cases were detected only in redundancy, and no other types of anomalies were detected. Through this result, among the verification data analysis results before and after the classification of exceptional rules, the result after the classification of exceptional rules is better, and there are numerous exceptional rules in the firewall policy. Since the only type of anomaly detected is redundancy, there are not many problems that are difficult to solve in the verification data, and the firewall policy is managed relatively well.  
We provide a radar chart showing the anomaly detection results before and after considering exceptional rules. However, the number of cases of correlation anomaly is extremely high, and the radar chart is not able to recognize problems. As shown in Figure 3, the visibility of the radar chart was increased by taking the logarithmic value of the anomaly detection results. In addition, by checking the anomaly detection results before and after considering the exceptional rules, information security managers can perform what-if analysis and increase work efficiency.
# Free Space Detection Algorithm Using Object Tracking for Autonomous Vehicles
###### Abstract  
In this paper, we propose a new free space detection algorithm for autonomous vehicle driving. Previous free space detection algorithms often use only the location information of every frame, without information on the speed of the obstacle. In this case, there is a possibility of creating an inefficient path because the behavior of the obstacle cannot be predicted. In order to compensate for the shortcomings of the previous algorithm, the proposed algorithm uses the speed information of the obstacle. Through object tracking, the dynamic behavior of obstacles around the vehicle is identified and predicted, and free space is detected based on this. In the free space, it is possible to classify an area in which driving is possible and an area in which it is not possible, and a route is created according to the classification result. By comparing and evaluating the path generated by the previous algorithm and the path generated by the proposed algorithm, it is confirmed that the proposed algorithm is more efficient in generating the vehicle driving path.  
autonomous vehicle; free space detection; object tracking; LiDAR sensor 2022222315
## 3 Field Trialed System
### Cyber and Network Security
#### 3.3.2 Resilience through a Satellite Link  
The network can recover from detected threats and failures in different ways, for example, by replacing compromised components with trusted ones. We also studied recovery with alternative connections, particularly using alternative private networks, commercial networks and a satellite link.  
A satellite link provides a backup backhaul channel that may be utilized when commercial backhaul is unavailable. Our satellite solution included a GEO satellite, a connection that was arranged by Dawson Ka-Sat SC-Zero 70K nomadic terminal from Viasat. This satellite terminal is capable of providing broadband connectivity. Transmission was performed using the Ka-band. The satellite terminal was then connected to the Goodmill router. The router is a multi-channel solution that allows us to monitor the networks and change the wireless transmission technology based on the QoS or other rules employed at the router. It is also possible to direct specific traffic flows to certain tunnels and set the desired backhaul for them. This approach can add an extra layer of protection for security-critical endpoints (such as a UE) by directing sensitive traffic through the most trustworthy route. Thus, when security incidents are detected in the commercial network, based on the specific traffic rules the Goodmill router can switch connectivity to use the secured satellite backup link.  
Apart from the backup satellite link, the utilization of satellite systems can be beneficial in terms of traffic balancing. The terrestrial network could be used to stream the video and the satellite network could be used to broadcast situational awareness data.
## 2 Materials and Methods
### Experiment Design, Monitoring, and Control  
PF200 was run daily during the periods of CWFP operation, except for maintenance stopovers. The study was divided into two phases.  
In the first phase, operational parameters were optimized--specifically, the sludge drainage rate and filter backwash duration. A series of randomized experiments was conducted, with systematic adjustments to these parameters. Each setting was applied for a fixed duration: either a full clarifier operation day or a complete filter cycle. Randomization minimized bias due to the order of experiments, and performance was continuously monitored to identify optimal operating conditions.  
In the second phase, performance data were collected and compared to the benchmark full-scale treatment system. Turbidity served as the primary performance metric, offering a general indication of process efficiency. As coagulation works by destabilizing and aggregating dispersed particles, turbidity removal is widely accepted as a reliable proxy for overall treatment effectiveness [4, 47].  
Data collection occurred from April to December, with the goal of capturing seasonal variation in raw water quality. Maintenance and unexpected shutdowns limited data availability, resulting in approximately five months of usable data. The dataset was post-processed in Python (version 3.7.6), beginning with the removal of downtime and malfunction periods. The remaining data were smoothed using a Savitzky-Golay filter (20-point window, 3 rd-degree polynomial, equal weights) to reduce instrument noise. The final results are presented using percentile plots to illustrate performance trends over time.
# A Multi-Strategy ALNS for the VRP with Flexible Time Windows and Delivery Locations
## 5 Results and Discussion
### Algorithm Analysis
#### 5.2.1 Comparisons with Gurobi  
Gurobi has a large limitation on the scale of the problem to be solved. To benchmark MSALNS against exact solutions, we compared it with the Gurobi solver on smaller problem instances, as Gurobi's runtime becomes prohibitive on the full datasets. These smaller instances, reported in Table 2, were derived from two of the larger base datasets (C16L62 and C21L80). Specifically, for each base dataset, three smaller test cases are created by selecting only the first 5, first 10, and first 15 customers, respectively, along with the depot and all associated optional delivery locations for the selected customers. For example, the instance labeled \"C16L62-10\" in Table 2 consists of the depot and the first 10 customers listed in the original C16L62 dataset.  
The performance comparison with Gurobi's solution results for these examples is shown in Table 2. In this table, the first column represents the different instances; the second column represents the number of optional locations; the third column, _Best_, represents the best solution cost found by MSALNS; _Avg_. represents the average solution cost found by MSALNS; _Gap_ represents the difference between _Best_ and _Avg_.; and _Time_ represents the fastest running time of MSALNS. In the fourth column, _Best_ represents the exact solution cost found by Gurobi, and _Time_ represents the time taken by Gurobi to find the solution. The formula for calculating \(Gap_{best}(\%)\) is as follows:  
\[Gap_{best}(\%)=(c_{MSALNS}-c_{Gurobi})/c_{MSALNS} \tag{18}\]  
where \(c_{MSALNS}\) represents the cost of the best solution obtained by MSALNS and \(c_{Gurobi}\) represents the cost of the best solution obtained by Gurobi.  
The results show that the proposed MSALNS can obtain a solution that is close to the exact solution of Gurobi, and, at the same time, the solution time has been significantly improved. Moreover, this proves the correctness of our proposed VRP-CS model. However, considering that the optional locations increase the complexity of the problem, when the number of customers increases to 16, the Gurobi solver cannot solve these problems on the device.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Instance** & **Locs** & \multicolumn{3}{c}{**MSALNS**} & \multicolumn{3}{c}{**Gurobi**} \\ \cline{3-8}  & & **Best** & **Avg.** & **Gap\({}_{best}(\%)\)** & **Time (s)** & **Best** & **Time (s)** \\ \hline C16L62-5 & 20 & 1394.70 & 1442.90 & **3.08\%** & 1.67 & 1351.69 & 28.67 \\ C16L62-10 & 43 & 1870.49 & 2078.07 & **2.35\%** & 3.98 & 1826.51 & 2414.4 \\ C16L62-15 & 62 & 2258.03 & 2407.51 & - & 6.06 & - & - \\ C21L80-5 & 18 & 1237.61 & 1331.66 & **3.13\%** & 1.89 & 1198.82 & 66.38 \\ C21L80-10 & 38 & 1785.97 & 2077.61 & **2.68\%** & 3.78 & 1738.19 & 2236.15 \\ C21L80-15 & 60 & 3194.04 & 3386.11 & - & 5.92 & - & - \\ Average & - & 1958.92 & 2131.72 & **2.81\%** & 3.88 & 1528.80 & 1186.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between MSALNS and Gurobi.
## 3 AI/ML in the Standards and Industry
### Data Analytics and AI/ML in 3 GPP  
Mobile network operators (MNOs) have traditionally relied on subscriber data collection (location, data rate, call drops, etc.) for network dimensioning and planning. The real-time monitoring of network failures has also been part of the operator's investment model, as it can improve anomaly detection and trigger (proactive) maintenance at reduced costs. With the advent of smartphones, we witnessed, for the first time, truly diversified services (data and voice), which created much more complicated data traffic patterns and made it imperative to adopt data analytics for network management and optimisation. Nevertheless, data analytics for enhancing the network performance have been so far diagnostic and mostly descriptive or, simply measuring various KPIs at several locations, which is suboptimal [60].  
In 5G networks and beyond (B5G), not only are the offered services heterogeneous, but so are the end-devices. For instance, low-power Internet-of-Things (IoT) have very different communication requirements and hardware constraints than do autonomous vehicles. This makes E2E optimisation and the optimal allocation of network slices an arduous task with the added complexity that must be solved in real time and, thus, strengthening the need for big data analytics. Proactivity over reactivity would greatly benefit MNOs over various events, such as potential dynamic and sharp load changes (e.g., group mobility), upcoming outage events because of high interference in certain areas, network faults and maintenance. This amplifies the need to move from diagnostic and descriptive analytics to predictive analytics with an associated confidence level [61] and prescriptive data analytics with suggestions and antecedent countermeasures to maintain the required QoE levels [60]. 5G networks are designed with extended network function virtualisation (NFV) and MEC that allow the collection and processing of data across the network. 3 GPP introduced in Release 15 the network data analytics function (NWDAF) as part of the 5G service-based architecture, a centralized entity for data collection and analytics at the core network [62]. The calculated analytics can either be predictive or provide statistics about past events.