esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 5 Experimental Evaluation and Results
### Hardware and Software Configuration  
#### 5.1.1 Ground Experiment  
The location of the experiment was in the city of Montreal with a ground vehicle equipped by the MC-SDR and an IMU. The principal goal of this experiment was to acquire the SOP downlinks transmitted by multiple LEO satellites to aid the EKF-based slave Doppler positioning system. A 9 degree-of-freedom (DoF) VN-100 IMU was mounted on the ground vehicle to record the vehicle's three-axis acceleration, three-axis magnetic field, and three-axis angular velocity as a dynamic receiver. To receive precise downlink signals, the antennas from each constellation were prepared based on our last signal acquisition tests in various environments. As a result, a dual-band VHF/UHF mobile Orbcomm as well as an Iridium-Next L-band antenna were connected to two different SDRs: a BladeRF V2.0 and a RTL-SDR dongle. First, the overhead satellites were detected using their predicted elevation angles. Later, the SDRs recorded the satellite's downlinks from the Iridium-NEXT and the Orbcomm constellations.  
To have an acceptable and noise-free signal acquisition, we used a RF bandpass filter on the Orbcomm signals and a low-noise amplifier (LNA) for the Iridium-NEXT channel. The recording sampling frequency for the IMS was set to 100 Hz and that of the SDRs was selected at 1.2288 MHz. The center frequencies of the Orbcomm and Iridium-NEXT channels were specified at 137.5 MHz and 1626 MHz, respectively. Figure 4 demonstrates the overall details of the utilized hardware and software during the experiment. The LASENA MC-SDR was used to analyze the signals. It also obtained the pseudorange rate measurements of the detected overhead satellites. At the end, the inertial data and the doppler shift outputs of the receiver were postprocessed in the MATLAB to run the proposed alignment method.
## 3 Proposed System Design
### Design of the Proposed System  
The design of the M-LUO converter-fed BLDC for the water pumping application is discussed in this section. The components, such as the input inductor, capacitors \(L_{1}\) and \(C_{1}\) respectively, output inductors \(L_{2},\ L_{3}\) and output dc-link capacitor \(C_{2}\) work in a continuous conduction mode with low stress on the devices and its elements. Depending on the duty cycle, the mode of operation can be changed.  
\[\text{Duty cycle}=V_{out}/(V_{out}+V_{solar})=300/(300+56)=0.84 \tag{4}\]  
where \(V_{out}\) is the average value of the output voltage of the LUO converter, which is equal to the BLDC motor's input voltage.  
The estimated output current can be written as:  
\[I_{0}=P_{out}/V_{out} \tag{5}\]  
The BLDC motor rating is taken as \(P_{out}=746\) W:  
\[I_{0}=746/300=2.48\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value with Units** \\ \hline Peak power (P\({}_{\text{mp}}\)) & 100 W \\ \hline Open Circuit Voltage (V\({}_{\text{oc}}\)) & 22.68 V \\ \hline Short circuit current (I\({}_{\text{sc}}\)) & 5.86 A \\ \hline Peak power voltage (V\({}_{\text{mp}}\)) & 18.75 V \\ \hline Peak power current (I\({}_{\text{mp}}\)) & 5.42 A \\ \hline Number of series-connected cells (N\({}_{\text{s}}\)) & 36 \\ \hline \end{tabular}
\end{table}
Table 1: Specification of the solar panel.  
Then, the estimated maximum permitted ripple values of \(L_{1}\) and \(C_{1}\) are:  
\[\begin{array}{ll}\Delta I_{L}&=I_{o}\times\frac{V_{0}}{V_{in(min)}}\\ &=2.48\ (300/56)+30\%\text{ ripples}\\ &=13.28+3.98\\ &=17.26\ \text{A}\\ &\Delta I_{L}=\Delta I_{\Pi}=\Delta I_{\Pi 2}\end{array} \tag{7}\]  
The inductor L\({}_{1}\) in the modified LUO converter is calculated by the following equation:  
\[\begin{array}{ll}L_{1}&=\text{D}V_{solar(in)}/f_{sw}\Delta I_{l1}\\ &=0.84\times 56/10\times 10^{3}\times 17.26\\ L_{1}&=0.273\ \text{mH}\end{array} \tag{8}\]  
The inductances L\({}_{2}\) & L\({}_{3}\) in the modified LUO converter are calculated by the following equation:  
\[\begin{array}{ll}L_{2}&=(1-\text{D})V_{solar(in)}/f_{sw}\Delta I_{l2}\\ &=(1-0.84)\times 56/10\times 10^{3}\times 17.26\\ L_{2}&=0.052\ \text{mH}=L_{3}\end{array} \tag{9}\]  
The change in the capacitor voltage across C\({}_{1}\) is given by:  
\[\begin{array}{ll}\Delta V_{cs}&=I_{0}\times D/C_{s}\ f_{sw}\\ &=2.48\times 17.26/10\times 10^{-6}\times 10\times 10^{3}\\ \Delta V_{cs}&=428.04\ \text{V}\end{array} \tag{10}\]  
The following equation calculates the capacitor \(C_{1}\) in the modified LUO converter:  
\[\begin{array}{ll}C_{1}&=I_{0}\times D/\Delta V_{cs}f_{sw}\\ &=2.48\times 0.84/428.04\times 10\times 10^{3}\\ C_{1}&=486\ \text{nF}\end{array} \tag{11}\]
# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 5 Case Study for the Healthcare Sector  
This section demonstrates the applicability of the APSIA methodology and tool in a case study of a hospital. The case study considers security and privacy concerns against the hospital's ICT infrastructure that threaten processing activities. The case study is based on actual requirements driven from a real use case in the context of the H2020 CUREX project [54]. More specifically, during the testing process of APSIA we approached a set of healthcare related stakeholders, ranging from the IT support team and the security administrator of a hospital to physicians, for acquiring information regarding the actual assets, medical devices and processes which shall be considered in a healthcare environment. Based on this information, we fleshed out the actual topology of ICT assets and applications along with their interdependencies, and we defined the data and the processing activities. The gathered information was used to create an accurate virtual topology in order to simulate in a lab environment the existence of vulnerable devices that support the hospital's data processing activities and may trigger privacy risks. APSIA deployed in a virtual machine to ensure network visibility and enable asset inventory and vulnerability detection. The generated setup of all the aforementioned aspects of the virtual environment and the visualisation results shown in Appendix A, were then ratified by the board of relevant professionals of the CUREX project, who identified the value of APSIA.  
More specifically, the IT infrastructure of the hospital facilitates the deployment of Healthcare Point of Care (POC) technologies. POC have been widely used during the last decade to pave the way to the emergence of healthcare monitoring and management. POC technologies are hospital information systems that includes terminals or other devices for medical diagnostic testing at or near the site of patient care [55]. The advances of POC have enabled patients to receive better care. However, along with these advances, there are concerns regarding the connectivity of these devices to the Internet or to a Health Information System (HIS), since it may affect both the security and the privacy of a patient. Even though, the connected medical devices improve the quality of patients' care, they also expose a wide attack surface and introduce new and domain-specific vulnerabilities. In addition, the rising number of the processed personal data in conjunction with the increasing data breaches has led to an attention towards data protection and privacy. Hospitals and care centers need to address these challenges by efficiently assessing the privacy risks in tandem with GDPR that mandates such an assessment. To this end, in our scenario we consider a subset of common assets and devices that exist in a hospital's POC to demonstrate the APSIA methodology and tool.
# Unsupervised Detection of Changes in Usage-Phases of a Mobile App
## 4 Results and Discussions
### Case Studies
The first insight was the need for a dynamically adjusted threshold. Our research confirmed that in order to detect changes, a threshold should be adjusted dynamically (Tables 2, 4, and 5). The empirical results showed that conventional change detecting methods based on hypothesis testing ([28; 30]) were not suitable for detecting changes in usage-phases of a mobile app. The inferior performance of the hypothesis testing based method resulted from the lack of sufficient screenshots in each usage-phase. In our datasets, each usage-phase consisted of 6.0 screenshots on average. Thus, conventional hypothesis testing based methods may be unsuitable for app testing. The second insight was the need for utilizing probability distributions resulting from screenshots. Although a graph kernel based method achieved better results overall, graph based methods had some deficiencies due to external causes. Firstly, current object recognition techniques are not perfect, so there existed some UI widgets that were unrecognized or wrongly recognized. Secondly, each screenshot from a mobile app contained a relatively small number of UI widgets. Thus, the failure of recognition could hinder the formation of graphs.
# A Solution Procedure to Improve 3D Solid Finite Element Analysis with an Enrichment Scheme
## 5 Discussion  
This paper introduces an automatic procedure for adaptive enrichment, designed to improve the accuracy of 3D solid finite element solutions. We employ the enriched 3D solid finite elements free from the linear dependence problem. The proposed procedure first estimates the error at each node using the error indicator and then applies an appropriate degree of cover functions (up to quadratic) to selected nodes using the cover function selection scheme. The proposed procedure can enhance the solution accuracy without requiring mesh refinement.  
Several examples were presented to validate the effectiveness of the proposed procedure. Notably, the accuracy of the solution demonstrated a marked improvement, especially in terms of the maximum von Mises stress, a key concern in engineering practice. In the case of the ad hoc problem with a coarse mesh, the error in the maximum von Mises stress was reduced from 60.48% to 19.39%. Furthermore, the results from other cases also demonstrated the effectiveness of the proposed procedure.  
In future studies, the automatic procedure for adaptive enrichment could be extended to shell analysis [26; 27; 28; 29; 30; 31], other physical problems [32; 33; 34; 35; 36; 37], and other finite element methods like smoothed FEM [38]. It could also be applied to dynamic analysis, such as component mode synthesis (CMS) methods, to enhance the precision of dominant component modes through the adaptive application of cover functions [39; 40; 41].  
Figure 21: von Mises stress (\(\tau_{vM}\)) distribution for the proximal femur problem.  
**Author Contributions:** Conceptualization, C.L. and S.K.; methodology, H.-G.C.; validation, Y.I.B. and C.K.S.; formal analysis, M.B.G.J.; investigation, S.K.; writing--original draft preparation, H.-G.C., Y.I.B., M.B.G.J. and C.K.S.; writing--review and editing, C.L. and S.K.; visualization, H.-G.C.; supervision, C.L. and S.K.; project administration, S.K.; funding acquisition, C.L. and S.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), grant number [NRF-2020R1G1A1006911]. This research was also funded by a Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE), grant number [P0017310, Human Resource Development Program for Industrial Innovation (global)].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data can be obtained from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Cost Evaluation
### Economic Evaluation of Mould Manufacturing  
Using the data centralised in Table 3, an analysis of the cost of manufacturing the mould by the additive manufacturing process using a 3D printer was carried out. Thus, approximately 9.3 kg PLA filament (approximately 13 rolls) were used, with each roll of material costing an average of EUR 44, which means that the material resources required for printing amount to EUR 572. The electricity used in 502 h of printing can be multiplied by the 500 Wh used by the Ultimaker S5 printer, giving a total of 251 kWh. Multiplying this value by the price of 0.3 EUR/kWh leads to the conclusion that the cost of printing associated with electricity consumption amounts to EUR 75.3. The last cost is the user labour cost. The labour is broken down as follows: pre-processing of the STL model and preparation of the printer--15 h; post-processing of the printed parts--2 h; assembly of the mould and adjustment of the active surfaces (materials included in the labour)--16 h; and application of the surface treatment (materials included in the labour)--5 h. Thus, the labour involved in making the prototype mould of the anti-torque blade was 38 h and  
Figure 14: Composite blade/ (**a**) Final blade shape manufactured using 3D-printed mould. (**b**) Detail of blade tip area.  
worth EUR 380. The cost of labour was calculated at 10 EUR/hour. From the cost analysis, it results that for the manufacture of the two moulds (lower mould-MAT-COMP V3-FEMALE and upper mould-MAT-COMP V3-MALE), the value of EUR 1027.3 was reached.  
Using the same input geometry, a quotation was requested via the Xometry website for a 6082 aluminium alloy mould in order to be able to compare the cost of making the mould. The production cost using CNC machining for the MAT-COMP V3-FEMALE (lower mould) was EUR 1926.56 and for the MAT-COMP V3-MALE (upper mould) was EUR 1409.30. Thus, to manufacture the mould, made of 6082 aluminium alloy, using CNC machining the total cost was EUR 3335.3.  
From the study of the cost of manufacturing the mould (by the two processes), it can be concluded that the production of the prototype mould of the anti-torque blade by conventional methods (CNC machining) is about three times higher compared to the cost of producing the mould by additive manufacturing using a 3D printer. In Table 4, the manufacturing costs of composite blades using the two types of manufacturing processes (additive manufacturing and CNC machining) are summarised. The costs of the 3D-printed mould include the following: cost of materials, cost of energy, and cost of mould assembly and preparation. The total 3D-printed mould manufacturing time includes the 3D printing time and mould assembly and preparation time.
# Real-Time Egress Model for Multiplex Buildings under Fire Based on Artificial Neural Network
## 3 Artificial Neural Network (ANN) Model for Estimating Available Safe Egress Time (ASET)
### Verification of ANN Model  
In order to examine whether the proposed ANN model can be applied to other multiplex buildings, a fire simulation was carried out on a new multiplex building with a floor area of 3150 m\({}^{2}\) (75 m \(\times\) 42 m), as shown in Figure 14. The type of fuels and the location of fire sources were set as main variables in the same way as in the simulation conducted previously. Polystyrene foams and polyurethane foams were considered as fuels, and the location of fire sources was divided into ten compartments. As shown in Table 9, the fire simulation was conducted on a total of 20 scenarios. For the fire growth curve, fire loads according to the fuel type were calculated based on the DB provided by NCFS [18], and the maximum HRR value was applied to the t-squared fire curve, in which the fire growth rate was set to medium. The natural ventilation condition was considered as in the simulation conducted earlier, and the building has 26 openings (windows and doors) which is 14.1% of the opening ratio, i.e., the ratio of total opening area to outer wall perimetric area.  
Figure 14: Plan of multiplex buildings for verification.  
Figure 13: Comparison of ANN model and unused data on training.  
Table 10 summarizes the FDS analysis results at a random fire point. The temperature, visibility, and concentrations of oxygen (O\({}_{2}\)), carbon dioxide (CO\({}_{2}\)), and carbon monoxide (CO) obtained from the FDS analysis were entered into the proposed ANN model to estimate the ASET. Figure 15 and Table 11 show a comparison between the FDS and ANN analysis results. The verification results showed that the average error rate of the FDS and ANN models was only 8.3%, and the COV of the ratio of the ANN analysis results to the FDS analysis results was 0.117, which means that the proposed model had achieved high levels of reliability.
## 5 Applications
### Intelligent Video Surveillance  
Traditional security surveillance systems use many cameras and require laborious human monitoring for video content analysis. On the other hand, intelligent video surveillance systems are aimed at automatically tracking the individuals or a crowd and can recognize their activities. These kinds of systems can also detect the suspicious or criminal activities and report to the authorities for immediate action. In this way, the workload of the security personnel can be reduced and security events can be alerted, which can be helpful to prevent dangerous situations.  
Different techniques have been proposed for the video surveillance systems. In [178], a visual surveillance system was proposed for tracking and detection of moving objects in real time. A novel method for tracking the pedestrians in crowd video scenes using local spatio-temporal patterns exhibited by the pedestrians was proposed in [179]. It used the collection of Hidden Markov Models (HMMs) trained on local spatio-temporal motion patterns. By employing this representation authors were able to predict the next local spatio-temporal patterns of the tracked pedestrians based on the observed frames of the videos. In [180], a framework was developed for an anomaly detection and automatic behaviour profiling without manual labelling of the training dataset. This framework consists of different components required to develop an effective behaviour representation for event detection.  
A real time approach for novelty detection and anomaly recognition in video surveillance systems was proposed in [181]. It is based on on-line clustering technique for the anomaly detection in videos using two-step process. In the first step, recursive density estimation (RDE) was used for novelty detection using frame wise Cauchy type kernel. In the second step, multi-feature on-line clustered trajectory was used for the identification of anomalies in the video stream. The detail survey on video surveillance can be found at [182; 183].
## 2 Materials and Methods
### Algorithm for Downscaling  
As shown in Figure 2, the proposed downscaling algorithm contains two phases: the regression process and area-to-point kriging (ATPK) process. It firstly conducts regression analysis between GPP and heterogeneity factors (i.e., altitude, slope, aspect and LAI), and then ATPK is performed on the downscaling residuals from the regression process.  
Figure 2: Flow chart of the proposed downscaling algorithm.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 3 O-RAN in Smart IoT Systems
### Smart Healthcare Systems
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference, Year** & **Objective** & **Gap** & **Contribution** & **Remarks** \\ \hline Ref. [34], 2022 & \(X_{2},Y_{2}\) & Traditional RAN slicing is achieved using proprietary solutions & Applied game theory and actor-critic learning in the RIC, to address the resource allocation problem of IIoT & Despite the solution’s robustness in preserving the system’s QoS, security must be kept in mind as well \\ \hline Ref. [81], 2022 & \(X_{2},Y_{2}\) & distributed AI/ML solutions in IIoT are implemented in small scales & Used the RIC to control the large-scale connectivity of diverse smart factory components & Large-scale solutions require dedicated testing and validation tools while considering safety threats targeting the model and the system \\ \hline Ref. [65], 2023 & \(X_{2},Y_{3}\) & signaling messages evaluation is hard to achieve with monolithic solutions & xApp that studies the IIoT control plane messages statistics and detects any outlier as malicious right from the registration stage & Anomaly detection methods in a dynamic, heterogeneous environment that encourages interoperability needs to be robust, resilient, scalable and operate in real time \\ \hline Ref. [66], 2023 & \(X_{2},Y_{3}\) & IIoT data security cannot be guaranteed by single scale solutions & Examines O-RAN’s readiness to provide an interoperable ecosystem housing diverse security solutions working together to preserve IIoT QoS requirements & Security by design principles needs detailed investigation in this context \\ \hline Ref. [67], 2023 & \(X_{2},Y_{2}\) & 5G-based IIoT solutions are monolithic & Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis of O-RAN in addressing the connectivity requirements of IIoT applications, i.e., in terms of monitoring and control & O-RAN potentials can only be uncovered by IIoT applications if the solutions are tested thoroughly, standardized for effective deployment \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of O-RAN in smart manufacturing systems. specifications, standards and tools facilitating the development of enhanced solutions that preserve the end-to-end privacy and security of the patient's data.
## 4 CLAP Correlation and Information Sharing Services
### Information Sharing and Exporting
#### 4.3.2 Exporting Outputs  
CLAP generates a PDF report containing the list of generated alarms within a predefined period of time, the top 10 detected attacks, most used ports, the hosts involved in the security alarms, the number of occurrences of each alarm, and the top 15 alarms based on their risk level. Automatic actions can be specified to deal with the platform output e.g., a PDF report can be automatically generated with a predefined periodicity, it can be sent by email or displayed in the platform dashboard.  
It is also possible to generate a report of the detected events either in CSV or PDF format. The report allows the display of up to 5000 events and includes several data features such as the type of event, the timestamp, the sensor responsible for the detection, the IP and port source and destination, as well as the severity level associated with the event.