esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design  
This study only chooses four states to control the quadrotor, the altitude (Z) for the linear motion, and three attitude angles (roll, pitch, and yaw) for the angular motion. The state variable for the chosen state is defined as in Equation (3) below:  
\[x=\left[z,\ \dot{z},\ \phi,\ \dot{\phi},\ \theta,\ \dot{\theta},\ \psi,\ \dot{\psi}\right]^{T}=[x_{1},\ x_{2},\ x_{3},\ x_{4},\ x_{5},\ x_{6},\ x_{7},\ x_{8}]^{T} \tag{3}\]  
Typically, the translational and rotational dynamics of the quadrotor can be decomposed into four separate subsystems as given in Equations (4)-(7) below:  
Altitude subsystem:  
\[\dot{x}_{1}=x_{2}\]  
\[\dot{x}_{2}=-g+cos\phi cos\theta\left(\frac{1}{m}\right)U_{1} \tag{4}\]  
Roll subsystem:  
\[\dot{x}_{3}=x_{4}\]  
\[\dot{x}_{4}=\dot{\theta}\dot{\psi}\left(\frac{1-I_{2}}{I_{x}}\right)-\frac{I_ {2}}{I_{x}}\dot{\theta}\Omega+\frac{1}{I_{x}}U_{2} \tag{5}\]  
Pitch subsystem:  
\[\dot{x}_{5}=x_{6}\]  
\[\dot{x}_{6}=\dot{\phi}\dot{\psi}\left(\frac{I_{2}-I_{2}}{I_{y}}\right)+\frac{ I_{y}}{I_{y}}\dot{\phi}\Omega+\frac{1}{I_{y}}U_{3} \tag{6}\]  
Yaw subsystem:  
\[\dot{x}_{7}=x_{8}\]  
\[\dot{x}_{8}=\dot{\phi}\dot{\theta}\left(\frac{I_{2}-I_{2}}{I_{z}}\right)+ \frac{1}{I_{x}}U_{4} \tag{7}\]  
In order to control and stabilize the system, an appropriate control law for the system must be designed to accomplish the desired control performance. Since the structure of the control system design is similar in each state, for simplicity, only one structure is shown, which is the altitude motion; the structure of the hybrid PD2-LQR controller is depicted in Figure 1.  
Let a state-space representation of a linear system is described as in Equation (8) below:  
\[\dot{X}=AX+BU\]  
Figure 1: The structure of the hybrid proportional double derivative and linear quadratic regulator (PD2-LQR) controller.  
Now, from the above control structure, the control law of the system can be represented as in Equation (9) below:  
\[U=-K_{T}+EK_{e} \tag{9}\]  
From there, we can see that the gain \(K_{T}\) is the total gain of the original LQR controller's gain and the PD2 controller's gain and is defined as Equation (10) below, and \(Ke\) is the system's error gain.  
\[K_{T}=KX+K_{p}CX+(K_{d1}+K_{d2})C\frac{dX}{dt} \tag{10}\]  
\(E\) is the error signal between the output y and the reference input defined in Equation (11). Now, combining Equations (10) and (11), the control input of the system can be expressed as Equation (12) below:  
\[E=-y+r_{d}=-CX+r_{d} \tag{11}\]  
\[U=-KX-K_{p}CX-K_{e}CX-(K_{d1}+K_{d2})C\frac{dX}{dt}+K_{e}r_{d} \tag{12}\]
# Resilience in the Internet of Medical Things: A Review and Case Study
## 6 A Case Study: Remote Patient Monitoring of a Heart Patient in the IoMT Networks
### Proposed Methodology
#### 6.3.1 First Combination: ML + SDN  
The combination of ML and SDN can efficiently resolve a number of challenges. The first challenge that this combination can handle is to develop a global view of the  
Figure 6: An expected framework of IoMT networks with distributed functionality and resilience.  
entire IoMT network to understand the existing or upcoming complications within the network [101]. The next important step is to conduct the traffic classification proactively. Traffic classification is conducted by redirecting the traffic through the safe nodes [102].  
Route optimization, which becomes a complex task due to the rapid and dynamic change in the topology, can be handled by this combination [103]. Finally, the localization of SDN controller at the appropriate position is possible with the help of ML so that the QoS requirement can be fulfilled and resource management can be conducted effectively in the dynamic conditions [104].  
So, overall, the combination of ML and SDN can benefit the IoMT infrastructure by providing monitoring as required for meeting the global view challenge. Secondly, this combination can benefit the IoMT networks by providing control, which is required for traffic classification and route optimization in the rapidly changing topology of IoMT networks. To avoid the single point of failure, monitoring and control should be redundant in the different parts of the IoMT infrastructure, as shown in Figure 6, so that the monitoring of active nodes and control through route optimization can still be possible during the controller failure.
## 4 Implementation Strategy
### Phase III--Autonomous Execution
```
1:Start
2:Display MainScreen() \(\triangleright\) Show main interface
3:Select Robot() \(\triangleright\) Select a robot from the list
4:Display TaskAssignmentScreen() \(\triangleright\) Show task assignment screen
5:ActionType \(\leftarrow\) SelectAction() \(\triangleright\) Choose action: Harvest, Maintenance, Pruning
6:DateTime \(\leftarrow\) EnterDateTime() \(\triangleright\) Set the date and time for task execution
7:Periodic \(\leftarrow\) SelectPeriodicOption() \(\triangleright\) Choose if the task is one-time or periodic
8:ifPeriodic = True then
9:Interval \(\leftarrow\) EnterInterval() \(\triangleright\) Set the interval for periodic tasks (e.g., daily, weekly)
10:endif
11:Route \(\leftarrow\) AssignRoute() \(\triangleright\) Assign path within the greenhouse using a map
12:ConfirmTask(ActionType, DateTime, Periodic, Route ) \(\triangleright\) Confirm task assignment
13:Display PauseButton, StopButton, ResumeButton \(\triangleright\) Show control buttons for pausing, stopping, or resuming the task
14:whileTaskInProgress do\(\triangleright\) Monitor task execution in real-time
15:RobotStatus \(\leftarrow\) GetRobotStatus() \(\triangleright\) Get current status of the robot
16:Display RobotStatus \(\triangleright\) Display robot status on screen
17:ifPauseButtonPressed then
18:PauseTask(Robot) \(\triangleright\) Pause the task if pause button is pressed
19:endif
20:ifStopButtonPressed then
21:StopTask(Robot) \(\triangleright\) Stop the task if stop button is pressed
22:endif
23:ifResumeButtonPressed then
24:ResumeTask(Robot) \(\triangleright\) Resume the task if resume button is pressed
25:endif
26:ifRobotStatus = Failure then
27:Display Alert(\"Robot Failure Detected\") \(\triangleright\) Alert user about robot failure
28:endif
29:ifRobotStatus = TaskCompleted then
30:Display Notification(\"Task Completed\") \(\triangleright\) Notify user that task is completed
31:endif
32:endwhile
33:End
```  
**Algorithm 1** Robot task assignment algorithm for greenhouse operations.
## 3 Results and Discussion  
### XRD Analysis  
The films deposited by electrophoresis were subjected to XRD analysis over a range of 20 to 80\({}^{\circ}\) to obtain information about the peaks, their positions, and intensities (Figure 2). The observed peaks were (100), (002), (101), (102), (110), (103), and (200). The predominant peak among all six deposited films was (101), indicating the preferred growth orientation of the structure. These peaks are consistent with the wurtzite structure of zinc oxide, as reported in [19]. The crystallite sizes for the films were calculated using the Scherrer equation:  
\[\mathrm{D}=\mathrm{k}\lambda/\beta\mathrm{cos}\ \theta, \tag{1}\]  
where D is the crystallite size, k is the Scherrer constant (0.9), \(\lambda\) is the wavelength of 0.15406 nm, \(\beta\) is the FWHM (rad) (full width at half maximum), and \(\theta\) is the Bragg angle position.  
The calculated crystallite sizes were 83.09 nm, 79.89 nm and 76.76 nm for the films deposited for 3 min at 60 V, 70 V and 80 V, respectively and 78.69 nm, 86.72 nm, and 95.2 nm for the films deposited for 3 min at 60 V, 70 V and 80 V, respectively. At 3 min of electrophoresis deposition time, an increase in the applied voltage led to an increase in the intensity of the peaks. Conversely, for a deposition time of 5 min, the intensity of the peaks decreased with increasing the applied voltage from 60 V to 70 V. The narrow widths of the peaks indicated good crystallinity quality for the deposited films.
###### Abstract  
In visual Simultaneous Localization and Mapping (SLAM), operational efficiency and localization accuracy are equally crucial evaluation metrics. We propose an enhanced visual SLAM method to ensure stable localization accuracy while improving system efficiency. It can maintain localization accuracy even after reducing the number of feature pyramid levels by 50%. Firstly, we innovatively incorporate the principal direction error, which represents the global geometric features of feature points, into the error function for pose estimation, utilizing Pareto optimal solutions to improve the localization accuracy. Secondly, for loop-closure detection, we construct a feature matrix by integrating the grayscale and gradient direction of an image. This matrix is then dimensionally reduced through aggregation, and a multi-layer detection approach is employed to ensure both efficiency and accuracy. Finally, we optimize the feature extraction levels and integrate our method into the visual system to speed up the extraction process and mitigate the impact of the reduced levels. We comprehensively evaluate the proposed method on local and public datasets. Experiments show that the SLAM method maintained high localization accuracy after reducing the tracking time by 24% compared with ORB SLAM3. Additionally, the proposed loop-closure-detection method demonstrated superior computational efficiency and detection accuracy compared to the existing methods.  
visual SLAM; principal direction projection; loop-closure detection; features pyramid Article  
1
## 2 Materials and Methods
### Microdosimetry Spectra Measurements Utilizing the SOI MicroPlus-Mushroom Microdosimeter  
An SOI MicroPlus-mushroom microdosimeter built upon a 3D cylindrical micron-sized SV array introduced by CMRP, University of Wollongong, Australia, was utilized in order to measure the microdosimetric lineal energy spectra [16; 17; 21; 26]. The 3D mushroom microdosimeter had an array of 400 cylindrical SVs, and each had a diameter of 18 \(\mu\)m and a thickness of 10 \(\mu\)m. The SVs were connected in a matrix 20 \(\times\) 20 SVs. All of them were connected via Al tracks as shown in Figure 1 of [27]. One track was connected to all n+ cores, and another track was connected to all p+ trenches. The 3D mushroom microdosimeter was plugged into a low-noise front-end device (also called the MicroPlus probe) developed at the CMRP, the output of which was fed to a shaping amplifier along with a shaping time of 1 \(\mu\)s [22]. Authors were not aimed for 2D spatial resolution in this research. All 400 cylindrical SVs were connected in parallel and fed to the input of a charge-sensitive preamplifier. The total data acquisition system included a MicroPlus probe with a shaper and an Amptek MCA 8000D multichannel analyser (MCA) connected to a laptop for MCA spectra visualization. For the conversion of MCA spectra to microdosimetry quantities, a software developed at CMRP was used. The low-level discriminator (LLD) of MCA was set to avoid any electromagnetic noise originating from the scanning magnet and robotic couch, which corresponded to an LLD of about \(\sim\)50 keV (Si) for low gain and \(\sim\)15 keV (Si) for medium gain. The experimental set up can be seen in Figure 1.  
The lineal energy \(y\) was calculated based on the deposited energy \(\epsilon\) using the following relationship: \(y=\epsilon/\overline{l}\), where \(\overline{l}\) is the mean path length of an SV that is 10 \(\upmu\)m [21]. Energy calibration for converting channel values to lineal energies was performed by irradiating the SOI MicroPlus-mushroom microdosimeter located behind a 10 mm thick polymethyl methacrylate (PMMA) phantom with a normally incident 170.5 MeV/u \({}^{4}\)He-, \({}^{12}\)C-, \({}^{16}\)O-, and \({}^{20}\)Ne-ion beams. The channel number corresponding to the centre of mass of the MCA peak was prescribed with energy deposited in the SV calculated by the Geant4-based Monte Carlo code [28] under the same experimental conditions. Details of the Geant4-based Monte Carlo simulation are given in Section 2.3. The same procedure was carried out for both medium and low gains on the shaping amplifier and led to the linear regression line energy vs. the channel number used to build microdosimetry spectra. A scaling factor of 0.58 was then applied for conversion of the silicon microdosimetric spectra to tissue [22; 29]. As the stopping power ratio varies over the therapeutic energy range, an uncertainty of \(\sim\)1% exists relating to a scaling factor of 0.58 [30; 31].
## 2 Materials and Methods
### Data Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Description** & **Abbreviation** & **Unit** \\ \hline Altitude & Alt & m a.s.l. \\ Aspect & Asp & cardinal orientations \\ Topographic position & Top & high slope, middle slope, valley, and ridge \\ Slope & Slo & \% \\ \hline Cover of beech & CBe & \\ Cover of common juniper & CJu & \\ Cover of mountain pine & CDp & \\ Cover of silver fir & CFi & \\ Cover of herbaceous layer & CHe & \\ Cover of larch & CLa & \\ Cover of litter & CLi & \% \\ Cover of _Rhododend ferrugineum_ & CRf & \\ Cover of spruce & CSp & \\ Cover of dwarf shrubs & CSu & \\ Cover of _Vaccinium myrillus_ & CVm & \\ Cover of _Vaccinium vitis-idea_ & CVv & \\ Cover of fine deadwood & CFD & \\ \hline Height of trees & HTr & \\ Height of shrubs & HSh & \\ Height of herbaceous layer & HHe & \\ Height of fir & HFi & m \\ Height of larch & HLa & \\ Height of spruce & HSp & \\ Height of dwarf shrubs & HSu & \\ \hline Presence of broadleaf species & PBr & \\ Evidence of silvicultural & & Presence-absence \\ interventions & & SIn & \\ Signs of grazing & SGr & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Variables used to explain changes in capercaillie presence–absence. The overall statistical model was performed following specific guidelines [34] and using the 'gbm' package 2.1.5 [39] for R statistical software [40]. We report the results of the BRT model in two ways. First, we present a histogram showing the relative influence of variables produced from the'summary.gmb' function of the R software package 'gbm'. Then, only for the variables with relative importance \(\geq 10\%\), we plot the fitted functions in the BRT model using partial dependence functions that show the effect of a variable or group of variables on carecaille summer occurrence after accounting for the average effects of all other variables in the model. BRT can be used with a variety of response types. If the response variable is binary, then the package 'gbm' of R adopts the Bernoulli logistic regression. The fitted values are the logarithms of the odds p/(1 - p) where p is the probability of carecaille occurrence for a given value of the predictor.
# Sea Surface Temperature in Global Analyses: Gains from the Copernicus Imaging Microwave Radiometer
## 4 Conclusions  
We have conducted an investigation into the effect of adding CIMR to an IR-only observing system on the global analyses of SST. Adding CIMR resulted in a reduction in the global RMSE of SST in analysis data from 0.37 K to 0.21 K for January and 0.40 K to 0.25 K for July. There were also strong improvements in dynamical regions, such as the Aghulas Current (from 0.59 K to 0.34 K in January and 0.38 K to 0.20 K in July) and Gulf Stream (from 1.29 K to 0.39 K in January and 0.51 K to 0.28 K in July). There were noticeable examples of artefacts in the reconstruction that were removed with the additional coverage provided by CIMR.  
CIMR will make an important contribution to global observations of SST and result in a large reduction in the errors in analyses. Its near-daily coverage and ability to provide SST in the presence of clouds will significantly improve the analysis reconstruction of dynamical regions where there are strong SST contrasts and in regions with persistent cloud coverage.  
Conceptualization, C.J.M. and C.D.; methodology, K.P. and C.J.M.; software, K.P., S.G. and O.E.; validation, K.P.; formal analysis, K.P.; investigation, K.P., O.E. and C.P.; resources, C.J.M.; data curation, K.P. and O.E.; writing--original draft preparation, K.P.; writing--review and editing, K.P., S.G., C.J.M. and C.P.; visualization, K.P.; supervision, C.J.M.; project administration, K.P., C.J.M. and C.D.; funding acquisition, C.J.M. This research was funded by the European Space Agency under 4000125189/18/NL/AI.  
**Acknowledgments:** The authors thank [PERSON] and [PERSON] of the National Oceanography Centre, Southampton, U.K., for access to the model simulations used in this study.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders commissioned the study and commented on interim results.
## 2 Methodologies
### Sam  
SAM pays attention to the internal correlations of input features and assigns different weights according to their importance [52]. Firstly, the input sequence \(X\) is linearly transformed into the query (\(Q\)), key (\(K\)) and value (\(V\)) using three different weight matrices \(W^{Q}\), \(W^{K}\) and \(W^{V}\), respectively. Secondly, the similarity between \(Q\) and \(K\) can be calculated, and is further normalized by a softmax function to achieve the self-attention matrix (\(W\)). Finally, the output matrix \(H\) can be obtained by multiplying the matrix \(W\) with \(V\). SAM can dynamically adjust the weights of different features and obtain the long-range dependencies of the sequences. Figure 5 depicts the structure of SAM, and the process of SAM is formulated as follows:  
\[\begin{cases}Q=W^{Q}X\\ K=W^{K}X\\ V=W^{V}X\end{cases} \tag{19}\]  
\[W=\text{Softmax}\bigg{(}\frac{QK^{T}}{\sqrt{D_{K}}}\bigg{)} \tag{20}\]  
\[H=\text{Attention}(Q,K,V)=WV=\text{Softmax}\bigg{(}\frac{K^{T}Q}{\sqrt{D_{K}}} \bigg{)} \tag{21}\]  
where \(D_{K}\) is the dimension of key \(K\) and Softmax (\(\cdot\)) is the function of normalization by column.  
Figure 5: The structure of SAM.
## 4 Methodology
### Feature Selection Methods  
Feature selection is crucial because it focuses on the information that truly matters when predicting solar power output. By narrowing the features to genuinely relevant ones, we ensure that trained models capture the most important factors without diving into unnecessary details. This approach improves model accuracy and makes the results more interpretable and computationally efficient. The first feature selection step in this study involves a variance-based method that filters out weather parameters showing little to no variation. Such low-variance parameters contribute minimal predictive value and can add noise to the model. Next, a hierarchical clustering based on correlation is performed to group parameters with redundant information. From each cluster, only one representative parameter is chosen, minimizing duplication. Finally, a meta-heuristic algorithm is applied to further refine the set of selected features by simultaneously aiming to reduce their number while maximizing the \(R^{2}\) score.
# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
###### Abstract  
NASA's carbon dioxide mission, Orbiting Carbon Observatory-2, began operating in September 2014. In this paper, we analyze four years (2015-2018) of global (60\({}^{\circ}\)S-60\({}^{\circ}\)N) XCO\({}_{2}\) anomalies and their annual variations and seasonal patterns. We show that the anomaly patterns in the column-averaged CO\({}_{2}\) dry air mole fraction, XCO\({}_{2}\), are robust and consistent from year-to-year. We evaluate the method by comparing the anomalies to fluxes from anthropogenic, biospheric, and biomass burning and to model-simulated local concentration enhancements. We find that, despite the simplicity of the method, the anomalies describe the spatio-temporal variability of XCO\({}_{2}\) (including anthropogenic emissions and seasonal variability related to vegetation and biomass burning) consistently with more complex model-based approaches. We see, for example, that positive anomalies correspond to fossil fuel combustion over the major industrial areas (e.g., China, eastern USA, central Europe, India, and the Hibveld region in South Africa), shown as large positive XCO\({}_{2}\) enhancements in the model simulations. We also find corresponding positive anomalies and fluxes over biomass burning areas during different fire seasons. On the other hand, the largest negative anomalies correspond to the growing season in the northern middle latitudes, characterized by negative XCO\({}_{2}\) enhancements from simulations and high solar-induced chlorophyll fluorescence (SIF) values (indicating the occurrence of photosynthesis). The largest discrepancies between the anomaly patterns and the model-based results are observed in the tropical regions, where OCO-2 shows persistent positive anomalies over every season of every year included in this study. Finally, we demonstrate how XCO\({}_{2}\) anomalies enable the detection of anthropogenic signatures for several local scale case studies, both in the Northern and Southern Hemisphere. In particular, we analyze the XCO\({}_{2}\) anomalies collocated with the recent TROPOspheric Monitoring Instrument NO\({}_{2}\) observations (used as indicator of anthropogenic fossil fuel combustion) over the Highveld region in South Africa. The results highlight the capability of satellite-based observations to monitor natural and man-made CO\({}_{2}\) signatures on global scale.  
carbon dioxide; satellite; remote sensing; anthropogenic; biogenic; global analysis +
Footnote †: journal: Remote Sensing, 11
## 4 Applications  
### Overview  
Meanwhile, a broad range of different applications on different components of ferromagnetic materials has been developed based on the 3 MA approach. Table 5 summarizes these applications. Subsequently, some of the main important 3 MA applications will be described in more detail. One of the earliest applications of 3 MA was the inspection of components in nuclear power plants. Such components are subjected to different ageing phenomena. For the steel components in the reactor pressure vessel, heat exchangers, pipe lines, etc., the main important aging phenomena are thermal ageing, fatigue, and neutron embrittlement. In the past, it was shown that 3 MA is an important tool in evaluating material degradation due to ageing in such components [35]. Besides neutron degradation, the superimposed impact from thermal ageing and low-cycle fatigue was investigated. In the case of austenitic stainless steels when exposed to mechanical static or cyclic loads, the material reacts with local phase transformations to generate bcc \(\alpha^{\prime}\) martensite. 3 MA can also detect such local martensite [36]. The nondestructive determination of hardness and residual stress distribution in the weld seam and in the heat-affected zone of welded components was another early 3 MA application [37]. Later, 3 MA was applied to determine the hardness and residual stress in heat-treated (turbine blades) or grinded components (e.g., bearing rings) [38; 39]. Another well-developed 3 MA application is the determination and characterization of grinding damage (\"grinding burns\") [40; 41; 42]. 3 MA is able to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. ** & **Component** & **Main Application/Description** & **Ref. ** \\ \hline \multirow{2}{*}{1} & Components in nuclear power plants & Materials damage and degradation; & \\  & & Neutron-induced embrittlement & [35; 36] \\ \hline \multirow{2}{*}{2} & Welds (laser, etc. ), turbine & \multirow{2}{*}{Hardness and residual stress} & [37; 38; 39] \\  & blades, bearing rings, etc. & & \\ \hline \multirow{2}{*}{3} & Machined parts (gear wheels, etc.)