esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing an Interactive Visual Analytics System for
## Appendix A Creating a Composite Weather Station Dataset
\begin{table}
\begin{tabular}{c c c} \hline \hline
**NO** & **VAR\_NAME** & **Explanation** \\ \hline \(1\) & IDX & Index \\ \hline \(2\) & COOP\_ID & NWS Cooperative network ID, assigned by NCBI. \\ \hline \(3\) & GHCND\_ID & Populated if the station is included in GHCN-Daily product by NCBI \\ \hline \(4\) & NCDC\_ID & A unique identifier used by NCBI. \\ \hline \(5\) & NWSLI\_ID & NWS location identifier \\ \hline \(6\) & FAA\_ID & Managed by USDT Federal Aviation Administration. \\ \hline \(7\) & WBAN\_ID & WBAN identifier (Weather-Bureau-Army-Navy), assigned by NCBI \\ \hline \(8\) & WMO\_ID & ID assigned by World Meteorological Organization \\ \hline \(9\) & ICAO\_ID & Managed by the International Civil Aviation Organization. \\ \hline \(10\) & TRANSMITTAL\_ID & The official ICAO identifier managed by the International Civil Aviation Organization. \\ \hline \(11\) & TRANSMITTAL\_ID\_TYPE & ICAO or TRANSMITTAL \\ \hline \(12\) & NAME & Station name \\ \hline \(13\) & ALT\_NAME & Alternate name or alias. \\ \hline \(14\) & CITY & City listed on the LCD publication. \\ \hline \(15\) & ST & USPS abbreviation for each state \\ \hline \(16\) & COUNTY & Name of county \\ \hline \(17\) & COUNTRY & FIPS country name \\ \hline \(18\) & COUNTRY\_CODE & FIPS country code \\ \hline \(19\) & LOCATION & Station location \\ \hline \(20\) & LOCATION\_AREA & Location area \\ \hline \(21\) & NWS\_REGION & NWS region \\ \hline \(22\) & ELEV & Station elevation in feet \\ \hline \(23\) & ELEV\_GROUND & Ground elevation. \\ \hline \(24\) & ELEV\_A & Wind anemometer height in feet. \\ \hline \(25\) & ELEV\_P & Pressure sensor elevation in feet. \\ \hline \(26\) & LAT & Decimal latitude \\ \hline \(27\) & LON & Decimal longitude \\ \hline \(28\) & STNTYPE & Type of observing programs associated with the station. \\ \hline \(29\) & UTC & Time zone \\ \hline \(30\) & CALL & Federal Aviation Administration ID number \\ \hline \(31\) & CALL\_SIGN & Official FAA identifier for LCD stations \\ \hline \(32\) & BEG\_DT & Beginning date of record \\ \hline \(33\) & ENG\_DT & Ending date of record \\ \hline \(34\) & CD & Climate division as determined by master divisional reference maps. assigned by NCBI. \\ \hline \(35\) & LOC\_PREC & Indicates precision of source lat and lon \\ \hline \(36\) & LAT\_DMS & Latitude degree, minute, etc format based on LOC\_PREC precision \\ \hline \(37\) & LON\_DMS & Longitude degree, minute, etc format based on LOC\_PREC precision \\ \hline \(38\) & EL\_GR\_FT & Ground elevation in Feet. \\ \hline \(39\) & EL\_GR\_M & Ground elevation in Meters. \\ \hline \(40\) & EL\_AP\_FT & Airport: Field, Aerodrome, or Runway elevation - in Feet. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A generated composite station dataset that includes forty-seven attributes.
# Federated Distillation Methodology for Label-Based Group Structures
## 3 Materials and Methods
### Federated Distillation  
In federated distillation, each client trains a local model and communicates its knowledge to the central server. We use a one-shot method [18] in which the client sends the trained results to the server once and receives the aggregated data in return. This approach minimizes the communication overhead and accelerates the learning process. The distillation process uses the standard KL divergence loss represented in Equation (1).  
\[\text{KL}(p,q)=\sum_{c=1}^{C}p(c)\log\frac{p(c)}{q(c)} \tag{1}\]  
where \(p(c)\) and \(q(c)\) denote the predicted probabilities of class \(c\) obtained from the client and group models, respectively. Mathematically, \(p(c)=\sigma(f_{k}(x))\) and \(q(c)=\sigma(\tilde{f_{l}}(x))\). \(f_{k}(x)\) is the logit from the client's model, while \(\tilde{f_{l}}(x)\) is the averaged logit from clients belonging to group \(l\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Clustering Criterion** & **Dataset** \\ \hline Clustered FL (CFL) [7] & Similarity of \(\Delta\)W & MNIST, CIFAR-10 \\ FL+HC [12] & Similarity of \(\Delta\)W & MNIST, FEMNIST \\ Federated SEM (FedSEM) [13] & Model that minimize loss & MNIST, FEMNIST, CelebA \\ IFCA [8] & Model that minimize loss & MNIST, CIFAR-10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Federated Learning Methods that utilizes clustering. Note that FL+HC means Federated Learning with Hierarchical Clustering and IFCA means Iterative Federated Clustering Algorithm.
## 3 Materials and Methods
### Hs-SPME/GC-MS Analysis  
Initially, VOCs released by each growth media alone were analyzed in three experimental replicates using the same 150 mL crimp-top Erlenmeyers, PTFE septa, under the same shaking and temperature conditions described for the growth of the yeast. These samples were used as a medium control analysis, with the aim of differentiating between the VOCs from each broth and those released by _M. furfur_ CBS 1878 cultivated in such medium. Afterwards, VOCs released from _M. furfur_ in both exponential and stationary phases and in the four liquid media: mDixon and MM supplemented with oleic acid (OA), or oleic and palmitic acid (OA+PA), or palmitic acid (PA), were analyzed in five replicates for each experimental treatment (De, Ds, Oe, Os, OPE, OPs, Pe, Ps).  
In order to extract the widest range of polarities of VOCs, a SPME fiber of Divinylbenzene/Carboxen/Poly(dimethylsiloxane) (DVB/CAR/PDMS, 50 \(\upmu\)m /30 \(\upmu\)m, gray) (SUPELCO, PA, USA) was used. The fiber was exposed for 20 min to the headspace of the Erlenmeyer with growth media and yeast, maintained at 33 \({}^{\circ}\)C, and the agitation diminished to 90 rpm during the sampling, so that the state of the fiber was not compromised. Subsequently, the desorption process was carried out in the GC HP 6890 Series equipped with an Agilent Mass Selective Detector 5973 (Agilent technologies, Palo Alto, CA, USA) at 250 \({}^{\circ}\)C using splitless injection. Separation was performed on a BP-5 capillary GC column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, SGE, Austin, TX, USA) using helium as a carrier gas at a flow rate of 1.3 mL/min. The temperature gradient program started at 40 \({}^{\circ}\)C for0.5 min, followed by an increase to 60 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C/min, then the temperature was raised to 150 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, and finally to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and this temperature was maintained for 6 min. The GC-MS filament source and the quadrupole temperature were set at 230 and 150 \({}^{\circ}\)C, respectively. The electron ionization (EI) source was set at 70 eV, and the mass spectrometer was operated in full scan mode applying a mass range from \(m/z\) 30 to 300 at a scan rate of 2.0 scan/s. All samples, including linear alkanes, were run under the same chromatographic conditions. Linear alkanes of the series C\({}_{8}\)-C\({}_{20}\) were used for the determination of retention indexes (RI) and later for the tentative assignment of compounds.
## 2 Antenna Synthesis and Modeling  
In this section we discuss the study, the analysis, and the design of a dual circular polarized antenna array working in Ka-band, suitable for receiving and transmitting LEO satellite communications. The optimal lattice and feed tapering are reported, and an insight into the mutual coupling between array elements is provided.
## 4 Experimental Results
### Cerber
#### 4.3.2 Hardened Environment Outcome  
Upon attempting to download the Cerber ransomware in the hardened environment, the process was interrupted even before the completion of the download. This immediate halt can be attributed to the log entries, shown in Figure S8. The instant disruption is indicative of the comprehensive defense mechanisms that the hardened configuration offers, with multiple layers working in tandem to ensure the highest level of security.  
The in-transit disruption happened only if the user tried to download the malicious file using Microsoft Edge, which reveals that the security feature that came into play is part of Microsoft Edge. If the user tried to download a malicious file using the most popular browser, Google Chrome, he/she would be given the option to keep the file, like any other executable (exe) file (Figure S9).  
In the above case, it was observed that Real-time protection, which actively detected the ransomware in the default environment, took a backseat in the hardened mode. Instead, the responsibility for ransomware detection transitioned to Smart App Control as soon as the user attempted to execute the application (Figure S10). This shift in roles between the two features is not just an incidental behavior but reflects a deeper strategy in Windows 11 Pro's defense mechanisms.  
The move from the default to the hardened setting showcased a marked shift in the dynamics of Windows Defender's behavior. While Real-time protection served as the initial line of defense in the default setting, actively identifying and handling threats, the hardened environment saw Smart App Control stepping up. The logic behind this shift can be attributed to the enhanced security layers in the hardened setting. Even as Real-time protection continues its vigil, scanning files in real-time, Smart App Control operates more at the application execution juncture.  
In sum, the collaboration and prioritization of Real-time protection and Smart App Control in the hardened setting underline Windows 11 Pro's adaptability and strategic defense. It is a clear demonstration of the OS's dedication to equipping users with flexible and potent protection against modern threats.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 3 Data Preprocessing
### STFT Spectrogram  
When an analysis window of length \(M\) slips on the received signal \(r(n)\), the short-time Fourier transform (STFT) of the signal is obtained by calculating the discrete Fourier transform (DFT) of the sampled signal after windowing. Let \(L\) be a non-zero overlap length to compensate the signal attenuation at the edge of the window. Suppose \(R_{k}(f)\) is a DFT of the sampled signal centered on time \(k(M-L)\) at frequency \(f\)  
\[R_{k}(f)=\sum_{n=-\infty}^{\infty}r(n)g[n-k(M-L)]e^{-j2\pi fn} \tag{3}\]  
where \(n\) is a time sampling point and \(g(n)\) is a window function of length \(M\). \(N_{r}\) is the length of sampling signal \(r(n)\) and \(1\leq k\leq k_{\max}=\left\lfloor\frac{N_{r}-L}{M-L}\right\rfloor\).  
The square of the DFT modulus \(\left|R_{k}(f)\right|^{2}\) of each windowed segment is combined to obtain the matrix \(\left|R_{STFT}(f)\right|^{2}\), which contains the amplitude of each time and frequency point. \(\left|R_{STFT}(f)\right|^{2}\) is visualized to get STFT spectrogram  
\[\left|R_{STFT}(f)\right|^{2}=\left[\left|R_{1}(f)\right|^{2}\quad\left|R_{2}( f)\right|^{2}\quad\cdots\quad\left|R_{k_{\max}}(f)\right|^{2}\right] \tag{4}\]  
\(\left|R_{STFT}(f)\right|^{2}\) contains the amplitude of each time and frequency point. The logarithm \(R\) of energy density is obtained by  
\[R=20\times\lg\left|R_{STFT}(f)\right| \tag{5}\]  
Taking time as the horizontal axis and frequency as the vertical axis, the STFT spectrogram is obtained by using color to represent the logarithmic energy density \(R\), so that the energy changes corresponding to the time-frequency components will show different texture features.  
The selected I/Q signal under typical EMI and the corresponding STFT spectrogram are shown in Figure 5. It can be obtained that the I/Q signal can fully show the time domain characteristics of the signal, while the STFT spectrogram can simultaneously show the time-frequency characteristics. In particular, the feature differences between WGN and BPSK in the STFT spectrogram are not obvious, which will make it difficult for the model to extract specific features. Therefore, it is necessary to increase the model input, which can show the phase difference of EMI signals.  
Figure 5: I/Q signal and STFT spectrogram when the data link loses lock with the EMI signal of: (**a**) WGN with the center frequency of \(f_{0}-4\) MHz and the bandwidth of \(8\) MHz, (**b**) BPSK with the center frequency of \(f_{0}-4\) MHz and the bandwidth of \(8\) MHz and (**c**) CW with the center frequency of \(f_{0}-4\) MHz.
## 4 Model to Minimize Cost Dynamically
### Architecture and Task Processing Flow  
Figure 1 exhibits the architectural scheme on three decoupled layers under a bottom-up view:  
* IoT Layer (L1): IoT device layer generates application tasks. These devices have a limited processing capability and operate with batteries;* MEC Layer (L2): MEC server layer has a restricted number of CPUs and less processing capability than the CC environment. MEC servers are closer to the IoT devices, producing smaller communication delays;
* CC Layer (L3): CC data centers compose this layer. These servers have high processing capability, are geographically distributed, and are located far from the IoT devices. They also add high network latency due to data transmission with more communication hops, if compared to other layers.  
The model associates the cost in terms of energy consumed and elapsed time for the allocation policy of each layer, taking into account task processing and data transmissions costs. The DVFS technique is used to calculate processing costs, proving the best pair of CPU core voltage and CPU core operating frequency that reduces total cost.  
Finally, the TEMS scheduler seeks the best cost among all three allocation policies and selects the lowest one. The scheduler decides between MEC and CC layers to offload a task. Otherwise, the processing takes place on the device itself.  
The rest of this section covers an extension of the cost model of our previous work shown in [33]. First, the assumptions about the network and the architecture components are introduced. After that, the cost models for local computing in the IoT device, local computing in the MEC server, and remote computing in the cloud are shown. Finally, the individual costs are combined into a final equation that represents the total cost.  
Table 2 summarizes the notation used throughout the model definitions of network, local IoT computing, MEC server, and cloud computing.  
Figure 1: System architecture.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## 3 Results
### Validation of Up-Scaled GPP at Footprint Scale
#### 3.2.2 Validation of Up-Scaled GPP at Footprint Scale  
The weighted GPP in the footprints were obtained according to the contribution rate, and were compared with the up-scaled GPP using machine learning methods (Figure 8). We found that a good linear relationship existed between the up-scaled GPP and GPP in the footprint at flux tower sites (R\({}^{2}\) ranged from 0.80 to 0.88, and RMSE ranged from 0.89 g C m\({}^{-2}\) d\({}^{-1}\) to 1.24 g C m\({}^{-2}\) d\({}^{-1}\)). Compared with the validation against filed GPP directly, we found that the accuracy was higher when validating the up-scaled GPP at the footprint scale. R\({}^{2}\) could be as high as 0.88 and RMSE was only 0.89 g C m\({}^{-2}\) d\({}^{-1}\) of the up-scaled GPP using RF, which also demonstrates that up-scaled GPP could obtain the highest accuracy using RF.  
Figure 6: Climate footprint of GPP at flux tower sites in (**a**) Sidaoqiao, (**b**) Huazhaizi, (**c**) Huyanglin, (**d**) Hunhelin, (**e**) Dashalong, (**f**) Daman, (**g**) Shidi, (**h**) Bajintan, (**i**) Arou. The x-coordinate is the distance of footprint at east–west direction, y-coordinate is the distance of footprint at north–south direction. The gray level means cumulative weight the contribution of the footprint.
# Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods
## 3 Background, Taxonomy and Ontology
### The Concept of System Resilience
Researchers and engineers involved in the development of resilient systems have formulated a number of heuristics that should be relied on when designing resilient systems [11; 15; 18]:  
* functional redundancy or diversity, which consists in the existence of alternative ways to perform a certain function;
* hardware redundancy, which is the reservation of the hardware to protect against hardware failures;
* the possibility of self-restructuring in response to external changes;
* predictability of the automated system behavior to guarantee trust and avoid frequent human intervention;
* avoiding excessive complexity caused by poor design practices;
* the ability of the system to function in the most probable and worst-case scenarios of natural and man-made nature;
* controlled (graceful) degradation, which is the ability of the system to continue to operate under the influence of an unpredictable destructive factor by transitioning to a state of lower functionality or performance;
* implementation of a mechanism to control and correct the drift of the system to a non-functional state by making appropriate compromises and timely preventive actions;
* ensuring the transition to a \"neutral\" state to prevent further damage under the influence of an unknown destructive disturbance until the problem is thoroughly diagnosed;
* learning and adaptation, i.e., reconfiguration, optimization and development of the system on the basis of new knowledge constantly obtained from the environment;
* inspectability of the system, which provides for the possibility of necessary human intervention without requiring unreasonable assumptions from it;
* a human being should be aware of the situation when there is a need for \"quick comprehension\" of the situation and the formation of creative solutions;
* implementation of the possibility of replacing or backing up automation by people when there is a change in the context for which automation is not prepared, but there is enough time for human intervention;
* implementation of the principle of awareness of intentions, when the system and humans should maintain a common model of intentions to support each other when necessary. Thus, resilience is a system property and is based on certain principles and stages of processing disturbing influences.
## 2 Wave Model and Wave Force Calculation
### Regular Wave Model  
In the regular wave model, the wave theory based on stream function takes into account the nonlinearity of waves and has many advantages. It can be widely used and extended to any order, which has good fitting conditions and can take into account the effect of ocean currents. It can simulate the near-breaking nonlinear wave in a shallow water well. The coordinate system is established based on a regular wave diagram, as is shown in Figure 2. The intersection point between the axis of the cylinder and the mean water level (MWL) is the coordinate origin, the direction of wave propagation is set to the \(x\) axis, and the \(z\) axis is vertical upward. Moreover, \(h\) is the water depth, \(D\) is the diameter of the tower, and \(\eta\) is the height of the wave surface.  
The wave model based on stream function can be expressed as [27]:  
\[\psi(x,z)= \frac{L}{T}z+\sum_{n=1}^{N}X(n)sinh[\frac{2\pi n}{L}(h+z)]cos(\frac{2 \pi nx}{L}), \tag{1}\]  
where \(X(n)\) is the coefficient of higher order perturbation term; \(L\) is the wavelength; \(T\) is the wave period.  
Figure 1: Operating environment of offshore wind turbine.  
Figure 2: Simple schematic diagram of regular wave.  
At the same time, the wave has periodicity both in time and space, which satisfies the boundary conditions: \(\psi(x,z,t)=\psi(x+L,z,t)\), \(\psi(x,z,t)=\psi(x,z,t+T)\).
## 4 Results: State-of-the-Art Approaches
### Semantic Modeling in IoUT
#### 4.1.1 Semantic Modeling and Interoperability
Hence, in order to overtake underwater environment challenges such as refraction and reflection, which affect the quality of images, they utilized deep learning. The result was images with lower quality, not so representative of the reality, but containing accurate semantic information about the environment.
## 7 Comparison of the Feasibility of Using a Hybrid SPV/WTG system vs. a Diesel Generator  
The comparison between the traditional power system DG and the proposed SPV/WTG system is summarized into two important key aspects: (i) economic feasibility and (ii) greenhouse gases (GHG) emissions. The following discussion is based on an average daily solar radiation for South Korea of 4.0 kWh/m\({}^{2}\) and a wind speed of 4.0 m/s as a case study. However, this discussion can be extended to include other cases of solar radiation, with a slight difference in the IC, O&M, and salvage costs.  
Figure 19: Discount factor for each year of the project lifetime.  
Figure 20: Summary of all costs occurring within the project lifetime for the SPV/WTG hybrid system.
## 1 Introduction
### Research Topic, Goals, and Objectives  
This research introduces an innovative approach for monitoring the production parameters of CNC machines by utilizing real-time monitoring software version 1.0 powered by Digital Twin technology. This software solution is designed to facilitate better production control and oversight. It employs Structured Query Language (SQL) [7] to handle information transmission, ensuring that data move efficiently between the systems. Additionally, the interface is developed using.NET [8], providing a robust and user-friendly platform for operators and managers to interact with.  
One of the primary benefits of this system is its capability to continuously synchronize data [9] across different systems, facilitating the real-time monitoring of critical performance indicators.  
Ultimately, the system's capability to provide real-time insights, predictive maintenance, and continuous synchronization enables manufacturing companies to optimize their operations, maintain high levels of productivity, and fully embrace the transformative potential of Industry 4.0 technologies [10]. The implementation of such a solution positions organizations to operate at the cutting edge of industrial innovation.
## 2 Results and Discussion
### The Formation of Maleimide and Succinimide Products: Mechanism of Polymerization-Rupture
#### 2.1.2 Degree of Rate Control and Thermodynamic Control  
In order to identify the rate-determinant process of this multistep profile (Figure 1) for formation of the maleimide and succinimide, which is difficult to discern by simple observation of the pattern, the method proposed by [PERSON] et. al. [15] was used; thus, degrees of rate control (X\({}_{\text{RC}}\)) for transition states and degrees of thermodynamic rate control (X\({}_{\text{TRC}}\)) for minimum energy configurations (reactive and intermediates) can be calculated using Equations (2) and (3), respectively. The matrix of exponential terms (M\({}_{\text{jk}}\)) obtained from the corresponding Gibbs free energies is shown, in part, in Equation (1) (See the complete M\({}_{\text{jk}}\) matrix in Table S2 in the supporting information), while the corresponding results for degrees of rate controls are shown in Table 2.  
\[M_{jk}=\left(\begin{array}{ccccc}M_{11}&M_{12}&\cdots&M_{118}\\ M_{21}&M_{22}&\cdots&M_{218}\\ \vdots&\vdots&\ddots&\vdots\\ M_{181}&M_{182}&\cdots&M_{1818}\end{array}\right)=\left(\begin{array}{ccccc} 5.1\times 10^{33}&7.6\times 10^{8}&\cdots&1.2\times 10^{29}\\ 7.6\times 10^{30}&8.5\times 10^{23}&\cdots&1.8\times 10^{26}\\ \vdots&\vdots&\ddots&\vdots\\ 1.1\times 10^{13}&1.2\times 10^{6}&\cdots&2.0\times 10^{26}\end{array}\right). \tag{1}\]  
The degrees of rate constants indicate that TS13 is the most rate-influent transition state in the entire profile, with a weight of 99% in the global reaction rate (X\({}_{\text{RC,13}}=0.999\)). This transition state represents the starting point of the rupture of the oligomer, which is also the most demanding step from the mechanistic point of view (see Scheme 1), showing two bond breakings and a relatively large distance for proton transfer. Additionally, the immediately preceding intermediate of the mentioned rupture (INT12) accounts for almost 100% of the total thermodynamic rate control of the entire reaction (X\({}_{\text{TRC,13}}=0.971\)), and expresses the fact that the beginning of the rupture is definitely the most important rate-influencing process.