esa-sceva/satcom-chunk-collection huggingface dataset:
# Extreme Climate Events and Energy Market Vulnerability: A Systematic Global Review
## 4 Impacts and Adaptation Strategies to Natural Events: A Review of Global Studies
### Global Studies
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Event/Phenomenon** & **Date** &
\begin{tabular}{c} **Supply Interruption** \\ **and Grid Instability** \\ \end{tabular} & **System Response** & **Proposed Strategies** & **Reference** \\ \hline Hurricanes, floods, and extreme weather phenomena & 2013 & Energy supply interruptions and grid instability due to infrastructure damage. & Change in consumption patterns, increased demand, price increase, change in energy mix. & Carbon pricing, promotion of renewable energy, stabilizing the energy market. & [13] \\ \hline Energy market risks due to climate change (clean and fossil) & 2016 & Unstable supply due to climate-induced disruptions and fluctuating availability. & Change in energy mix (higher demand for renewables), price increase. & Carbon pricing, promote renewable energy, discourage coal use. & [14] \\ \hline Geopolitical risks and climate change (Russian invasion of Ukraine) & 2012–2022 & Interruptions from war, instability in the grid due to sanctions and blockages. & Price increases and search for alternative energy sources (renewables and neutral gas). & Investment in renewable energy, energy diversification, energy supply security. & [15] \\ \hline Magnetic storms and effects on electrical transmission & 2015 & Instability due to transformer saturation from geomagnetically induced currents (GIC), thermal stress on components. & Improvement in transmission infrastructure. & & \\ \hline Natural disasters and effects on clean and fossil energy markets & 2015–2022 & Interruptions due to natural disasters affecting generation and transmission of energy. Increased volatility in the grid. & Price increase, change in energy mix towards renewables. & Energy diversification, promotion of clean energy. & [17] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Worldwide extreme weather events occurring from 2012 up to 2024 and classified in terms of their impact on the energy systems and mitigation strategies. Source: own elaboration from [13; 14; 15; 16; 17].
# _Sponias mombin_ Seed Oil Compounds Identification by Raman Spectroscopy and NMR
## 2 Materials and Methods
### ABTS Radical Scavenging Activity Assay  
The radical cation scavenging capacity of _Spondias mombin_ L. seed oil was examined against 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) radical cation (ABTS\(\bullet\)+). A stock solution was prepared by reacting 7 mM ABTS (Sigma-Aldrich, Saint Louis, MO, USA) solution with 2.5 mM potassium persulfate as a final concentration, followed by 16 h incubation at room temperature in the dark. The ABTS\(\bullet\) + stock solution was diluted with ethanol to an absorbance of 1.0 at 734 nm. Then, antioxidant activity was evaluated by incubating 150 \(\mu\)L of ethanol-ABTS\(\bullet\) + with 50 \(\mu\)L of oil solution in ethanol at different concentrations (25-500 \(\mu\)g mL\({}^{-1}\)). After 30 min, the percentage inhibition of ABTS\(\bullet\) + at 734 nm was calculated with the equation % _Inhibition = [(AC--AO)/AC] \(\times\) 100_, where _AC_ is the absorbance of the control (ABTS\(\bullet\) + solution), and _AO_ is the absorbance of the oil. Trolox and catechol were used as positive controls.
## 3 System Model
### Sum Rate
#### 3.2.3 Data Rate in the Unlicensed Spectrum  
In the unlicensed spectrum, the service level of the Wi-Fi users is the highest and cannot be ignored. To guarantee the QoS of the Wi-Fi users, D2D-U and LTE-U technologies adopt duty-cycle mechanism. We use \(P_{tr}\) to represent the probability that at least one user is in the transmitting state, i.e.,  
\[P_{tr}=1-(1-\tau)^{w}, \tag{8}\]  
where \(w\) is the number of the competing Wi-Fi users, \(\tau\) is the probability of a user transmitting in a slot time. Denote \(P_{s}\) as the successful probability that a transmission occurring on the subchannel, i.e.,  
\[P_{s}=\frac{w\tau(1-\tau)^{w-1}}{P_{tr}}. \tag{9}\]  
According to [32], the saturation rate of the whole Wi-Fi network can be expressed as  
\[R_{w,u}^{s}=\frac{P_{tr}P_{s}E[P]}{(1-P_{tr})T_{\sigma}+P_{tr}P_{s}T_{s}+P_{tr }(1-P_{s})T_{c}}, \tag{10}\]  
where \(E[P]\) is average packet size, \(T_{\sigma}\) is the duration of an empty slot time, \(T_{s}\) is the average time that the subchannel is sensed busy on account of a successful transmission, and \(T_{c}\) represents the average time that the subchannel is sensed busy by each station during a collision.  
In this mode, the CUs, D2D users and Wi-Fi users transmit on different time slots on unlicensed subchannel, and the SINR of CU \(C_{m}\) and D2D receiver \(D_{n}^{r}\) on unlicensed subchannel \(u\) can be expressed as:  
\[\gamma_{m,b}^{u}=\frac{s_{m}^{u}p_{c}h_{m,b}^{u}}{B_{u}\sigma_{N}^{2}}, \tag{11}\]  
and  
\[\gamma_{n}^{u}=\frac{s_{n}^{u}p_{n}h_{m}^{u}}{B_{u}\sigma_{N}^{2}}, \tag{12}\]  
respectively. Furthermore, let \(\rho_{c}\) and \(\rho_{d}\) represent the proportions of CU \(C_{m}\) and D2D user \(D_{n}\) in all time slots, respectively. Thus, we can calculate their data rates by  
\[R_{m,b}^{u}=\rho_{c}B_{u}\log 2(1+\gamma_{m,b}^{u}), \tag{13}\]  
and  
\[R_{n}^{u}=\rho_{d}B_{u}\log 2(1+\gamma_{n}^{u}). \tag{14}\]  
Moreover, the rate degradation of the Wi-Fi network caused by the CUs and the D2D users occupying the unlicensed spectrum can be expressed as  
\[R_{w}^{s}=(\sum_{m}s_{m}^{u}\rho_{m}^{c}+\sum_{n}s_{n}^{u}\rho_{n}^{d})R_{w,u} ^{s}. \tag{15}\]  
And the rate of Wi-Fi user \(w\) is given by  
\[R_{w}^{u}=R_{w,u}^{s}-R_{w}^{s} \tag{16}\]  
To guarantee the minimum transmission rate of the Wi-Fi users, we denote the minimum rate of Wi-Fi user \(w\) as \(R_{w}^{l}\). Thereupon, there is a threshold \(U^{s}\) for the number of the users which can access to the unlicensed spectrum, i.e., when the number of users accessing to the unlicensed spectrum reaches \(U^{s}\), the BS will reject access-request of the other users.
## 3 Materials and Methods
### Pretraining the Beat-Type Classifier on Fine-Grained Database  
To start the proposed framework, we use the MIT-BIH Arrhythmia Database (MITDB), which provides fine-grained annotations, including beat-level labels for each R peak and rhythm annotations with the corresponding onset and offset [32]. To reduce the impact of signal noise, we applied discrete wavelet transform using the Daubechies-4 wavelet, while preserving key morphological features. Unlike conventional bandpass filtering (e.g., 0.5-40 Hz), the wavelet transform attenuates noise through multiresolution decomposition, without requiring a fixed pass-band. A convolutional neural network (CNN) is then pretrained on the denoised dataset to learn the primitive features of multiple beat types, such as waveform morphology, from ECG beat segments. This model follows a 1D convolutional neural network architecture adapted from the VGG16 structure [36]. While the rhythm classification model in our framework is based on a modified VGG16 architecture for its proven performance in hierarchical feature extraction, the framework itself is compatible with alternative CNN structures. In our implementation, we employed all five convolutional blocks of VGG16, followed by a global average pooling layer and two fully  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rhythm Types** & **SBR** & **GSVT** & **AFIB** & **SR** \\ \hline No. of samples & 3889 & 2307 & 2225 & 2225 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the SPH database used in this study adapted from [34].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rhythm** & **SR** & **AFIB** \\ \hline No. of samples & 16,782 & 1514 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample distribution for the chosen rhythm types from the PTB-XL database.  
connected layers. All layers were trained from scratch. The total number of parameters is approximately 14.7 million, all of which are trainable. The remaining parameters are batch normalization statistics and model buffers.  
In our model implementation, we use 2.4-s-long beat-centered ECG segments as the input and classify them into 15 beat types annotated in the MITDB, including normal (N), premature ventricular contraction (V), and atrial premature (A) beats. A complete list of beat types used in this study is provided in Appendix A Table A1. The CNN architecture consists of multiple convolutional and pooling layers followed by fully connected layers, optimized with categorical cross-entropy loss. The beat-type classifier learned through this process becomes a kind of feature extractor that captures the intrinsic characteristics of the ECG signal for several predefined beat types. This 15-class beat-label configuration follows the standard MITDB annotations, which we also used in our previous work [34]. The weights of the pretrained classifier serve as initialization for the subsequent domain adaptation process and are later further refined to adapt to coarse-grained databases.
## 4 Results and Discussion
### Sensitivity Analysis for the Electron Temperature and the Negative Ion Density
The variations in dust particle radius are similar in magnitude to the deviations due to variations in the electron temperature; the average dust particle size variation over the measured time range is \(+9.7\,\rm nm\) and \(-11.4\,\rm nm\), for \(n_{\rm i-}=2n_{\rm e0}\) and \(n_{\rm i-}=8n_{\rm e0}\), respectively, both with respect to the default case of \(n_{\rm i-}=4n_{\rm e0}\). Similarly, the average difference in the dust density is about \(-0.44\times 10^{14}\,\rm m^{-3}\) and \(+0.95\times 10^{14}\,\rm m^{-3}\), compared to the default case. Figure 6: Sensitivity analysis for the electron temperature. Using the same raw data set, the dust particle radius (shown in panel (**a**)) and dust density (shown in panel (**b**)) are determined for an electron temperature ranging between \(T_{\rm e}=2\)–\(4\) eV, while a constant value of the negative ion density \(n_{\rm i-}=4n_{\rm e,initial}\) is assumed. These trends can be explained as follows: For a higher negative ion density, the positive ion density also increases, which follows from the quasi-neutrality condition shortly after igniting the plasma. An increase in the positive ion density \(n_{\text{i+}}\) results in a larger ion current, which allows a higher electron current at the floating potential, as Equation (6) suggests. This implies that the floating potential becomes less negative for increasing the negative ion density, and that the dust charge \(Z_{\text{d}}\) decreases for the fixed dust particle radius \(a\). For a fixed \(\Delta n_{\text{e}}\), this results in a higher dust density \(n_{\text{d}}\). From the Lambert-Beer law, it can be seen that this implies a lower dust particle radius \(a\), which is consistent with the effect of a decreasing dust charge \(Z_{\text{d}}\).
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 5 Discussion
### Performance and Comparison
Data fusion based on HLS and MODIS/VIIRS may not be necessary for the cloud-clear regions to detect cover crop termination. Further study is needed for operational applications to standardize the processing (e.g., with/without data fusion) using HLS. Machine learning approaches are suitable for detecting changes within a time-series. However, they require a large amount of ground data for training, which are hard to obtain. Unlike machine learning approaches, the WIST approach is built on the characteristics of cover crop termination anddoes not need large samples for training. The algorithm is simple and computationally efficient. In a Linux system (18 cores @ 3.07 GHz), the WIST algorithm took about 8 min to run all steps for the BARC site (1.5 million pixels). Most of the computing time was spent on the SG filter (first step). When ground truth is not available, the WIST algorithm can store the estimated termination dates, the uncertainty of estimations, the momentum of MACD, and the amplitude of NDVI for the retroactive decision or verification.
## 3 Results and Discussion
### Frequency Scaling  
The potential for scaling to higher frequencies is largely determined by ferrite performance. Table 2 shows performance parameters for low-cost NiZn soft ceramic ferrite materials and toroids designed for RF applications, such as EMI suppressors (upper group) and broadband transformers and baluns (lower), including their initial relative permeability \(\mu_{i}\), Curie temperature \(\mathrm{T_{C}}\), and loss factor. The main difference between groups is the maximum usable frequency \(\mathrm{F_{max}}\). Following [PERSON]'s limit [52], \(\mu_{i}\) falls as \(\mathrm{F_{max}}\) is raised. High-frequency performance is therefore accompanied by a reduction in magnetic field confinement and an increase in loss. Small toroids may be used to minimise the volume of magnetic material. However, careful design will be required to optimise performance, power handling will be reduced, and automated coil winding may be required for construction [53]. Despite this, the data suggest that response may be extended to the low UHF band. Above this, air-cored inductors may be used, but non-nearest-neighbour coupling and radiation will inevitably complicate the design and reduce performance.
## 2 Data Fusion for Object Localization and Identification
Figure 4 illustrates the Foe Suspected status that triggers an alarm for the guard. Figure 3: Status of _Friend with unknown ID_ (gray box). Figure 2: Fundamental modules (structure) for a mobile unit (MU) and a base station (BS). Obviously, if there are two or more objects with enemy status, the alarm cannot be cancelled and security personnel must check the unknown objects (e.g., remotely via the CCTV system or by sending a patrol in person). GPS Positioning SimulationsA simulation of the trajectory of two objects has been performed, which provides some indication of the expected intersection area if the objects can be distinguished (based on GPS positioning accuracy). Assumptions:  
* The objects can be distinguished with 90% statistical probability if the distance between them is at least 2 m;
* The objects can be distinguished with 50% statistical probability if the distance between them is at least 1.2 m;
* The speed of both objects is 1 m/s and the GPS positioning rate is 1 per second (1 Hz)--it shows the distance of 1 m between each sample in the direction of the objects' movement. Figure 5 shows the trajectories of two objects: _friend_ (red) and _foe_ (purple). Both objects are initially placed on the left side and move to the right with an assumed speed of 1 m/s. The solid lines mark the boundaries of their positions (for 90% accuracy of the GPS simulator). It can be observed that the region of overlapping positions (indistinguishability region) has, approximately, dimensions of 10 \(\times\) 10 m (about 100 m\({}^{2}\) area). This results in a positioning uncertainty of \(\pm\)5 m, which is the expected value and comparable to measurements with real GPS receivers (presented in the next chapters). Figure 4: Status examples of _Foe_ (red box), _Foe Suspected_ (gray box) and _Friend_ (green box). Figure 5: Simulated GPS trajectory with intersection region for two objects.
## 5 Experimental Setup  
Figure 7 shows the experimental setup of the slave device. The hydraulic manipulator has one servo motor and two cylinders. The hydraulic cylinder is used to actuate the 2\({}^{\text{nd}}\) link and the end effector, whereas the servo motor is used to control the base of the manipulator.  
The 3 DOF hydraulic manipulator was used to perform several experiments. The specifications of the hydraulic manipulator are listed in Table 1.  
Several previous studies have discussed identification schemes and control design strategies for hydraulic manipulators [43; 44]. Such systems include a couple of hydraulic cylinders and an AC servo motor. Usually, signal compression methods are utilized to derive dynamical equations for all of the described systems. These methods provide an equivalent impulse signal and utilize correspondingly derived functions to estimate the dynamical model [45]. This dynamical model can be represented as  
\[\ddot{x}=\frac{1}{\dot{I}_{i}}u-\frac{1}{\dot{I}_{i}}D_{i}\dot{x}i=1,2,3 \tag{38}\]  
where \(D_{i}\) = damper and \(\dot{J}_{i}\) = inertia. The values of the dynamics are presented in Table 2.  
A pictorial view of the experimental setup is shown in Figure 8. It contains the master and slave systems and the corresponding control system.  
\begin{table}
\begin{tabular}{c c c c} \hline
**S. No** & **Items** & **Specification** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Hydraulic cylinder} & Stroke = 0.2 m \\  & & Piston diameter = 0.04 m \\  & & Rod diameter = 0.022m \\  & & P\_max = 210 bar \\
2 & Hydraulic pump & Q\_max = 201/min \\
3 & Displacement transducer & Stroke = 0.2 m (\(\pm\)10 V) \\
4 & Control valve & D633-313A, Moog, Inc \\
5 & Relief valve & P\_set = 20 bar \\
6 & Control board & PC-based MMC \\ \hline \end{tabular}
\end{table}
Table 1: Hydraulic manipulator specifications.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Links** & \begin{tabular}{c} **Moment of Inertia** \\ **(Master)** \(kg\)-\(m^{2}\)** \\ \end{tabular} & \begin{tabular}{c} **Moment of Inertia** \\ **(Slave)** \(kg\)-\(m^{2}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Damper (Master)** \\ \(kg\)-\(m^{2}\)** \\ \end{tabular} &
\begin{tabular}{c} **Damper (Slave)** \\ \(kg\)-\(m^{2}\)/\(s\) \\ \end{tabular} \\ \hline
1 & 1.35135 & 303.26 & 3.99 & 17,355.5 \\
2 & 1.5 & 59.52 & 3.99 & 5241.66 \\
3 & 0.74 & 355.91 & 3.99 & 2214 \\ \hline \end{tabular}
\end{table}
Table 2: Hydraulic manipulator dynamics parameters.  
Figure 7: Slave device.  
The master/slave manipulators both have 3-links. The first link in both systems is mathematically connected to the base of the corresponding system. Reaction forces are generated at the end effector and the 2 nd link. The reaction forces are calculated/estimated based on SMCSPO. The operator at the master manipulator generates commands for the master device that are subsequently followed by the slave device, and using a personal computer, the operator can take a visual look at the master device. The design of the SMC controls the trajectory of the slave device in accordance with the movement of the master device. The GUI for visual feedback is presented in Figure 9.
# Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network
## 3 Transfer Path Analysis Test  
### Testing Arrangement  
The test mainly collects the vibration and noise signal and transfer function of the flow cabinet in different states and working conditions and analyzes the vibration and noise transmission characteristics of the transformer under test by using the transmission path. Position the microphone and accelerometer as described below, connect the data acquisition system (LMS. Test. Lab 17A, 24 channels) to the laptop computer, use the sampling analyzer FFT for spectrum analysis, and use the time recorder to record the time domain data. The sound excitation source is provided by LMS Company (Louvain, Belgium), model E-MVVFS. The main test contents are shown in Table 1.  
When the converter is not working, a microphone is used to test the background noise on the surface of the converter body. The test environmental conditions require the background noise level (SPL) to be low enough. Generally speaking, the difference between the background noise and the total noise of the test object sound source is less than 3 dB (A), so the measurement is not accurate. It needs to be corrected to between 3 and 10 dB (A). When the noise of the tested object is more than 10 dB (A) higher th the result does not need to be corrected. Install the inverter cabinet on the test bench during a quiet period (when there is no activity or little noise impact in the traction test room). The background noise at each measurement point is more than 10 dB (A) lower than the test noise, which can be seen through noise testing to be 14.88 dB (A) on average. The test results do not need to be corrected. Through the above analysis, n theoretical transfer paths (\(n=13\)) can be analyzed according to the structural characteristics and source characteristics of the transformer tank of the test object. According to the general requirements of the TPA theory above, 26 reference points are arranged in the test scheme. Figures 3 and 4 depict the measurement point layout, and the layout form is as follows. Six target spots are designated A1 through A6, each 0.8 m from the cabinet's surface; the 13 path points are designated P1-P13. Six vibration points are among them and each are transmitted twice along the beam, in the Z and X directions, with little or no effect in the Y direction, which is the beam's axial direction. Three of the four places on the fan's suspension system that convey vibration in one direction are air noise path points. The bottom air outlet is where P13 is located; to gather surface normal acceleration data, there are 26 reference points labeled I1a, I1b-I13a, and I13b, which are all positioned on the left or right sides of the 13 path points.
## 3 System Model
### Basic Assumptions  
The model operates on a 2-D map with many devices, each equipped with sensors to perform specific tasks. We make the following basic assumptions:  
* **A1**: The network structure is flat. All nodes are homogeneous with the same energy budget.
* **A2**: The positions of sensor nodes are uniformly distributed.
* **A3**: The sensing area is 2-D.
* **A4**: The transmission/sensing radii are tunable.
* **A5**: Time is asynchronous. It is hard to coordinate sensors without a central controller but, otherwise, the central controller incurs a performance penalty. So, sensors embrace asynchronous scheduling--every sensor autonomously decides its duty cycle without synchronizing with each other.
* **A6**: The sensor node can be movable.
* **A7**: Location information is unknowable.
* **A8**: Distance information is unknowable.
# Enhancing Visual Data Security: A Novel FSM-Based Image Encryption and Decryption Methodology
## 4 Results and Discussion
### Histogram Analysis  
The histogram serves as a graphical representation that delineates the pixel value distribution within an image, which fundamentally exists as a two-dimensional matrix populated with pixel values ranging from 0 to 255. This graphical tool maps the frequency of occurrence of each pixel value, offering a total of 256 possible pixel intensities. In the context of cipher images, an ideal histogram exhibits a uniform distribution, indicating that each pixel value appears with equivalent probability, thereby obfuscating any discernible patterns within the encrypted image. This study encapsulates the encryption of four distinct plain images, followed by the generation and analysis of their histograms both pre- and post-encryption, as depicted in Figure 3. The analysis reveals that the histograms of the encrypted (cipher) images manifest as uniformly distributed (flat histograms), signifying the absence of identifiable biases or statistical anomalies. Such uniformity in pixel distribution across the histogram underscores the efficacy of the encryption process, as it intuitively demonstrates the achievement of uniform pixel distribution within the encrypted images, thus ensuring the concealment of any potential patterns.