esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
As seen, our proposed EMS communicates data under space-time constraints and is intuitively more effective than other solutions described previously that try to reach as many entities as possible and neighbor discovery overhead communication. We name our solution the Pheromone Emergency Message System For Vehicular Networks, abbreviated as PhEMS4 VN. Highlights of its operation in dynamic environments include the addition of a communication protocol over the vehicular network infrastructure for the dissemination of emergency messages (EMs, not to be confused with EMS), the communication of EMs only, and not additional control messages, and the nonmandatory knowledge of communicating entities or the concurrent run of entities' location services. The results of the experimental environments, with varying vehicular density, indicate that the total number of bytes communicated is lower than in other solutions while producing similar or identical results, achieving the more effective use of the spectrum and storage. The remainder of this paper is organized as follows: in Section 2, we explore the theoretical background and review work related to our research; a proposed reclassification of approaches that address the problem of message dissemination among vehicles in those networks is shown in Section 3; in Section 4, we provide a detailed description of PhEMS4 VN, define the system model for its operation, propose the experimental setup and performance metrics, and the observe results; finally, Section 5 includes the conclusions and future directions of this research field.
## 3 Biosynthesis of Aromatic and Isoprenic Precursors of Prenylquinones in Parasitic Protozoa
### Aromatic Head Group Biosynthesis  
The shikimate pathway, together with specific amino acid degradation, supplies the precursors required for the biosynthesis of a wide variety of compounds (folates, amino acids and preylquinones,  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Phenylquinone**} & \multicolumn{3}{c}{**Function**} \\ \cline{2-4}  & **Antioxidant** & **Mitochondrial** & **Trans-plasma Membrane** \\  & **Defense** & **Respiration** & **Electron Transport** \\ \hline Ubiquitin & [45; 57; 58; 59] & [50] & \\ \hline
1-4 Menaquinone & & [60.61] & \\ \hline
1-4 Tocopherol & [62; 63; 64] & \\ \hline
1-4 Chlorobiumquinone & & & [65] \\ \hline
1-4 Thermoplasmaquinone & & & [66.67] \\ \hline
1-4 Rhodoquinone & & [48] & \\ \hline \end{tabular}
\end{table}
Table 1: The table indicates the parasitic preylquinone functions among protozoa. For more information, check the reference section. Grey squares contain examples of previously characterized preylquinones from protozoa. White squares indicate still non-characterized preylquinones.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Organism**} & \multicolumn{2}{c}{**Aromatic Head Group Biosynthesis**} & \multicolumn{2}{c}{**Isoprenoid Side Chain Biosynthesis**} \\ \cline{2-5}  & **Via Shikimate** & **Via Amino Acid** & **Mevalonate** & **Methylerythnitol** \\  & **Pathway** & **Degradation** & **Pathway** & **4-Phosphate Pathway** \\ \hline \multirow{2}{*}{_Leishman spp._} & \multirow{2}{*}{-} & Indirect evidence [63] & Experimental & \\  & & [63] & evidence at protein & - \\  & & & level [68; 69] & \\ \hline \multirow{2}{*}{_Trypanosoma cruzi_} & \multirow{2}{*}{-} & Protein predicted & Experimental & \\  & & & & evidence at protein & - \\  & & & level [70] & \\ \hline \multirow{2}{*}{_Trypanosoma brucei_} & \multirow{2}{*}{-} & \multirow{2}{*}{Protein predicted} & Experimental & \\  & & & evidence at protein & level [71; 72] & \\ \hline \multirow{2}{*}{_Plasmodium_ spp.} & Experimental & & Indirect evidence & Experimental evidence at \\  & evidence at protein & level [73; 74; 75] & & [76; 77] & protein level [78; 79; 80] \\ \hline \multirow{2}{*}{_Gardia intestinalis_} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & Indirect evidence [81] & \\  & & & [81] & - \\ \hline \multirow{2}{*}{_Trichomonas_ vaginalis_} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & Protein predicted * & - \\ \hline \multirow{2}{*}{_Toxoplasma gondii_} & Proteins inferred & & Indirect evidence [84] & Experimental evidence at \\  & from homology & & [84] & protein level [20; 85; 86] \\ \hline \end{tabular}
\end{table}
Table 2: Isoprenic/aromatic sources in pathogenic protozoa. The presence of aromatic amino acids degradation pathways (tyrosine and phenylalanine), the shikimate pathway and the isoprenoid source in pathogenic protozoa.  
among others). The presence of these metabolic pathways in parasitic protozoa have been briefly discussed (Figure 2).
# High-Temperature Liquid-Liquid Phase Transition in Glass-Forming Liquid Pd43 Ni20 Cu27P10
## 3 Results and Discussion
### Bond Angle Distribution  
To analyze the short-range structure of atoms in the three-dimensional space, we performed a bond angle distribution (BAD) analysis [43], which provides more information on the chemical and topological orderings of the higher-order correlations. The BAD can be obtained by considering a group of three atoms, where one atom is denoted as a central atom, and the other two nearest neighbors are defined as side atoms. The BADs of Pd\({}_{43}\)Ni\({}_{20}\)Cu\({}_{27}\)P\({}_{10}\) at different temperatures are illustrated in Figure 8. The characteristic angles of ideal icosahedron clusters are 63.4\({}^{\circ}\) and 116.6\({}^{\circ}\), and the BADs of P-Cu-P change most obviously after LLPT, from an octahedron structure under high-temperature liquid to an icosahedron structure under low-temperature liquid. The remaining BADs change  
Figure 7: Temperature dependence of BOO parameters \(w_{6}\) (**a**), \(q_{6}\) (**b**), \(w_{4}\) (**c**), and \(q_{4}\) (**d**) of each element. The black dashed line represents the temperature at which LLPT occurs.  
little before and after LLPT (Figure S5), among which the BADs of Pd-Ni-Pd and Pd-Cu-Pd are in the best agreement with the icosahedral characteristics, which is also consistent with the number of Cu and Ni icosahedron-like clusters in the previous VP. The deviation of other bond angles from the characteristic angles of the icosahedron may be ascribed to the distorted icosahedral cluster composition. In addition, the hump located at a bond angle of 154\({}^{\circ}\) in P-Ni-P became clear after LLPT, indicating a change in short-range order. These results further confirm that the structure has changed and that Cu and P atoms play important roles in the LLPT process.
###### Abstract  
Soil spectroscopy is known as a rapid and cost-effective method for predicting soil properties from spectral data. The objective of this work was to build a statistical model to predict soil carbon content from spectral data by partial least squares regression using a limited number of soil samples. Soil samples were collected from two soil orders (Andisol and Ultisol), where the dominant land cover is native _Nothofagus_ forest. Total carbon was analyzed in the laboratory and samples were scanned using a spectroradiometer. We found evidence that the reflectance was influenced by soil carbon content, which is consistent with the literature. However, the reflectance was not useful for building an appropriate regression model. Thus, we report here intriguing results obtained in the calibration process that can be confusing and misinterpreted. For instance, using the Savitzky-Golay filter for pre-processing spectral data, we obtained \(R^{2}=0.82\) and root-mean-squared error \((RMSE)=0.61\%\) in model calibration. However, despite these values being comparable with those of other similar studies, in the cross-validation procedure, the data showed an unusual behavior that leads to the conclusion that the model overfits the data. This indicates that the model should not be used on unobserved data.  
chemometrics; SOC; spectral diffuse reflectance; partial least squares regression; cross-validation +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Pearson Correlation Coefficient and Root Mean Square Error  
The deformation rate derived from different methods with respect to each pixel in major deformation areas were compared using the Pearson correlation coefficient (\(r\)) and the root mean square error (_RMSE_). During the estimation, we only considered the common valid pixels taken from the deformation map of three InSAR methods. The Pearson's \(r\) is often measured as a correlation coefficient with 0 indicating no linear relationship between the variables, +1 indicating a perfect increasing linear relationship and vice versa [44].  
\[r=\frac{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)\cdot\left(y_{i}-\overline {y}\right)}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}\cdot\sum_{ i=1}^{n}\left(y_{i}-\overline{y}\right)^{2}}} \tag{1}\]  
where n is the total number of common valid pixels of the selected reference points, \(x\) and \(y\) (\(\overline{x}\) and \(\overline{y}\) mean values) are the values of the pixels taken from the selected reference points.  
The _RMSE_ values can be used to compare the individual model performance to that of other predictive models; it is often used in the field of geosciences, where researchers use the _RMSE_ for model errors and comparison [45; 46; 47].  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(X_{m,i}-X_{s,i}\right)^{2}}{n}} \tag{2}\]  
where \(n\) is the total number of common valid pixels of the selected reference points, \(X_{m,i}\) and \(X_{s,i}\) are the deformation rate values of the reference points taken from the deformation map of two InSAR methods, respectively.
# Role of Blockchain Technology in Combating COVID-19 Crisis
## 4 Results
### Applications of Blockchain Technology to Fight against COVID-19
#### 4.4.5 Online Education and Secure Certification  
Since many schools and universities have closed in different countries due to the quarantine, this does not mean stopping, or having an intermission in, the education process. Education is irreplaceable and making it an online operation is a challenge itself. Moreover, there are some associated challenges such as poor cross-platform collaboration and difficulty verifying students' credentials (degree, transcripts, and other certificates) [3].  
Blockchain-based online education platforms can mitigate these challenges [3]. Decentralized blockchain-based storage can provide access to authorized users while keeping the data of others secure [3]. As known as a blockchain, it gives secure issuance and sharing of verifiable educational credentials where one of the issuing authorities of an institute uploads a credential to a blockchain-based system [4]. One of the best examples is that the MOODLE platform, a global blockchain-based network, offers the mentioned advantages [3].
# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
## 2 WEC Device
### WEC Dynamics  
The dynamics of a WEC operating in heave can be described by considering all forces acting on the system as shown in Equation (1):  
\[m\cdot a=F_{\text{exc}}+F_{\text{rad}}+F_{\text{hydro}}+F_{\text{ fric}}-F_{\text{pto}} \tag{1}\]  
where \(m\) is the mass of the WEC, \(a\) is the vertical acceleration of the WEC, \(F_{\text{exc}}\) is the excitation force exerted by incoming ocean waves, \(F_{\text{rad}}\) is the radiation force modeling the reactive effect of water due to the movement of the WEC, \(F_{\text{hydro}}\) is the hydrostatic restoring force reflecting the balance of buoyancy and gravity, \(F_{\text{fric}}\) is the frictional force from internal damping within the WEC system, and \(F_{\text{pto}}\) is the PTO force, which opposes the motion of the WEC to extract energy [4].  
For maximum power absorption, mechanical impedance matching is applied by aligning the PTO impedance \(Z_{\text{pto}}(\omega)\) with the complex conjugate of the intrinsic mechanical impedance of WEC, \(Z_{i}(\omega)\)[18], where  
\[Z_{i}(\omega)=B(\omega)+b_{f}+j\bigg{(}\omega(m+A(\omega))-\frac{k_{h}}{ \omega}\bigg{)} \tag{2}\]  
The WaveBot's dynamics are driven by a combination of wave excitation forces, PTO forces, and the system's intrinsic mechanical impedance. The linear equation of motion for vertical displacement in response to wave-induced forces is given by  
\[F_{\text{exc}}V(\omega)-F_{\text{pto}}V(\omega)=Z_{i}V(\omega)\cdot V(\omega) \tag{3}\]  
Here, \(B(\omega)\) represents the radiation damping, \(A(\omega)\) denotes the added mass, and \(\omega\) is the angular frequency of the oscillatory motion. This impedance is a function of the device's hydrodynamic properties and acts as a frequency-dependent resistance to vertical motion. The optimal condition for power transfer is achieved by setting  
\[F_{\text{pto, opt}}(\omega)=Z_{\text{pto}}(\omega)V(\omega)=Z_{i}^{*}(\omega) V(\omega) \tag{4}\]  
This optimal control law can be approximated in sea states dominated by one frequency as a proportional-integral feedback law  
\[F_{\text{pto, opt}}(\omega)\approx Z_{\text{pto,PI}}(\omega)V(\omega)=\left( K_{P}+\frac{K_{I}}{jw}\right)\,V(\omega) \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Unit** \\ \hline Rigid body mass, \(m\) & 875 & kg \\ Hydrostatic stiffness, \(k_{h}\) & 2.44 & kN/m \\ Linear friction coefficient, \(b_{f}\) & 1 & Ns/m \\ Gear ratio, \(N\) & 12.4666 & rad/m \\ Shaft inertia, \(J_{d}\) & 2 & kg m\({}^{2}\) \\ Torque constant, \(k_{\tau}\) & 6.1745 & Nm/A \\ Motor winding resistance, \(R_{w}\) & 0.5 & \(\Omega\) \\ \hline \end{tabular}
\end{table}
Table 1: WaveBot physical parameters.  
To maximize power transfer, we approximate \(Z_{\text{pto,PI}}(\omega)\) to match \(Z_{i}^{*}(\omega)\) within the range of frequencies where the wave excitation is significant. This approach results in the following conditions for the PI controller gains:  
\[K_{P}=B(\omega)+b_{f} \tag{6}\]  
\[K_{I}=\omega^{2}(m+A(\omega))-k_{h} \tag{7}\]
## 3 Materials and Methods
### Proposed Method  
As illustrated in Figure 3, the proposed architecture is based on the UNet model, known for its effectiveness in medical image segmentation tasks. We enhance the segmentation performance for liver and tumor images by incorporating residual connections and a dual- attention mechanism into the UNet architecture. Adding residual connections in both the encoder and between the encoder and decoder addresses the vanishing gradient problem. This is achieved by allowing the model to skip connections in each encoder section and the RDAM block, promoting smoother gradient flow and improving convergence during training. For the attention mechanism, our approach introduces a novel dual-attention mechanism that focuses on critical areas within the feature map, enabling the model to prioritize relevant information for segmentation. The attention process operates over multiple input scales. It begins by converting input feature maps into patches and tiles, creating a structured representation. Attention functions are applied independently to each channel, enabling the model to assign weights to spatial locations and channels based on their importance. The model employs a two-stage attention strategy: the first stage feeds feature map information into extra layers, while the second directly processes the feature maps. This ensures comprehensive attention across all relevant regions. Finally,  
Figure 1: The pipeline of the pre-processing stage.  
Figure 2: The structure of the lightweight CNN.  
the attention weights from both channels are fused, creating a consolidated representation that captures the most crucial features. Additionally, the residual connection in the RDAM block further ensures smooth gradient flow during the dual-attention process. This combination of residual connections and dual-attention mechanisms addresses the challenges of missing pixels in the outer regions of the liver and border pixel misclassification by efficiently extracting the features, even in small regions. Residual connections promote efficient learning through improved gradient flow, while the dual-attention mechanism selectively focuses on salient features, enhancing the model's accuracy in identifying liver areas. These elements collectively improve the segmentation process.
# Synthetical Modal Parameters Identification Method of Damped Oscillation Signals in Power System
## 3 Validation of SMPI with Simulated Signals
### Parameter Matching  
According to the modal parameter identification results obtained by the SSI and Prony algorithm, they are paired with each other according to the principle of the similar frequency, whose detailed procedure has been illustrated in Section 2.4.  
If the relative error between \(f_{s}\) and \(f_{p}\) is less than 0.005, it is accepted that \(f_{s}\) is one of the natural frequencies of DLFO, and then the corresponding modal parameters can be found with respect to the natural frequencies. Using parameter matching, there are three sets of similar frequencies have been matched successfully, that is, 0.3318 Hz, 0.7807 Hz, and 1.0072 Hz which have been determined as natural frequencies of DLFO. And other corresponding modal parameters are the modal parameters of true modes in these three groups, respectively, summarized in Table 4.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 4 Case Study
### Scenario Description for the Case Study
Secondly, based on the sample data of planned output power, a neural network model is trained to obtain a control input model, which allows controllable loads to follow the intra-day rolling optimization dispatch plan after receiving real-time control instructions. * Scenario 4: Based on the rolling optimization and dispatch plan within the day of the industrial park and considering the response characteristics of the controllable loads, real-time predictive control is performed on the controllable loads.
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 3 Data and Methods
### Methods
#### 3.1.4 Potential Sources of Distortion of SAR and Optical Remote Sensing  
Snow cover classification with optical and SAR remote sensing methods can be affected by various factors such as cloud coverage (only relevant for optical sensors) and dense forest canopy, among others (for more detailed information, see Appendix A). To address these challenges, we assessed the probability of cloud cover presence (from here on: PCPI) pixel-by-pixel on a monthly basis within each mountain range, applying a similar procedure to the one used for the calculation of the PSCPI. The PCPI is defined as the ratio of days with cloud cover to days with available data (non-filled data), representing the average occurrence of cloudy conditions over the 23-year period of records. Additionally, we evaluated the average probability of forest density at 10 m elevation intervals within each mountain range.
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 3 The Proposed Secure and Authentic Sensor Cloud Architecture  
This section presents a brief introduction of the proposed secure sensor cloud architecture of IoT based on WSNs. The detail of its algorithms is to be argued in the subsequent subsections. In the first algorithm, network nodes are divided into centroid-based regions and various clusters are represented based on the nodes locality. Unlike other solutions that compute centroid values for the consideration of position factor, our proposed algorithm makes use of both distance and energy factors in weighted means to determine the centroid vectors. Accordingly, quantifiable analysis is being exploited based on the network status. Afterward, the generated clusters are self-organized by using computed centroid vectors and an appropriate cluster head is selected within each cluster to gather and forward sensors' data towards the BS. In the second algorithm, the sensed data is stored on cloud infrastructure for further processing and retrieving purposes, which results in decreased computational overhead and improved network lifetime. Furthermore, to secure sensors' data that is stored on cloud infrastructure against malicious threats, the proposed algorithm gives a lightweight security scheme based on the OTP mechanism. The proposed security scheme exploits exclusive-OR (XOR) bitwise operation by the integration of both data bits and random secret keys. The random secret keys are made using a pseudorandom number generator (PRNG) algorithm [63; 64], which produces a sequence of bits that are random and never be reused. Due to this mechanism, malicious nodes cannot be able to detect the patterns of key bits. Accordingly, due to the randomness of secret keys, the computed OTP requires the least workout and perfectly secures data transmissions against mischievous entities. Moreover, the proposed solution ensures data integrity and authentication based on message authentication code (MAC) among cluster heads and BS. The block diagram of the SASC is depicted in Figure 1.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 4 Security Analysis
### Perfect Forward Security  
In the proposed protocol, since \(r_{1}\),\(r_{2}\),..., \(r_{n}\) and \(r_{s}\) are randomly selected and independent among protocol executions, a compromised password \(pw_{i}\) does not yield any previous session keys \(SK_{m}=h(GID_{im}||sk_{i,m})\) for \(G_{im}\), where \(sk_{i,m}=T_{r_{1}r_{2}\cdot r_{5}}(x)\text{mod}p\cdot T_{r_{2}\cdot r_{3}\cdot r _{5}}(x)\text{mod}p\cdot\ldots\cdot T_{r_{n}\tau_{1}\cdot r_{5}}(x)\text{mod}p\). The session key security is based on the ECM-DH problem. Accordingly, the proposed protocol provides perfect forward security.
## 2 Dual-Port MIMO Antenna
### Geometry and Design Evolution
This final modification combined with parametric analysis allows the antenna design to effectively cover the full Ultra-Wideband (UWB) spectrum. Figure 5 shows the fabricated prototype equipped with 50-ohm SMA connectors.