esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Propeller Design for HAPS Operation  
### Design Issues for High Altitude Propellers  
Several challenges are associated with the design of a propeller for high-altitude platforms. The primary concern is the selection of the propeller rotational speed (rpm), which is limited by considerations related to the Mach number at the propeller tip. Typically, the Mach number at the propeller tip is maintained between 0.65 and 0.75 to prevent the formation of shock waves on the blade.  
The secondary and most critical challenge, as previously mentioned, concerns the operating environment. Due to the high altitude, airfoils operate under low Reynolds number conditions (from \(10^{4}\) to \(10^{5}\)). This flow regime is characterized by the onset of laminar-to-turbulent transition, where viscous effects dominate the aerodynamic behavior. Compared to high Reynolds number conditions, several phenomena become more pronounced: the lift-to-drag ratio decreases significantly after reaching its peak due to the development of thicker boundary layers; symmetric airfoils may exhibit nonlinear aerodynamic responses even at small angles of attack; and flow separation along the bladescan generate either short or extended laminar separation bubbles. Moreover, the flow becomes increasingly sensitive to external disturbances such as free-stream turbulence, which increases drag and degrades overall propeller performance [36; 37; 38].  
The laminar separation bubble is a primary factor contributing to the reduction in propeller efficiency at low Reynolds numbers [39]. Designing an effective propeller requires mitigating the detrimental effects of laminar separation bubbles on the blades [40]. Moreover, at low Reynolds numbers, the variation in Reynolds number along the propeller blade can become significant due to the increasing linear velocity from the hub to the tip, especially if the airfoil chord does not decrease consistently. This variation, which is negligible at high Reynolds numbers, is typically not considered in the design of conventional propellers. In fact, during the design phase of a conventional aircraft propeller operating in a high Reynolds number regime, constant airfoil characteristics such as lift and drag curves are used for each propeller section, derived from an average constant Reynolds number [110]. Consequently, applying traditional design methods to HAPSs will not yield optimal aerodynamic performance. A poorly optimized design may cause unnecessary drag, increase demands on the power system, and ultimately compromise the mission feasibility.  
Additionally, to efficiently design a propeller, it is crucial to account for the variation in Reynolds number with wind speed. Although wind intensity is minimal at the altitudes where HAPSs operate [122], wind speed remains a critical factor. It affects not only the thrust required for station keeping but also the Reynolds number experienced by each blade section and the relative inflow velocity at the propeller disk, which combines the vehicle's forward speed with any ambient wind along the flight direction. Therefore, analyzing the wind characteristics in the mission region is essential.
## 5 Simulation Experiments and Analysis
### Comparative Analysis of Algorithm Performance
#### 5.3.3 Algorithm Performance Comparison  
Compare and analyze the experimental results in different scenarios. The average shortest path steps are shown in Figure 12a, the average path length is shown in Figure 12b, and the average calculation time is shown in Figure 12c.  
Figure 12: Comparative analysis of experimental results. (**a**) average shortest path steps. (**b**) average path length. (**c**) average calculation time.  
From the histogram (a) of Figure 12, in the simulation experiments of scenarios 1, 2, and 3, the average shortest path steps of UGVs under the OWS Q-learning algorithm in the path planning process is less than the average shortest path steps under the SARSA algorithm, Q-learning algorithm, and speedy Q-learning algorithm. The histogram (b) shows that under the three experimental scenarios, the average length of the path planned by the OWS Q-learning algorithm is the shortest. The histogram (c) shows that under different experimental scenarios, the OWS Q-learning algorithm takes the shortest time to perform path planning and has the highest problem-solving efficiency. Specifically, the improvement in the calculation time of the OWS Q-learning algorithm compared to the SARSA algorithm, Q-learning algorithm, and speedy Q-learning algorithm is shown in Table 6.  
The convergence performance of the four algorithms in different experimental scenarios and the results of the average reward are integrated into Table 7.  
From the results of Table 6, the SARSA algorithm, Q-learning algorithm, and speedy Q-learning algorithm do not converge in the simulation experiments in the three scenarios. The OWS Q-learning algorithm proposed in this paper can achieve a convergence effect in the simulation experiments of three scenarios.  
Based on the above result analysis, the OWS Q-learning algorithm proposed in this paper is superior to SARSA, Q-learning, and speedy Q-learning algorithms in terms of path selection, algorithm convergence, and calculation time.
# Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models
## 2 The Methodology
### Data Pre-Processing  
#### 2.1.1 Data Collection and Normalization  
Today, there is a diverse range of laser scanners, which are the leading equipment for point cloud collection, e.g., spot scanner, single linear scanner, and multi-linear scanner. The source data gathered by the laser scanner can deduce the object's depth value. However, the data types used are different. As an example, Kinect provides six data sources: ColorFrameSource, InfraredFrameSource, DepthFrameSource, BodyIndexFrameSource, BodyFrameSource, and AudioSource. The depthFrameSource channel is selected to gain depth  
Figure 1: The proposed framework for FPPS (feature-preserved point cloud simplification).  
data and this is converted to point cloud (*.pcd). Another common device is Lidar (Light Detection and Ranging). The format of Lidar data is the LDEFS (Lidar Data Exchange Format Standard), whose file extension is \".las\". The \"point data\" is extracted in LDEFS and converted to point cloud (*.pcd). In this paper, the point clouds data set is indicated as \(R\), \(R=\{P_{i}(x,y,z)\}\), \((1\leq i\leq n)\), where points are addressed with a constructor notation \(p(x,y,z)\) and \(n\) is the size of data set \(R\). Because of the uncertainty of the measured object scale, it is difficult to find a suitable view port and show the complete object model for the user. During the pre-process point clouds, data normalization is carried out. The data normalization procedure is executed as shown in Equation (1):  
\[p_{i}(x^{\prime},y^{\prime},z^{\prime})=p_{i}(\frac{x-\min\limits_{i=1}^{n}(x_ {i})}{\max\limits_{i=1}^{n}(x_{i})-\min\limits_{i=1}^{n}(x_{i})},\frac{y-\min \limits_{i=1}^{n}(y_{i})}{\max\limits_{i=1}^{n}(y_{i})-\min\limits_{i=1}^{n}(y _{i})},\frac{z-\min\limits_{i=1}^{n}(z_{i})}{\max\limits_{i=1}^{n}(z_{i})-\min \limits_{i=1}^{n}(z_{i})}) \tag{1}\]
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 4 Results
### Ionospheric Delay Results
#### 4.2.1 Data from Reference Stations--Static Conditions
\(\sigma_{i}\) is the standard deviation of the 3D positioning error per scenario \(i\) over all tracks, defined as 1-\(\sigma\) (i.e., 68%) of a normal distribution and given by  
\[\sigma_{i}=\sqrt{\frac{1}{N_{i}}\sum_{j=1}^{N_{i}}(d_{i,j}-\epsilon_{i})^{2}} \tag{12}\]  
The final statistics are taken as means and standard deviations averaged over all scenarios \(i=1,\dots,N_{scn}\):  
\[mean(\epsilon_{i})=\frac{1}{N_{scn}}\sum_{i=1}^{N_{scn}}\epsilon_{i} \tag{13}\]  
and  
\[mean(\sigma_{i})=\frac{1}{N_{scn}}\sum_{i=1}^{N_{scn}}\sigma_{i} \tag{14}\]  
As shown in Table 4, our model performs best when it comes to decreasing the mean of the positioning error induced by the ionosphere for high-grade GNSS receivers (Table 4a,b). The green percentages shown in Table 4 represent the value change for each model with respect to Method 1 (no ionospheric correction).
## 3 Methodology
### Analysis of the HDR Image Reconstruction Problem
This process is mathematically expressed by the following formula: \(I_{c}=C(I_{n})\), where \(C(\cdot)\) represents the compression function. By analyzing the principles of the LDR imaging pipeline, the operations that lead to loss of image quality are classified into six steps: sensor and exposure adjustments, camera noise, overexposed areas clipping, tone loss in underexposed areas and visual artifacts introduced during the quantization process, nonlinearization, and compression of artifacts. The effect of the loss of quality of the pictures is also shown in Figure 2. It outlines the principles of the imaging pipeline, from HDR Scene Luminance to LDR image formation, as well as the image quality loss operations introduced throughout the process. Based on reversing these operations, which cause a loss of image quality, an LDR-HDR framework is proposed to delineate the imaging process of generating an HDR image from an LDR image into several stages: exposure adjustment, denoising, recovery of overexposed regions, dequantization, reconstruction of linearized mappings, and suppression of compression artifacts. The effects of sensor and exposure adjustments often result in HDR images that are either overexposed or underexposed, with similar impacts arising from the AD quantization and sensor response stages. Image denoising is a well-established field in low-level vision, with researchers continuously developing efficient denoising algorithms [61; 62; 63; 64] and advancing their understanding of complex noise models. Therefore, this work does not design separate modules for exposure adjustment and denoising. In summary, based on the LDR-HDR framework, the RLUNet model is proposed for balancing the effects of dynamic range expansion and recovery of lost information in  
Figure 2: Principles of the LDR imaging pipeline and lossy image quality operations. Visual representations are provided to illustrate the degradation in image quality resulting from these lossy operations. overexposed regions. This model addresses the Recovery of Overexposed Regions (ROR), Dequantization (DQ), Reconstruction of Linearized Mappings (RLM), and Compression Artifact Adjustment Module (CAAM). These components are illustrated in Figure 3.
# A Review of Systems and Technologies for Smart Homes and Smart Grids
## 2 State of the Art
### The New Role of the Users  
Changes in the users' behavior are also another important factor in relation to the improvement of the energy performance of buildings. The data from Intergovernmental Panel on Climate Change (IPPC) showed that behavioral changes could affect energy savings in lighting by up to 70% [35].  
Recently, users have become more conscious of energy usage in buildings and are increasingly interested in real-time energy monitoring and controlling devices and tools [36]. Furthermore, the market for residential energy management is poised to grow dramatically due to increased users' demands and new governmental and industry initiatives [37]. Different energy efficient routing protocols and energy management systems have been proposed [38; 39; 40] to provide information about energy usage patterns. They offer users actionable information and control features while ensuring ease of use, availability, security, and privacy [41].
# Nanovised Control Flow Attestation
## 6 Evaluation
### Test 2: Latency Dissection  
In order to measure the overhead of C-FLAT Linux in its two modes of operation, we have written and executed a CPU-intensive program depicted in Figure 10. The functions do_odd and do_parity multiply their two arguments and return the result. The function foo runs a 100,000 iterations loop that alternately invokes the do_odd and do_parity functions. The program was compiled and executed 10 times in each of the following configurations:  
* Without C-FLAT Linux--to establish the baseline performance;
* With C-FLAT Linux with its BKPT opcodes replaced by the NOP opcode--to measure the degradation associated with the presence of C-FLAT Linux;
* With C-FLAT Linux and full instrumentation--to measure the maximum performance degradation.  
Table 3 presents the execution times of the compiled program in each configuration.  
In order to calculate the attestation latency, we use the following observations:  
* In each operation, the program jumps to the trampoline twice: in the condition of the _for_ and in the condition of the _if_;
* The number of iterations if 100,000;
* The number of trampoline invocations is 200,000;  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Measure** & **BRK Trap** \\ \hline Avg & 92 ns \\ \hline StdDev & 41 ns \\ \hline Max & 156 ns \\ \hline Min & 52 ns \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transition latency.  
Figure 10: Measurement program for C-FLAT Linux.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Test** & **Avg** & **Max** & **Min** & **Std Dev** \\ \hline No C-FLAT & 5188 & 5823 & 5055 & 241 \\ \hline C-FLAT with NOPs & 37,097 & 37,237 & 36,979 & 92 \\ \hline C-FLAT with BKPT & 148,056 & 154,086 & 146,994 & 2174 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Program execution times in \(\mu\)-seconds.  
According to Table 2, a single entry to the Nanovisor takes 92 nanoseconds; 200,000 entries will take 18,400 \(\upmu\)s.  
Therefore, the attestation latency can be expressed as follows:  
\[148,056-5188-37,097-18,400=87,371\upmu\text{s}\]  
where:  
* 148,056 represents the total execution time in the \"C-FLAT with BKPT\" configuration;
* 5188 the total execution time in the \"No C-FLAT\" configuration;
* 37,097 the total execution time in the \"C-FLAT with NOPs\" configuration;
* 18,400 represents the time required to enter the Nanovisor.  
The attestation itself is responsible for \(\frac{87}{148}=58\%\) of the total execution time.
# Deep-Reinforcement-Learning-Based Intelligent Routing Strategy for FANETs
## 6 Future Work  
Suitable routing algorithms are quite important for FANETs, the proposed method based on deep reinforcement learning in this paper can be well applied to FANETs with topological dynamic characteristics by considering multiple states of nodes in a local region. Furthermore, it is not mandatory to obtain the information of all the nodes within the two hops, which also makes the method feasible in reality. Compared with several utilized comparison methods, the main disadvantage of the proposed method is its high complexity, as it needs to obtain more information and carry out the corresponding preprocessing, but the calculation of the proposed method consumes very few resources, which is completely affordable for UAV nodes.  
It should be noted that although central routing algorithms cannot solve the dynamic problem of the FANETs well, their global planning process may have some advantages over the distributed algorithm in some respects. Therefore, a routing algorithm combining central and distributed modes may have better performance, which can be taken as a further direction for researchers. Meanwhile, the types of messages will also become increasingly abundant with the rapid development of wireless communication networks. It is expected to be able to provide users with required services more flexibly and efficiently by considering the different performance requirements of various message types in the design of a routing algorithm.
# Distributed Authentication and Authorization Models in Cloud Computing Systems: A Literature Review
## 3 Results
### Cloud Access Control Mechanisms Included in the Review
#### 3.2.4 Attribute-Based Access Control (ABAC)  
The use of ABAC to overcome the constraints of traditional access control models has triggered a lot of interest recently. The basic object of ABAC is to use the attributes of involved entities as a basis to make the access control decision [24]. To represent the user's access profile, ABAC uses the combination of the attributes associated with the subject's \"S\", such as the identity, role, and department; the attributes associated with the resource \"R\" such as owner, type, and created time [24]; and the attributes of the environment \"E\" in which authorization process occurs as a time, location, or other technical and operational settings. Thus, ABAC provides a fine-grained expression of the authorization policies rules.  
RULE X: f (ATTR(s), ATTR(r), ATTR(e)) \(\rightarrow\) can_access(s,r,e)  
Such a feature is vital in dynamic and open environments such as the cloud and the use of different attributes in ABAC instead of one \"role\" as in RBAC, which is expected to satisfy the flexibility needs of the cloud paradigm. AWS uses this characteristic and provides such a type of authorization based on context information such as location, time, and address which is represented in ABAC authorization policies [13]. Many researchers have designed cloud computing access control models by merging the flexible administration feature of RBAC and the dynamic features of ABAC. RBAC was extended by including attributes of the subject, object, and the cloud context environment in a dynamic authorization process and used the roles in RBAC [26,33]. However, this model sacrificed the performance of the dynamic access decision process in the cloud, which depends on verifying a list of access policies in the system against the cloud environment context. In [34], an access control model for cloud computing was proposed using the roles and attributes of subject and object. This provides more specific fine-grained rules in the access policy. However, the frequent and dynamic changing in attributes requires a new calculation to assign the permission and take the access decision, which may be exploited by attackers to destroy the data.
# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
## 7 Zigbee Network Traffic Dataset and Its Characteristics
### Network Traffic Characteristics
#### 7.2.8 Power Source Distribution of Traffic  
In this subsection, we analyze the distribution of network traffic based on the power source of the Zigbee devices, focusing on devices powered by batteries and those connected to mains power. This analysis is critical for understanding how power source impacts communication patterns and network behavior.  
(1) Power Source Distribution  
To visualize the distribution of network traffic, a pie chart (Figure 12a) showing the percentage of packets originating from battery-powered and mains-powered devices was generated. In total, 601,463 packets (2.46%) were transmitted by battery-powered devices, while 23,843,029 packets (97.54%) came from AC-powered devices.  
When considering the number of devices involved, with 3 battery-powered devices and 12 AC-powered devices, the average number of packets sent per device can be calculated. On average, each battery-powered device transmitted approximately 200,488 packets, while each AC-powered device transmitted about 1,986,919 packets (Figure 12b). This significant disparity highlights the higher communication frequency and load handled by AC-powered devices, likely due to their stable energy supply, enabling more sustained network activity.  
(2) Traffic Involving Battery-Powered Devices  
In addition to the overall distribution, we also examined the number of packets specifically transmitted from and sent to battery-powered devices. A bar chart (Figure 13) illustrates this comparison. Battery-powered devices sent 39,276 packets, while 38,155 packets were received by battery-powered devices. This relatively balanced traffic suggests a bidirectional communication pattern between battery-powered nodes and other network participants.  
This analysis of power-related traffic patterns provides valuable insights into the network dynamics, highlighting the dominant role of mains-powered devices in handling the bulk of communication, as well as the interaction between battery-powered devices within the Zigbee network. Such information is vital for optimizing energy-efficient communication protocols and maintaining network performance.
## 3 Results
### Diagnostic Performance of PET/CT and BMB in Determining BMI
#### 3.4.1 Diagnostic Performance of [18]FDG PET/CT and BMB in Determining BMI in NHL Patients  
Among the included studies, the median values for sensitivity, specificity, PPV and NPV of [18]FDG PET/CT and BMB were analyzed and presented in Table 5. For the general population of NHL, the sensitivity and specificity of [18]FDG PET/CT in determining BMI ranged from 0% to 100% (median = 73.30%) and from 50% to 100% (median = 89.70%) across the eligible studies, respectively. Moreover, the PPV and NPV of [18]FDG PET/CT in determining BMI for NHL patients ranged from 0% to 100% (median = 63.30%) and from 56.70% to 100% (median = 92.45%), respectively. For the BMB analysis of the NHL cases, the sensitivity ranged from 24.00% to 96% (median = 56.00%), the specificity ranged from 95.80% to 100% (median = 100%), the PPV 100% (median = 100%), NPV ranged from 69% to 95% (median = 83.65%).  
Among the included studies with patients data of indolent NHL for [18]FDG PET/CT (n = 1164; studies = 12), the median values of the sensitivity, specificity, PPV and NPV were 58.50%, 85.10%, 60.00%, 78.30%, respectively. For the studies of BMB with patients data of indolent NHL (n = 186; studies = 5), the median sensitivity and NPV was 93.70% and 95.55%, while the specificity and PPV were reported only on one study [46] and were equal with 100%. Regarding the studies with patient populations with aggressive NHL for [18]FDG PET/CT (n = 2821; studies = 24), the median sensitivity was 77.00%, specificity 93.80%, PPV 63.60%, and NPV 97.00%. For the included studies with patients data of aggressive NHL for BMB (n = 1216; studies = 9), the median sensitivity was 57.90% and NPV 84.42%, whereas the specificity and PPV were calculated only in five and four of the studies, respectively, and they were both equal to 100%.
# Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks
## 2 Background
### Related Works
In [24], authors proposed a cooperative MAC protocol based on TDMA. However, the TDMA-based MAC protocol is not suitable for the IEEE 802.15.4 standard. Also, an energy efficient communication technique for the multi-hop link has been proposed to increase the lifetime of wireless sensor networks [25]. In [25], the authors try to extend the lifetime through adaptive sleep. But the communication scheme proposed in [25] cannot solve high latency by the competition for a channel access and does not consider applying to the IEEE 802.15.4 standard. There are also numerous interest research on cooperative MAC based on the IEEE 802.15.4 standard [9; 26; 27; 28; 29]. However, cooperative schemes in [9; 26; 27; 28] are mainly focused on the PHY layer. Also, the cooperative scheme proposed in [29] is not suitable for the multi-hop network environment since it is proposed for the single hop NPAN environment. Therefore, in this paper, we propose a new cooperative scheme suitable for multi-hop networks.