esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sensitivity Analysis
#### 3.4.2 Monte Carlo Sampling Starting Point Generation Logic  
The genesis points of synthetic typhoons are primarily determined by movement direction (\(\theta\)) and minimum distance (\(D_{min}\)), with their intersection with the simulation circle assumed to be the initial position, which must be located over the ocean. During Monte Carlo sampling of key parameters for typhoon generation, the independent sampling of \(\theta\) and \(D_{min}\)--combined with the simulation circle radius constraint--may yield genesis points over land. The previous methodology discarded such cases through immediate resampling, but this approach risked permanently excluding typhoons with specific directional-distance combinations, contradicting observational reality. To address this, the genesis point generation logic is refined as follows: (1) Extend the simulation circle radius to 500 km; (2) check for viable oceanic genesis points along the extended trajectory; and (3) if no valid points exist within 500 km, trigger parameter resampling.  
Table 8 presents extreme wind speed predictions for Guangzhou across various return periods via empirical, Weibull, and Gumbel distributions (with the new starting point generation logic), and the corresponding Kolmogorov-Smirnov (KS) test results are summarized in Table 9.  
Refinement of the genesis point generation logic addressed the unrealistic exclusion of certain typhoon scenarios in the original approach. Extending the permissible search radius for oceanic genesis points (up to 500 km) allowed for more diverse directional-distance combinations, improving the representativeness of synthetic typhoon origins without introducing spatial bias. While changes in return period wind speeds were not substantial, the method provides a physically more realistic mechanism for parameter sampling and genesis point determination, which contributes to the credibility of the simulation framework.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Return Period (Year)** & **10** & **30** & **50** & **100** & **200** \\ \hline Weibull (m/s) & 17.66 & 20.92 & 22.23 & 23.84 & 25.32 \\ Gumbel (m/s) & 16.71 & 20.78 & 22.64 & 25.14 & 27.64 \\ Empirical(m/s) & 17.65 & 21.96 & 24.00 & 27.16 & 28.63 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Predicted average wind speeds as a function of the return period for Guangzhou (with the new starting point generation logic).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Probability** & **\(p\) Value** & **Test Statistic** & \
\begin{tabular}{c} **Critical Value** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Critical Value** \\ **(1\%)** \\ \end{tabular} \\ \hline Weibull & \(\sim\)0 & 0.0843 & \multirow{2}{*}{0.0449} & \multirow{2}{*}{0.0538} \\ Gumbel & 0.0652 & 0.0430 & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: KS test results of extreme value distributions for Guangzhou (with the new starting point generation logic).
## 4 Optimal Trajectory  
### Optimization Method  
In Section 3, we showed how to find, for a given trajectory \(\Gamma\) and target point \(P_{T}\), two possible launch positions \(\mathbf{p}_{L1}\) and \(\mathbf{p}_{L2}\). In this Section, instead, we seek an optimal trajectory \(\Gamma_{opt}\) (for a given \(P_{T}\)), that is, one having the lowest \"cost\" (which can be defined according to users' requirements) while respecting desired constraints. Since an elliptical path is completely defined by vector \(\mathbf{p}_{C}\) and matrix \(\mathbf{b}\), while the remaining trajectory parameters \(\alpha_{L}\) and \(\omega\) can be computed from Equations (17) and (20), we look for the optimal choice for \(\mathbf{p}_{C}\) and \(\mathbf{b}\), which minimizes a given objective function \(F\). In a throwing operation, we may be interested in limiting both the energy required for throwing and the motion time of the launched object. Reducing the motion time provides obvious advantages in terms of productivity; however, we noted empirically that minimizing \(\Delta t_{LT}\) alone tends to produce unacceptable solutions, as the velocities involved and the corresponding cable tensions may become excessively high. It thus appears sensible to evaluate the energy \(E\) required for the launch motion, as \(E\) depends on both tensions and velocities. Therefore, we include the ballistic flight time \(\Delta t_{LT}\) and the total energy \(E\) required for launch in a vector-valued function \(F\) with two optimization objectives:\[F= \begin{bmatrix}E\\ \Delta t_{LT}\end{bmatrix}  \tag{23}\]  
Some sensible constraints may be set on the velocity \(\dot{\mathbf{p}}_{T}=\left[\dot{x}_{T},\dot{y}_{T},\dot{z}_{T}\right]^{T}= \mathbf{n}_{T}\|\dot{\mathbf{p}}_{T}\|\) at the target point: here, we choose to assign the direction \(\mathbf{n}_{T}=\left[n_{Tx},n_{Ty},n_{Tz}\right]^{T}\) and a maximum magnitude \(\dot{p}_{max}\), such that \(\|\dot{\mathbf{p}}_{T}\|\leq\dot{p}_{max}\). We note that, by differentiating Equation (8) with respect to time, \(\dot{\mathbf{p}}_{L}\) can be derived from \(\dot{\mathbf{p}}_{T}\) as  
\[\dot{x}_{L} =\dot{x}_{T}\] \[\dot{y}_{L} =\dot{y}_{T} \tag{24}\] \[\dot{z}_{L} =z_{T}+g\Delta t_{LT}\]  
The flight time \(\Delta t_{LT}\) can be found from Equation (21), but it is convenient to express it as a function of the optimization parameters. By introducing \(\Delta xy_{LT}=\sqrt{\left(x_{T}-x_{L}\right)^{2}+\left(y_{T}-y_{L}\right)^{2}}\) and \(n_{T,xy}=\sqrt{n_{Tx}^{2}+n_{Ty}^{2}}\), the flight time can be expressed from Equations (9) and (10) as  
\[\Delta t_{LT}=\frac{\Delta xy_{LT}}{\|\dot{\mathbf{p}}_{T}\|n_{T,xy}} \tag{25}\]  
The energy required for launch is the sum of a kinetic and a potential term due to gravity. In order to simplify the trajectory planning, we choose to start the EE from rest and let it reach the dynamical conditions of trajectory \(\Gamma\) through transition trajectories [7] with progressively increasing motion amplitudes. With this choice, the initial rest position is at the center \(P_{\text{C}}\) of \(\Gamma\).
## 4 Simulation Setup  
In order to evaluate our proposed SMIP approach, we have compared the proposed SMIP approach against GIGM-MIP algorithm [19] as well as to the basic MIP algorithm (CL-MIP algorithm). The implementations have been tested using a simulation developed via MATLAB R2014a. The implementations have been tested using simulation developed via MATLAB R2014a. In this paper, MATLAB was chosen for two reasons. First, it is an easy-to-use mathematical simulation tool for different mathematical models and analysis of results. Secondly, in order to fairly compare the simulation results, MATLAB is used because it was used by GIGM-MIP algorithm, which is the compared algorithm to our proposed SMIP approach. We adopted the same network model in [16; 17; 19; 20], which is the most popular network model in data gathering-based MIP. We used the same energy consumption model as in [17; 27]. The sensor nodes are static, densely and uniformly deployed within a 1000 m \(\times\) 500 m network size. We selected a large-scale network with 800 sensor nodes in order to validate the scaling of the proposed SMIP approach. The sink node is located at the center of the network, and it has unlimited energy supply and higher computational capability. All the sensor nodes have the same initial energy with fixed transmission range. Each sensor node has at least one neighbor node with its transmission range set to 60 m. In each data gathering task, a random number of source nodes is selected which is varied from 10 to 40 by the step of 5. Each compared MIP algorithm was tested on the same selected number of source nodes. The sink node has all the geographic information of the sensor nodes, and it is responsible for calculating the main MAs and SMAs itineraries for each data aggregation task. The itineraries are static, where the list of the visited sensor nodes is predetermined at the sink. The simulation parameters are listed in Table 1.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 2 Study Area and Data  
### Study Area  
The winter wheat experiment was carried out in the Tongzhou and Shunyi districts of Beijing from 2008 to 2009, as indicated in Figure 1.  
Tongzhou District is located southeast of Beijing. It is 36.5 km wide from east to west and 48 km long from north to south, for an area of 906 km\({}^{2}\). The highest altitude is 27.6 m and the lowest altitude is 8.2 m. It has a typical continental monsoon climate with distinct seasons; with rain and heat in the same season, a large temperature difference between day and night, an annual average temperature of 10-12 \({}^{\circ}\)C, an annual average precipitation of 620 mm, and an average frost-free period of about 180 days. Winter wheat was sown over an area of 156 km\({}^{2}\).  
Shunyi District is located northeast of Beijing. It is 45 km from east to west and 30 km from north to south, for a total area of 1021 km\({}^{2}\). The plain area accounts for 95.7%. The terrain is high in the north and low in the south. The highest point in the northern mountains is 637 m above sea level. It has a temperate continental semi-humoid monsoon climate with distinct seasons; with cold and dry in winter, hot and rainy in summer, an annual average temperature of 11.5 \({}^{\circ}\)C, an annual average precipitation of 610 mm, and an average frost-free of about 195 days. The average elevation is 35 m and the planting area covers 139 km\({}^{2}\).  
The main grain crops in the study area are winter wheat and summer maize. Winter wheat is the main crop of summer grain in both Tongzhou District and Shunyi District, and its sown area and harvested yield are in the forefront in Beijing.
# Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell +
## 3 Controller Design
### Proposed Control Design  
The initial step in synthesizing the state-feedback controller is establishing the state representation of the system. Figure 2b depicts the structure of the control diagram with state feedback, incorporating integral action to eliminate the system's static error. The control framework comprises two loops: a current loop, ensuring that the inductor currents \(i_{i}\) track a reference current \(I_{\text{ref}}\) generated by the voltage loop controller. Additionally, the current loop serves another function, generating a control law \(u(t)\) aimed at guiding the output voltage to match a reference voltage.  
The gains of the two controllers are synthesized in two steps.
## 4 Experiments and Measurement Uncertainty Analysis
### Deep and Small Hole Diameter Measurement Experiment  
The experiments were carried out using the specifically designed deep and small hole standards to verify the capability of measurable depth and depth-to-diameter ratio. Experimental results using the #2 standard and the \(\phi\)3-mm probe in Figure 8 are shown in Figure 14. Diameters of six cross-sections at different depths were measured. The interval between adjacent cross-sections was 20 mm, and the maximum depth measured was 120 mm. Figure 14 indicates that the hole in #2 standard was generally conical with a slight trumpet shape at two ends.
## 3 The Simulation Environment
### OMNeT++ and Artery-Based Simulation Framework  
The Artery/OMNeT++ framework is a state-of-the-art simulation solution supporting V2X communication with real protocol implementation and advanced traffic modeling capabilities. The Artery framework binds and integrates the following main components.  
* The OMNeT++ framework: The OMNeT++ [49] is an event-based discrete-time network simulation framework that serves as a basis for the Artery framework. It is responsible for the simulation core of the network simulator. It also helps with many utility commands, like its own configuration file format, the XML parsing utilities, or the capability of defining multiple configurations with variable parameters.
* SUMO: The SUMO [50]--Simulation of Urban Mobility--is a discrete-time microscopic traffic simulation framework. The SUMO framework supports network files imported from real-world maps using OpenStreetMaps. It is easily configurable with various parameters like the dynamics of the vehicles or the number of cars in a flow. The most important configuration options are the used network files and the route files defining the vehicles or vehicle flows in the simulation.  
Figure 2: The proposed and implemented simulation pipeline.  
* INET: The INET framework [51] is practically an open-source simulation model library built on top of the OMNeT++ environment. It implements protocols, agents, and other models for researchers and students working with communication networks. It models and implements various Internet-related protocols like IP, UDP, TCP, Wi-Fi, and many others. The Artery framework uses this library for multiple purposes, like the physical layer implementation.
* Veins: Veins [52] is an open-source framework for executing vehicular network simulations. It integrates INET/OMNeT++ (the event-based network simulator with well-detailed protocol models) and SUMO (the road traffic simulator component). Veins also extends these main components to offer a comprehensive suite of models for inter-vehicle communication.
* Vanetza: Vanetza [53] is an ETSI protocol stack library comprising compiled ASN.1 descriptors. It includes various protocols from the ITS transport, network, security, management, and facilities layers. This package was originally designed to operate on the ETSI-standardized ITS-G5 channels in V2X networks using IEEE 802.11p [54], but it can also be combined with other communication technologies.
* Artery: The Artery framework [55] is considered the state-of-the-art V2X simulation solution to evaluate complete C-ITS service infrastructures based on ETSI ITS-G5 protocols like GeoNetworking and Basic Transport Protocol (BIP). Simulated vehicles can be equipped with multiple V2X interfaces to access various possible ITS-G5 services through Artery's middleware component that also implements common facilities for the C-ITS services. Furthermore, the framework provides a sensor architecture with local and global environment models and a scripting toolset for dynamically evolving scenarios.
## 5 Roles of SDN for Cloud-Fog Enabled 5G and Beyond
### Application Placement in Cloud-Fog  
In NFV-based hybrid cloud-fog systems, application components can be implemented as VNFs and the application itself can be seen as a VNF forwarding graph (FG). As one of the rare works that considered the mobility of fog nodes, ref. [16] proposed an algorithm for application components over the cloud-fog system. The authors used the random waypoint model (RWP) to model the mobility of a fog node. This work tried to minimize the makespan and cost to embed VNF-FGs. Makespan here is the time it takes for the first component to start the execution until the execution of the last component is completed. Although in this work, the role of SDN is not clearly explained, the proposed algorithm can be implemented in the control plane of an SDN-based NFV framework. Another work in [66] studied the same problem in a more heterogeneous environment, including software and hardware NFV platforms, such as virtual machine (VM), docker container and SmartNIC.  
Research work [67] contributed to handling dynamic IoV by enabling the hierarchical distributed controllers, where a root controller at a central fog DC manages the underlying controllers and networks. The rest of the controllers are domain and edge controllers, whereas RSUs are directly connected with edge controllers. Edge controllers are deployed at the edge layer, where servers collaborate for fog functions horizontally. However, domain controllers share the network information to the root or global controller in the vertical distributed control architecture. Majorly, the contribution of this work is efficient network management distribution over the root, domain and edge controllers to ensure the least delay for speedy vehicle support. Moreover, the authors also suggested the control channel placement in the fog layers based on the betweenness centrality and modified Louvain algorithm.
## 2 Results and Discussion
### Analyses of 2-Nonenal in the Body Odor Collected from Several Body Parts  
Body odor samples were collected by the wiping method from several body parts 6 h after washing with soap and then analyzed by HS-SPME/GC-MS. Figure 4 shows the 2-nonenal content in body odor samples collected from the arms, axillae, back, chest, forehead, palm, and behind the ears of three subjects. Relatively high amounts of 2-nonenal were detected in the samples from the foreheads, arms, and ears, although this varied between individuals. To determine the amount of 2-nonenal secreted at different times of the day, body odor samples were collected from the forehead every 3 h by the wiping method and analyzed by HS-SPME/GC-MS. As shown in Figure 5, the secretion of 2-nonenal was highest between 13:00 and 16:00 in the afternoon (average contents of 2-nonenal from three people). The chromatogram of the 2-nonenal sampled from the forehead is shown in Figure 6. It was found that 2-nonenal was detected with clear separation and was not affected by the interfering peak.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Storage Condition 1** & **Storage Time (h)** & **Peak Height Count** \\  & & **Mean \(\pm\) SD (\(n=3\))** \\ \hline  & 0 & 25,694 \(\pm\) 1486 \\  & 1 & 25,025 \(\pm\) 2830 \\ Room temperature & 2 & 24,537 \(\pm\) 1454 \\  & 3 & 25,968 \(\pm\) 2162 \\  & 6 & 24,208 \(\pm\) 3058 \\  & 24 & 15,250 \(\pm\) 1600 \\
4 \({}^{\circ}\)C (refrigerator) & 24 & 24,594 \(\pm\) 3679 \\ \hline \hline \end{tabular}  
* The gauze soaked with 50 ng of 2-nonenal was stored in vials.  
\end{table}
Table 2: Comparison of storage conditions after sampling by the wiping method.  
Figure 4: Comparison of 2-nonenal contents in body odor samples collected from several body parts (A: female in their 20 s, B: male in their 20 s, C: male in their 30 s).
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 7 Discussion
### Network Scenario  
Ultimately, the most interesting scenario for NFDM systems is a network scenario, in which different users may share the same fiber links using different portions of the nonlinear spectrum, hence avoiding interference [18]. This is the scenario which can effectively uncover the potential advantages of NFDM over conventional WDM techniques, which cannot avoid nonlinear interference between different unknown channels. In this perspective, it is not really required that NFDM systems actually outperform linearly-modulated systems in the single-user single-channel scenario considered in this work. However, it is at least required that they perform reasonably well in this simple scenario, achieving a spectral efficiency that is comparable to that of conventional linearly modulated systems. Moreover, the actual replacement of WDM networks with NFDM networks would probably require the development of optical add-drop multiplexers that operate directly in the nonlinear spectrum domain. This is a fascinating subject to investigate but, at the moment, it remains purely at a theoretical level.
## 5 Experimental Case Planning, Results, and Analysis
### Experimental Case
#### 5.2.1 Case Study 1--Signal Processing Techniques
The results are shown in Table 5. The CWT compared to two-dimensional time-domain signals transform shows that CWT provides more information on the characteristics of the classification model for fault diagnosis. The average accuracy of the CWT is 99.82% and 98.34%, respectively, which is much higher than the average accuracy of the two-dimensional time-domain signals transform, which is 22.21% and 10.76%, respectively, as shown in Figure 7. This case study compares different signal processing techniques and verifies the superiority of CWT. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Operating Status**} \\ \hline \multirow{2}{*}{Healthy} & \multirow{2}{*}{\begin{tabular}{c} Stator inter-turn \\ short circuit \\ \end{tabular} } & \multirow{2}{*}{Rotor broken bar} & \begin{tabular}{c} Bearing outer \\ ring damage \\ \end{tabular} & \begin{tabular}{c} Misalignment \\ fault \\ \end{tabular} \\ \hline \multicolumn{3}{c}{Training dataset split—per category} \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Training data \\ 350 (samples) \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} Validation data \\ 50 (samples) \\ \end{tabular} } \\ \hline \multicolumn{3}{c}{Testing dataset split—per category} \\ \hline \multicolumn{3}{c}{500 (samples)} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dataset partition—signal processing techniques. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Across Loading** & **Signal Processing** & **CWT** &
\begin{tabular}{c} **Two-Dimensional Time-Domain** \\ **Signals Transform** \\ \end{tabular} \\ \hline
50\% load\(\rightarrow\)100\% load & Average accuracy & 99.82 (\%) & 78.59 (\%) \\ \cline{2-4}  & STD & 0.2006 & 4.889 \\ \hline
100\% load\(\rightarrow\)50\% load & Average accuracy & 98.34 (\%) & 88.08 (\%) \\ \cline{2-4}  & STD & 0.8887 & 4.948 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result—signal processing techniques.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 4 IoT in 5G Performance Requirements
### Energy Efficiency  
**Energy efficiency** is defined as the number of bits that can be transmitted per joule of energy [216]. Compared with current wireless technologies, the energy efficiency (measured in b/J) of the 5G network may need to be improved by a factor of 1000 [4; 7; 34; 110; 217]. High energy efficiency is important in case of ticketing- and in some cases of tracking-based activities (e.g., smart industry [205] or implantable medical devices [35]). Other activities, such as both mMTC and uMTC monitoring- and managing/controlling-based activities (e.g., health condition [205]) and some tracking-based activities (e.g., sports wearables [205]) require medium energy efficiency. Energy efficiency is very important design objective for the reduction of operating costs of telecom operators, as well as for minimizing the environmental impact of the wireless domain [217]. On a higher layer of the network protocol stack, adaptive base station switch on/off algorithms use renewable energy sources to save energy [218] along with an energy scheduler, as in the with heating, ventilation and air conditioning (HVAC) [111]. However, at the physical layer, adaptively switching off unused carriers is a key strategy that can be used to save energy from the radio-frequency (RF) transceiver chain of base stations [218].