esa-sceva/satcom-chunk-collection huggingface dataset:
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 3 Data Preprocessing and Model Training
### Model Training  
After completing preprocessing, the dataset is utilized to train and evaluate the random forest model. The root mean square error (\(RMSE\)), mean absolute error (\(MAE\)), and coefficient of determination (\(R2\)) metrics of the trained model are subsequently evaluated. The definitions of these metrics can be found in (15)-(17), where m represents the number  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Raw** & **Data** & **Downsampling** & **Training Set** & **Testing Set** \\  & **Data** & **Enhancement** & **Downsampling** & **Training Set** & **Testing Set** \\ \hline Nonprecipitation samples & 244,497 & 244,497 & 53,826 & 43,060 & 10,765 \\ Precipitation samples & 26,913 & 53,826 & 53,826 & 43,060 & 10,765 \\ \hline \end{tabular}
\end{table}
Table 2: Training and test sets generated using unbalanced data.  
Figure 6: Distribution of the training dataset, including (**a**) raw data; (**b**) augmented data, and (**c**) data acquired after performing downsampling. Red represents the new data obtained after adding Gaussian white noise.  
of test samples, \(RF\_SI\) denotes the simulated scatter index, and \(SI\) represents the true scatter index and denotes the average scatter index within the samples.  
\[RMSE=\sqrt{\frac{1}{m}{\sum\limits_{i=1}^{m}{(RF\_SI_{i}-SI_{i})}^{2}}} \tag{15}\]  
\[MAE=\frac{1}{m}{\sum\limits_{i=1}^{m}{|RF\_SI_{i}-SI_{i}|}} \tag{16}\]  
\[R2=\frac{\sum\limits_{i=1}^{m}{(SI_{i}-RF\_SI_{i})}^{2}}{\sum\limits_{i=1}^{m}{ (SI_{i}-\overline{SI})}^{2}} \tag{17}\]  
The experimental results are depicted in Figure 7. The \(RMSE\) of \(RF\_SI\) relative to the \(SI\) is 1.55 K, the \(MAE\) is 0.86 K, and the \(R2\) value is 0.94, indicating strong consistency between \(RF\_SI\) and the \(SI\) overall. In areas with intense scattering (where the \(SI\) exceeds 50 K), the average \(RF\_SI\) is found to be 5 K lower than that of the \(SI\). This discrepancy may be attributed to the complexity of the nonlinear relationship that is inherent in the intense scattering process, which the random forest model may not fully capture. Nonetheless, in the case of strong scattering, \(RF\_SI\) still demonstrates the ability to accurately identify instances of precipitation, with values exceeding 30 K being indicative of such events. This suggests that \(RF\_SI\) is well suited for precipitation detection.
# Anisotropy Corrected FMC/TFM Based Phased Array Ultrasonic Imaging in an Austenitic Buttering Layer
## 3 Experimental Details
### Phased Array Ultrasonic Measurements  
The experiments were performed using GEKKO(r) phased array ultrasonic equipment supplied by M/s. Eddyfi, France, with the maximum number of 64 simultaneous transmitting and receiving channels. A 2.25 MHz linear array ultrasonic transducer having 128 elements with an element pitch of 1 mm was used for velocity measurement using the TT technique as well as for defect characterization.  
The reason behind selecting the PA probe of 2.25 MHz with 1 mm pitch for the present study is as follows. In a coarse grain material, the material attenuation is primarily governed by grain scattering. The ultrasonic scattering coefficient due to Rayleigh scattering (when \(\lambda>>d\)) can be expressed as:  
\[\alpha_{s}=Sd^{3}f^{4} \tag{2}\]  
where \(S\) is the scattering factor that depends on the elastic anisotropy and sound velocities, \(d\) is the average grain size in the material, and f is the frequency. It can be seen from Equation (2) that the attenuation increases with the fourth power of frequency. In case of a higher frequency, the depth of interrogation is limited, whereas the resolution is improved. It demands a compromise between depth of interrogation and resolution, especially during testing a highly attenuating material [9]. It is always recommended to use a larger aperture to have better resolution. A larger aperture can be achieved either by using a larger pitch or by increasing the number of elements [29]. However, increasing pitch beyond half the wavelength (\(\lambda\) = -2.4 mm in the present case) may introduce side lobes. Hence, a transducer with 1 mm pitch is used. The data were acquired at 100 MHz and 33.3 MHz sampling frequencies for velocity measurement and defect characterization experiments, respectively. The data for velocity measurements were acquired at the highest available sampling frequency of 100 MHz in the TT mode to obtain the velocity values with the best possible accuracy. However, as longer data lengths are required for defect characterization in pulse-echo and pitch-catch modes, data were acquired at a lower sampling frequency of 33.3 MHz for defect characterization to limit the data file size. The 12-bit data acquired using the GEKKO(r) system was post-processed using specific software developed in Python.
## 4 Mission S-Net
### S Band Isl System
#### 4.3.4 Spacecraft Network Controller  
The SNC implements the higher layers, including the network layer (routing algorithm). An orbit propagator (SGP4) is implemented to estimate the position of each node, which are the input parameters for the geometric routing algorithm (see Section 3.8). The communication interface to the main OBC, which generates user data, is realized by SPI. Data communication with SLINK is implemented by SPI for data frames and RS485 for data frames and control information. In order to perform various ISL routing methods, as proposed in Section 3.8, a state machine within the SNC stores control sequences to command the SLINK terminal. Physically, the unit is based on a 32 bit controller, including a latch-up protection circuit, an external watchdog, and low-voltage protection.
## 2 System Model
### Problem Expression  
The objective of this paper is to minimize the delay and energy consumption of the whole system, that is, to minimize the total system cost. To get better results, the vehicle network task offloading is simulated. In the calculation of complex tasks, the delay and energy consumption are adaptively adjusted by linear weight factors to obtain a formula that enables a better calculation of the total system cost. We consider a weighting factor where the total cost of offloading is considered only in terms of delay or energy consumption when the weighting factor is 0 or 1. In this paper, we consider the weighted sum of delay and energy consumption of the whole task offloading system and introduce a weight factor: \(\alpha\in[0,1]\), so that different weights can be given according to the actual needs of task offloading.  
\[W=\alpha T+(1-\alpha)E \tag{18}\]  
where, \(W\) represents the total cost of collaborative offloading system.  
In this paper, the offloading of tasks and allocation of resources are formulated as an optimization problem with the aim of minimizing \(W\) in Equation (18), so that the optimization problem can be formulated as: \(\underset{S,F}{\max}\sum\limits_{i=1}^{n}W\)  
\[\begin{array}{c}s.t.C1:s_{i}^{c}s_{i}^{mcc},s_{i}^{cloud}\in[0,1]\\ s_{i}^{c}+s_{i}^{mcc}+s_{i}^{cloud}=1,\forall i\in[1,n]\\ \frac{s_{i}^{c}s_{i}^{mcc},s_{i}^{cloud}\in[0,1]}{C2:\max}\Big{(}T_{i}^{c},T_{i} ^{mcc},T_{i}^{cloud}\Big{)}\leq\min[T_{i}^{\max},T_{i}^{stay}],\forall i\in[1,n] \\ C3:\sum\limits_{i=1}^{n}f_{i}^{mcc}\leq f^{mcc},i\in[1,n]\end{array} \tag{19}\]  
\(S\) indicates the computing uninstallation policy, and \(F\) indicates the computing resource allocation, that is, \(F=\{f_{1}^{mcc},f_{2}^{mcc},\cdots f_{N}^{mcc}\}\), \(T_{i}^{stay}=\frac{s_{i}}{\forall}\), \(s_{i}\) indicates the distance from the location where the vehicle belongs to the point where it leaves the coverage of the MEC server. C1 indicates that only one uninstallation platform can be selected for each task. C2 indicates that the time to complete the task for each vehicle should not exceed the maximum tolerated time delay and the vehicle travel time within the MEC coverage area; C3 indicates the constraint on the total computing resources of the MEC server.  
The adaptability function is reciprocal of Equation (18), and the higher value of Equation (20) means that the total cost of the selected offloading scheme is smaller, the smaller the instantaneous delay and energy consumption, and the better the offloading scheme.  
\[Fitness=\frac{1}{W}=\frac{1}{\alpha T+(1-\alpha)E} \tag{20}\]
## 3 Dataset  
The radiometric data used for developing and testing the proposed models were collected from the Baseline Surface Radiation Network (BSRN) [26]. Most of the stations in this network provide _GHI, DHI,_ and _DNI_ measured at 1 min resolution. The general database developed for this study includes two datasets. The first dataset, denoted D_FIT, was used for developing the proposed models. The second dataset, denoted D_TEST, was designed for comparing the performance of the proposed models with the performance of the reference models introduced in Section 2. D_FIT contains 193,294 lines of data, recorded for one year (2020) at 1 min resolution at the station of Payerne, Switzerland (46.815 N, 6.944 E, BSRN indicative _PAY_). This location is situated in a temperate climate region classified as Cfb according to the Koppen-Geiger system [27]. D_TEST contains nearly 7 million lines. Data were collected from 36 locations spread across four climatic zones: tropical (A), arid (B), temperate (C), and continental (D). Only data measured at the Sun's elevation angle \(h>5^{\circ}\) were included in D_FIT and D_TEST. This is a common practice in research conducted with radiometric data because the pyranometer's accuracy is questionable close to sunrise and sunset. The data in D_TEST are characterized by diversity; they were recorded in locations with latitudes between \(-45\) and \(+58\) deg, and the time span extends from 2002 to 2021. Table 1 summarizes the stations' metadata. There is some inherent overlapping between the locations included in D_TEST and the origin location of the models. Thus, Payerne (_PAY_), the origin location of the proposed
## 3 Results
### Functional Enrichment Analysis
In contrast, protein-protein interaction network functional enrichment analysis for sulforaphane-underexpressed genes revealed dysregulation in extracellular matrix organization, intracellular signaling by sec  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Upreguated** & & & & & & & \\ \hline
**Gene** & **logFC** & **AveExpr** & **t** & & & & \\ \hline LINC00536 & 7.64755127 & \(-\)3.0149304 & 21.9961465 & 0.00024888 & 0.03788923 & 0.36312489 \\ IL1R2 & 7.0572737 & \(-\)3.3119119 & 38.9200915 & 2.15 \(\times\) 10\({}^{-5}\) & 0.0314058 & 1.11208622 \\ BBOX1-AS1 & 5.82578022 & \(-\)3.9274667 & 32.9561069 & 3.65 \(\times\) 10\({}^{-5}\) & 0.0314058 & 0.96501409 \\ PLEKHS1 & 5.70468219 & \(-\)3.9959986 & 13.8792101 & 0.00094197 & 0.04820704 & \(-\)0.1886544 \\ AC105177.1 & 5.62076293 & \(-\)4.0337932 & 28.649169 & 5.71 \(\times\) 10\({}^{-5}\) & 0.0314058 & 0.88104941 \\ LAMG3 & 5.49154119 & \(-\)4.1016187 & 15.2901064 & 0.00069452 & 0.04291078 & \(-\)0.0618761 \\ TINP3 & 5.4717553 & \(-\)4.1066281 & 35.425051 & 2.90 \(\times\) 10\({}^{-5}\) & 0.0314058 & 0.9555416 \\ AC20571.1 & 5.30127359 & \(-\)4.1922612 & 32.8798182 & 3.68 \(\times\) 10\({}^{-5}\) & 0.0314058 & 0.90642193 \\ AKR1C3 & 5.09234014 & 5.97048189 & 18.5451872 & 0.00040802 & 0.04045807 & 0.71525274 \\ LINC00565 & 4.88357902 & \(-\)4.402276 & 25.9281408 & 7.85 \(\times\) 10\({}^{-5}\) & 0.03315288 & 0.73875417 \\ \hline
**Downregulated** & & & & & & \\ \hline
**Gene** & **logFC** & **AveExpr** & **t** & & & & \\ \hline DCN & \(-\)5.9219991 & \(-\)3.5794442 & \(-\)13.758792 & 0.00096589 & 0.04824652 & \(-\)0.1635847 \\ MARCOL & \(-\)5.2456221 & \(-\)3.9144047 & \(-\)30.897349 & 4.49 \(\times\) 10\({}^{-5}\) & 0.0314058 & 0.94001916 \\ NKK6-1 & \(-\)4.9009606 & \(-\)4.0843505 & \(-\)26.237668 & 7.56 \(\times\) 10\({}^{-5}\) & 0  
ond messengers (strength 0.25, false discovery rate 0.0098) and cell differentiation (strength 0.55, false discovery rate 2.07 \(\times\) 10\({}^{-6}\)). Given these results, NAMPT was considered to be the main candidate that could interact with sulforaphane and be associated with these transcriptional changes. Thus, subsequent sections of this work focused on a detailed exploration of this protein. Figure 4: Protein–protein interaction network functional enrichment analysis for sulforaphane-overexpressed genes.
## 6 Discussion  
From the experimental results in Section 4, it is considered that the effect on CO\({}_{2}\) diffusion and CO\({}_{2}\) concentration in a room is insignificant under any condition of distance from a subject, height difference and position with a subject in a space with CO\({}_{2}\) concentration of about 1000 ppm, and there is no effect on CO\({}_{2}\) diffusion considering the error of the sensor used.  
Figure 14: Predicted and measured values of CO\({}_{2}\) concentration in the medium studio (2, 4, 6 subjects).  
Figure 13: Predicted and measured values of CO\({}_{2}\) concentration in the small studio (2 and 4 subjects).  
Figure 15: Predicted and measured values of CO\({}_{2}\) concentration in the large studio (2, 4, 6 subjects).  
The experimental results in Section 5 indicate that the prediction model is sufficiently useful for predicting CO\({}_{2}\) concentration in a room if the ventilation cycle is at least above 0.67 (cycle/h). According to the Japanese building code, the minimum number of ventilation is 0.5 in a human living space [14]. Therefore, this model is valid for many residential spaces. However, in an environment with low ventilation cycle, the difference from the prediction model increases, so it is necessary to use a measurement device and measure it in real-time.  
These results show that when measuring CO\({}_{2}\) concentration in a room under the condition of no external influence, it is not necessary to measure at multiple points at the same time, and it is clear that it is possible to measure CO\({}_{2}\) concentration in a room within the error of the measurement device even if it is localized. In addition, under the condition of measurement at 1-minute intervals and the accuracy of the CO\({}_{2}\) concentration sensor used, we considered that the room concentration increases near the measurement point in the same way as instantaneous uniform diffusion, albeit quasi-uniformly. [PERSON]'s equation was selected as the prediction model based on the results of this experiment. We compared the measured CO\({}_{2}\) concentration with the predicted value using the prediction model in a room with different volume on the same basement level of the same building, changing the number of subjects. As a result, the prediction model is considered to be sufficiently useful for predicting indoor CO\({}_{2}\) concentrations when the ventilation cycle is above 0.67 (cycle/h), but there is a large discrepancy between the predicted and measured values of the prediction model when the ventilation cycle is low. Therefore, it is necessary to measure CO\({}_{2}\) concentration in real-time using a device to predict CO\({}_{2}\) concentration.
## 2 Theory and Methodology
The boundary layer parameters for a NACA 0012 was benchmarked with the viscous-inviscid interaction code XFOIL [35] and the strong viscous-inviscid interactive coupling code Q\({}^{3}\)UIC [36], and compared with the measured values [13]. The comparisons (not shown) indicated that XFOIL under-predicts the boundary layer thicknesses about 30% and Q\({}^{3}\)UIC gave a good agreement with the measurement. In this study, we compute the boundary quantities of airfoils using Q\({}^{3}\)UIC. For more advanced calculations, the structure deformation in the blade torsion direction can also be considered, which will lead to changes in angle of attack. More details about the noise prediction calculation can be found in [18; 37]. In wind turbine control systems, the proportional-integral-derivative (PID) controller is widely applied to control the generator speed and blade pitch angle. Obviously, the ideal controller should be developed by using the PID controller theory. In the present study, given the practical complexity, two control strategies, of (1) full step pitch input control and (2) interpolated pitch input control, are developed, which are P-type controls. For the full step pitch input control, the blade pitch \(\beta\) is controlled at each time-step with an objective of AoA at one or more selected cross-sections, \(\alpha_{i}\), equal to its mean value in a cycle at one or more selected cross-sections, \(\alpha_{i,mean}\), as follows  
\[\beta\ =\ \frac{1}{N}\sum_{i\ =\ 1,\ldots,N}\alpha_{i}-\alpha_{i,mean}\]  
where \(N\) is the number of selected cross-sections. After approximately 100 s, the solution becomes stable. Alternatively, the interpolation pitch input control can be used and Figure 2 illustrates the concepts. The idea is to control the blade pitch in the way that (i) at the peaks or troughs of one or more cross-sections, the blade pitch is controlled as the one in the full step pitch input control; (ii) at the azimuth positions in between the blade pitch is controlled by a pitch angle interpolated from the pitch angles at the peaks and troughs in (i) according to its azimuth position. Concretely, the peaks and troughs are extracted first with their corresponding azimuth angles and the mean AoAs are obtained from the maximums and minimums (red circles in Figure 2):
# Model-Free Adaptive Control Based on Fractional Input-Output Data Model
## 2 Problem Formulation  
Consider a class of discrete-time nonlinear systems of the form  
\[y(k+1)=f(\bar{y}(k),\bar{u}(k)), \tag{1}\]  
where \(f(-)\) is an unknown, but smooth, nonlinear function, \(y(k+1)\in\mathbb{R}\) is the output, and \(u(k)\in\mathbb{R}\) is the control input. Consider the vectors \(\bar{y}(k)=[y(k),\ y(k-1),\ \cdots\,\ y(k-n_{y})]^{T}\) and \(\bar{u}(k)=[u(k),\ u(k-1),\ \cdots\,\ u(k-n_{u})]^{T}\), where \(n_{y}\) and \(n_{u}\) are unknown integer numbers.  
The problem is to design \(u(k)\), such that the output \(y(k)\) tracks a smooth desired reference \(r(k)\). Moreover, only input-output data are available for control purposes, that is, \(y(k)\) and \(u(k)\), and their preceding values.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 6 Experimental Validation
### Clustering in Static Topologies
#### 6.2.2 Experiment Procedure  
Depending on the topology under investigation, only the respective testbed nodes were active. The CHaCh prototype was started simultaneously on all devices via the Ethernet control network. At the beginning of each measurement, all nodes were already connected to each other on the base channel (single-channel mesh network). Clustering was considered complete once the last node reached phase 7 of the CHaChA phase sequence. As described in Section 6.1, no secondary interfaces were installed. All nodes applied the cluster channel selection and configuration in phases 5 and 6 only virtually and stored the result for subsequent analysis. Five channels (36, 40, 44, 48, 158) with no overlap to the base channel 149 (\(f_{c}=5745\,\mathrm{MHz}\)) were given as an exemplary cluster channel pool. For the topology sizes investigated in this work, with up to five clusters being created, this specification was sufficient. In practice, with a channel bandwidth of \(20\,\mathrm{MHz}\), up to \(19\) orthogonal channels are possible in the \(5\,\mathrm{GHz}\) band [44]. As explained in Section 4.3, the concept of CHaChA also considers re-selection of already used channels by means of a distance heuristic.  
In addition to a log file to record its actions in the phase sequence, each node produced a capture of its outgoing TCP and UDP messages using the command line tool tcpdump. At the end of each measurement, the CHaChA prototype also produced a separate status file that contained information about the time difference between program start and reaching phase 7, the node's final role, and information about its cluster (CH and cluster channel). Any files generated on the nodes were stored in a RAM file system (Linux tmpfs), so that possible influences of slow SD card write operations were avoided.  
Initialization of the mesh network and CHaChA instances, as well as automation of the experiments, were accomplished by command line scripts. These were run partly on the mesh nodes and partly on a laboratory PC connected to the test environment via the separate Ethernet control network. Following a series of measurements (series of clustering runs), the log/status files and tcpdump packet captures of all nodes were retrieved and processed. An analysis tool, developed as a student research project using the free MATLAB alternative Octave [45], was used to visualize all formed cluster constellations and to determine their respective occurrence rates in the measurement series. The grid positions of the nodes in the visualization were statically predefined. Based on each node's status information (role and cluster membership), it was highlighted and colored accordingly. From the collected log/status files and packet captures, the tool also calculated the mean clustering duration and communication overhead generated by CHaChA control messages (TCP/UDP data size and message count).
# Service for Deploying Digital Twins of QKD Networks
## 2 Background  
Simulated and virtualized models have been proving their worth for decades. NASA has been simulating its space machinery for more than 50 years, successfully finding solutions through the simulation of different scenarios, for example, to the explosion of an oxygen tank on Apollo 13 shortly after its launch. This kind of technology is considered to be the precursor of digital twins.  
A digital twin is a virtual, (i.e., softwarized) replica of a physical object, which accurately models its complex behavior without necessarily having to interact with it. It may be used to resemble a physical entity, enabling testing and experimentation with technologies of interest without the requisite of having access to, or compromising, the machinery of the physical entity. This is the same digital twin approach followed in [13; 14], for example. Having a virtual copy of the subject matter offers many advantages, such as allowing the simulation of several scenarios without having to physically build them, providing major flexibility and cost-effectiveness, or assisting in the prediction of problems of its physical twin, which allows for staying one step ahead in resolving errors in the physical entity [15; 16].  
Nowadays, digital twin technology can be found in many different fields: from manufacturing or aviation to retail, healthcare, or smart homes and cities [16; 17]. In the networking field, digital twin technology also offers interesting benefits that are now beginning to be exploited. For instance, modern networks are constantly growing either in complexity, size, number of connections, or traffic, making them difficult to support. Using digital twins, and leveraging NFV orchestration tools, the performance of large infrastructures can be estimated quickly and accurately, allowing the anticipation of when an existing network will run out of resources based on a given growth [14].  
Since QKD networks are currently not easy to build, but are a particularly attractive technological element to develop, a QKD digital twin environment may offer a powerful solution, enabling experimentation in this field without the need to have access to real quantum infrastructures. Thus, the features discussed above, including flexibility and scalability, open a door to interesting and novel studies such as the integration of quantum communications in the current network infrastructures, as it can be potentially incorporated into larger experiments on this issue.  
Building a digital twin requires a deep knowledge of the physical object to virtualize. Here, some basic concepts about QKD, along with the state-of-the-art of quantum networks are reviewed. Also, a look at quantum network simulators is taken, as it will be an essential part of virtualizing the QKD network. Lastly, a brief outline of NFV orchestration is stated.