esa-sceva/satcom-chunk-collection huggingface dataset:
# Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid
## 3 Microgrid
The peak load is 150 kW and the basic load is 30 kW, 136 kW solar panels were installed locally, generating approximately 112 MWh per year--representing about half of the community's total annual consumption, together with a \(2\times 100\) kW and \(1\times 150\) kW diesel generators complemented by a battery energy storage system (BESS) Intensium(r) Max 20M Medium Power (IM 20M) (Saft, Jacksonville, USA), which provides 232 kWh of energy in combination with a 200 kW Power Conditioning System from ABB (ABB, Napier, New Zealand) [26; 27]. At the other extreme, microgrid is also supporting a remote community in Bolivia's Amazonian rainforest, near the border of Brazil and Peru. The remote community is not  
Figure 6: Microgrid working system. grid connected and depended on 16 MW diesel generators and 5 MW solar PV, together with a BESS comprising two Saft IM 20M Medium Power containers (Saft, Jacksonville, USA), each with 580 kWh storages and 1.1 MW peak power output to meet total 8 MW load. BESS allows the penetration of renewables to be optimized while ensuring operational excellence in terms of reliability and stability [26]. Figure 7 shown used microgrid. Experience has shown that research into renewable energy sources and increasing the efficiency of already used electricity sources is still needed. The combination of BESS and solar PV has proved its worth in practice, and it is necessary to address the issue for the design of the energy mix of sources and not only use fossil energy sources. In such a case, if we consider in the future with the use of battery systems, it is necessary to deal with their recycling, increasing their lifespan and technical parameters.
## 6 The 6 GCloHE System Architecture  
Research shows that there is no consensus on the 6 GCloHE system architecture. The three-layer architecture defines the main idea of the CIoHE, but it is not sufficient for researching the 6 GCloHE system. Healthcare applications have different requirements; therefore, the five-layer architecture is chosen to directly affect the application performance. Furthermore, when a healthcare project is implemented with cutting-edge technologies (6G Tactile Internet, CDI, IoHE) and broad application areas, the five-layer architecture is considered best. That is why we proposed the five-layer architecture. The five-layer 6 GCloHE architecture proposed is inspired by the layers of processing in the human brain and the ability of human beings to think, feel, remember, make decisions, and react to the physical environment. The proposed 6 GCloHE system architecture addresses the latency issues present in cloud-based solutions. This architecture also supports time-critical healthcare applications that require an emergency response and contains five layers, as shown in Figure 14, namely: (i) the sensing layer (perception layer) comprises the physical elements such as sensors, devices, and machines; (ii) the communication layer (transport layer) includes the communication systems such as 6G Tactile Internet; (iii) the processing layer (middleware layer) consists of communication protocols, cloud computing, and CDI software to analyze data; (iv) the application layer deals with intelligent applications; and (v) the business layer defines the business model.  
Figure 13: Linking CC, MDA, and AI for healthcare value creation.  
To capture real time data concerning the physical world, the CIoHE phenomenon is fast gaining momentum in different application domains, especially in healthcare. Inspired by the human nervous system and cognitive abilities, a set of autonomic cognitive design patterns alleviate the design complexity of smart IoHE-based systems while taking into consideration CDI and scalability management. These patterns are articulated within a model-driven methodology that we proposed in Section 4 for developing a flexible cognitive monitoring system to manage a patient's health based on heterogeneous sensors.  
The usage of the 6 GCIoHE system technology in the healthcare domain is still in its nascent stage. As such, many challenges need to be addressed by the research communities and the industry. Some challenges and the existing solutions are discussed in the next section.
# Verdet Constant of Magneto-Active Materials Developed for High-Power Faraday Devices
## 2 Characterization of the Verdet Constant as a Function of Wavelength and Temperature
### Experimental Setup for the Characterization
\(\theta(\lambda_{i},T_{j})\) represents the induced polarization rotation angle, which occurs only in the measurement with the magnetic field, and, hence, it may be easily calculated as a subtraction of the respective fitting parameters values (the initial phase shifts) obtained for the measurement with the magnetic field from the values obtained for the measurement without it. Example data are shown in Figure 3b obtained at a single wavelength and temperature for the  
Figure 3: Experimental setup for the Verdet constant characterization, detection system with an arbitrarily rotated half-wave plate [48]: (**a**) a 3D overview of the experimental setup; and (**b**) an example of data obtained at a single wavelength and temperature. Figure 2: Experimental setup for the Verdet constant characterization. \(I_{2}\)-configuration [48]. The Verdet constant data \(V(\lambda_{i},T_{j})\) are calculated from the rotation angles \(\theta(\lambda_{i},T_{j})\) using the expression \(V(\lambda_{i},T_{j})=\theta(\lambda_{i},T_{j})/\left(B_{\text{eff}}L\right)\). The effective value of the magnetic field is obtained by a numeric integration of the known longitudinal distribution \(B(z)\) of the axial magnetic field magnitude over the length \(L\) of the sample. It is assumed that the sample is placed in the maximum of the magnetic field, i.e., \(B_{\text{eff}}=\int_{-L/2}^{L/2}B(z)\text{d}z\) (the maximum is located at \(z=0\)). Using a standard error calculus, the uncertainty of the obtained Verdet constant data \(u_{V}(\lambda_{i},T_{j})\) is given by  
\[u_{V}(\lambda_{i},T_{j})=\frac{1}{B_{\text{eff}}L}\sqrt{u_{\theta}(\lambda_{i },T_{j})^{2}+\left(\frac{\theta(\lambda_{i},T_{j})}{B_{\text{eff}}}\right)^{2 }u_{B_{\text{eff}}}^{2}+\left(\frac{\theta(\lambda_{i},T_{j})}{L}\right)^{2}u _{L}^{2}}\quad. \tag{9}\]  
\(u_{\theta}(\lambda_{i},T_{j})\) is the uncertainty of the obtained rotation angles \(\theta(\lambda_{i},T_{j})\), caused by small deviations of the intensity data from the cosine-squared functions in Equations (5)-(8). \(u_{B_{\text{eff}}}\) represents the uncertainty of the effective magnetic field value given by both measurement error of the magnetic field longitudinal distribution as well as by an undesired displacement of the material sample from the maximum of the magnetic field. \(u_{L}\) is the uncertainty of the sample length \(L\).
## 4 Results
### Displacement: C-Band Versus X-Band  
The magnitude of scaled displacements is similar between the sensors although that the viewing direction between Sentinel-1 and COSMO-Skymed differs considerably (SE versus SW). The relationship with cumulative DDT is similar between the years as well as sensors contrary to the day of year relationship (Figure 6) suggesting vertical motion. This is especially exemplified by the results from the different years available from Sentinel-1. The cumulative DDT relationship varies for normal years (with total season thawing cumulative thawing degree-days close to long-term average), but an average displacement of about 3 cm is recorded around the time when the average thawing index (780, see Table 3) is reached in all years. Displacement progressed until the end of thaw season in all cases, but rates deviated after approximately day of year 200 (20 July).  
Figure 5: Map of Sentinel-1 derived displacement in cm (scaled and offset corrected for thaw before start of records based on 2017 records) and in situ measurements in cm over the Circumpolar Active-Layer Monitoring (CALM) grid for 2016. Elevation contour interval is 1 m (source [PERSON] et al. (2016)). For location of the CALM grid, see Figure 1.  
Figure 6: Average scaled displacement within the Sentinel-1 and COSMO-Skymed overlap area versus day of year (**left**) and cumulative degree-days of thawing (**right**). Sentinel-1 data from 2016 and 2018 are offset corrected for early season thaw with 2017 rates.  
The progression of average displacement with respect to DDT for the overlap area of Sentinel-1 and COSMO-Skymed shows the same behavior as over the CALM grid (which corresponds to the dominant land-cover type) (Figures 6 and 7). The rate of scaled displacement in 2016 is also similar to the in situ observations at the CALM grid in 2018 (3.9 mm) with approximately 3.7 mm for Sentinel-1 and 4.0 mm for COSMO-Skymed (over 100 DDT).
# Historical Church Bell Tower Structural Behavior Induced by [PERSON]'s Acoustic Wave Propagation
## 2 Materials and Methods  
### The San Michele Arcangelo Cathedral Bell Tower  
The San Michele Arcangelo Cathedral bell tower is a relevant architectural and cultural heritage asset of Caserta Vecchia, the historic fraction of Caserta municipality (Campania Region, Italy). Built during the 13 th century, this monumental bell tower has withstood centuries of seismic activity, with each event leaving an indelible mark on its structural integrity. Over the years, numerous restoration and conservation efforts have been undertaken to preserve its original characteristics while ensuring its structural stability.  
The tower features a square base measuring approximately 7.60 m per side, rising to a height of 31.5 m. Its foundational blocks, composed of large stones sourced from ancient Roman structures, remain visible due to the lowered elevation of the adjacent square. The structure is divided into five distinct levels, each showcasing unique architectural elements that highlight the craftsmanship of medieval builders. The main dimensions of the tower are reported in Figure 1.  
The base level, notable for its sharply arched opening, accommodates a public road, seamlessly blending architectural functionality with urban practicality. This feature reflects similar designs observed in the bell tower of the Naples Cathedral and other medieval structures. The transition between levels is marked by protruding cornices, emphasizing the vertical progression of the structure.  
The second level includes rectangular windows framed with limestone, while the third and fourth levels feature double-arched windows supported by slender marble columns. These elements are enhanced by decorative relief arches and pilasters, which add depth and texture to the facade. The fifth level transitions the tower into an octagonal shape, a hallmark of medieval architecture, and is crowned by four cylindrical turrets at its corners. These turrets are topped with conical pinnacles, completing the tower's silhouette.  
Figure 1: Main dimensional properties of the tower bell. Data in the figure are reported in units of cm.  
One of the most striking features of the tower is the interplay of Arab-Norman artistic influences. The sharply arched openings, intertwined arches, and alternating bands of light traverting and dark tuff stone create a visually compelling aesthetic. Historically, the bell tower underwent significant modifications. For example, the original spire, described in historical manuscripts as pyramid-like, was replaced with the current octagonal top and turrets. Restoration efforts in the late 20 th century addressed structural issues, such as widespread mortar degradation and delamination of the outer masonry layers from the core. Techniques like anchoring the external layers to the inner core with reinforced rods were employed to stabilize the structure.  
The bell tower houses seven bells, of which only three are rung full circle during festivities (Figure 2).
# High Voltage Ride through Strategy of Wind Farm Considering Generator Terminal Voltage Distribution
## 6 Conclusions  
Based on the voltage distribution inside the wind farm, this article analyzes the HVRT problem of wind farms under a DC blocking fault in the HVDC transmission system. By extracting the key voltage node of the wind farm and taking the key voltage node as the control target of the reactive power compensation strategy, a HVRT strategy considering voltage distribution was proposed, which enhanced the HVRT capability of wind farms. The article draws the following conclusions:  
1. With the expansion of wind farms and the increase of collection line length, the generator terminal voltage of the wind turbines downstream of the collection line is higher than that of upstream wind turbines. The further away from the grid-connection point, the smaller the voltage difference between two adjacent wind turbines.
2. By considering the terminal voltage distribution of the wind turbines inside the wind farm, a key voltage node extraction method based HVRT strategy can guarantee the HVRT requirements are fulfilled by the wind turbines.
3. Simulation results showed that the VSI can effectively evaluate the HVRT capability of wind turbines in a wind farm. The proposed HVRT strategy reduced the wind farm's ride-through index VSI, with an average of 0.056, and the average RRT was shortened to about 33 ms.  
Conceptualization: Y.Q., Z.C. and B.G.; Validation: Y.Q. and Z.Y.; Data curation: X.D.; Investigation: Y.Q. and X.D.; Methodology: Y.Q., Z.C. and Z.Y.; Original draft: Y.Q. and B.G.; Writing, Review and Editing: Y.Q. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This research is supported by State Grid Company Science and Technology Project (5230 DK200009) \"Fault Evolution and Ride through Technology of Wind Power Cluster via HVDC Transmission System\".  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 1 Introduction
### Irradiation Sources  
Figure 3 shows the spectrum of electromagnetic irradiation. Cosmic rays are composed of particularly high-energy particles and photons. Primary cosmic rays are composed primarily of 99% nuclei (about 90% protons, 9% alpha particles, and 1% nuclei of heavier elements) and about 1% solitary electrons. The energy of the cosmic rays is high up to 10\({}^{20}\) eV for the primary cosmic rays. When entering the Earth atmosphere, the primary cosmic rays collide with atoms and molecules to produce secondary cosmic particles with lower energy and electromagnetic waves, including gamma-ray, X-ray, neutrons, protons, electrons, and alpha particles. The energy of the generated gamma rays can be 50 MeV. These primary and secondary cosmic rays should damage microelectronic devices because of sufficient energy, causing soft errors in electronic integrated circuits, especially in small-scale devices. Here, several ion irradiation and electromagnetic irradiation from GeV to meV are reviewed on 2D MoS\({}_{2}\) few-layers. Physical properties of MoS\({}_{2}\) bulks and thick films are also covered for comparison with those of 2D MoS\({}_{2}\) few-layers.  
Figure 3: Spectrum of electromagnetic irradiation.  
Figure 2: Calculated band structures of (**a**) bulk MoS\({}_{2}\), (**b**) quadri-layer MoS\({}_{2}\), (**c**) bi-layer MoS\({}_{2}\), and (**d**) mono-layer MoS\({}_{2}\)[39]. The solid arrows indicate the lowest energy transitions. Bulk MoS\({}_{2}\) is characterized by an indirect band gap. The direct excitonic transitions occur at high energies at \(K\) point. With reduced layer thickness, the indirect band gap becomes larger, while the direct excitonic transition barely changes. For mono-layer MoS\({}_{2}\) in (**d**), it becomes a direct band-gap semiconductor. The horizontal solid lines in each panel indicate the valence band maximum (VBM) and the dotted lines indicate the conduction band minimum (CBM). The solid blue arrows indicate the lowest energy transitions. Reprinted with permission from Reference [39]. Copyright © 2010 American Chemical Society.
## 3 Results and Discussions
### Vacuum Impregnation Validation and Dehydration of Apple Slices Vacuum Impregnated
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatment** & **pH** & **TA (\% Citric Acid)** & **TSS (\({}^{\text{o}}\)Brix)** & **Water Activity (aw)** & **Moisture (\%)** \\ \hline Untreated control & 4.01 \(\pm\) 0.01 a & 0.18 \(\pm\) 0.01 c & 15.03 \(\pm\) 0.35 a & 0.94 \(\pm\) 0.01 b & 86.08 \(\pm\) 0.39 ab \\ T1 & 3.29 \(\pm\) 0.21 d & 0.39 \(\pm\) 0.04 ab & 9.97 \(\pm\) 0.61 b & 0.94 \(\pm\) 0.01 b & 87.81 \(\pm\) 0.34 a \\ T2 & 3.31 \(\pm\) 0.02 d & 0.37 \(\pm\) 0.03 ab & 8.83 \(\pm\) 1.65 e & 0.93 \(\pm\) 0.01 b & 85.58 \(\pm\) 0.60 ab \\ T3 & 3.38 \(\pm\) 0.15 cd & 0.43 \(\pm\) 0.01 ab & 8.20 \(\pm\) 0.10 b & 0.93 \(\pm\) 0.01 b & 84.36 \(\pm\) 0.88 ab \\ T4 & 3.32 \(\pm\) 0.01 d & 0.39 \(\pm\) 0.01 ab & 8.23 \(\pm\) 0.23 b & 0.94 \(\pm\) 0.01 c & 85.53 \(\pm\) 0.04 ab \\ T5 & 3.54 \(\pm\) 0.02 bcd & 0.45 \(\pm\) 0.01 a & 9.93 \(\pm\) 0.35 b & 0.92 \(\pm\) 0.01 e & 87.20 \(\pm\) 0.34 a \\ T6 & 3.46 \(\pm\) 0.09 bcd & 0.45 \(\pm\) 0.01 a & 9.20 \(\pm\) 0.62 c & 0.98 \(\pm\) 0.01 a & 88.01 \(\pm\) 0.15 a \\ T7 & 3.55 \(\pm\) 0.04 bcd & 0.40 \(\pm\) 0.01 ab & 9.97 \(\pm\) 1.11 b & 0.97 \(\pm\) 0.01 ab & 87.80 \(\pm\) 0.01 a \\ T8 & 3.53 \(\pm\) 0.07 bcd & 0.42 \(\pm\) 0.01 ab & 9.90 \(\pm\) 0.10 b & 0.97 \(\pm\) 0.01 ab & 87.51 \(\pm\) 0.03 a \\ T9 & 3.52 \(\pm\) 0.04 bcd & 0.37 \(\pm\) 0.02 ab & 9.33 \(\pm\) 0.30 b & 0.96 \(\pm\) 0.02 abcd & 86.13 \(\pm\) 1.33 a \\ T10 & 3.64 \(\pm\) 0.06 bcd & 0.43 \(\pm\) 0.02 ab & 9.53 \(\pm\) 0.25 b & 0.96 \(\pm\) 0.01 ab & 87.09 \(\pm\) 0.35 a \\ T11 & 3.62 \(\pm\) 0.12 bcd & 0.41 \(\pm\) 0.05 ab & 9.83 \(\pm\) 0.70 b & 0.97 \(\pm\) 0.02 ab & 85.88 \(\pm\) 0.40 ab \\ T12 & 3.49 \(\pm\) 0.09 bcd & 0.38 \(\pm\) 0.02 ab & 9.17 \(\pm\) 0.49 c & 0.97 \(\pm\) 0.01 ab & 86.52 \(\pm\) 8.44 b \\ T13 & 3.65 \(\pm\) 0.09 bcd & 0.40 \(\pm\) 0.01 ab & 9.70 \(\pm\) 0.45 bcd & 0.96 \(\pm\) 0.01 ab & 86.41 \(\pm\) 0.20 a \\ T14 & 3.55 \(\pm\) 0.03 bcd & 0.40 \(\pm\) 0.01 ab & 9.50 \(\pm\) 0.17 b & 0.97 \(\pm\) 0.01 ab & 86.86 \(\pm\) 0.55 a \\ T15 & 3.67 \(\pm\) 0.07 b & 0.36 \(\pm\) 0.02 ab & 9.07 \(\pm\) 0.05 b & 0.86 \(\pm\) 0.01 abc & 87.30 \(\pm\) 0.13 a \\ \hline \hline \end{tabular}  
* Data are presented as mean \(\pm\) standard deviation (\(n=9\)). Statistically significant differences (\(\alpha=0.05\)) among the treatments are denoted by lowercase letters in each file. TA: titralude acidity. TSS: Total soluble solids. T1–T15: The key sample numbers are presented in Table 1.  
\end{table} 
Table 5: Physicochemical properties of apple slices after vacuum impregnation of aqueous extracts of _Hibiscus sabdariffa_ calves. Figure 4: Visual appearance of apple slices before and after vacuum impregnation of aqueous extract of _Hibiscus sabkarifa_ calvces (**A**,**B**), and before and after dehydration (**C**,**D**).
## 4 Experimental Results and Analysis
### Extended Security Analysis and Formal Verification
#### 4.3.2 Formal Security Verification  
To formally validate the security of the proposed ECC-based authentication protocol, we rely on the Elliptic Curve Discrete Logarithm Problem (ECDLP), which serves as the cryptographic foundation of the scheme. The security proof is structured as follows:  
Definition of Security Model  
1. Let \(P=k*G\) be the public key generated from a private key \(k\) and the generator point \(G\) on the elliptic curve.
2. The shared session key \(K\) is computed as: \(K=k_{A}*P_{B}=k_{B}*P_{A}\) where \(k_{A}\) and \(k_{B}\) are the private keys of communicating devices \(A\) and \(B\), respectively, where the keys are chosen from selected subspaces.
3. The hardness of the Elliptic Curve Discrete Logarithm Problem (ECDLP) ensures that an adversary cannot derive \(k\) given \(P\), making key recovery infeasible.  
Security Proof of Key Exchange Protocol  
Theorem 4.1: _The proposed ECC-based key exchange protocol is secure under the assumption that the ECDLP is computationally infeasible within polynomial time._  
Proof.:
1. The adversary \(Adv\) attempting to break the key exchange must determine, \(k\) from the chosen subspace, given \(P\) (solving \(k=P/G\)).
2. This requires computing the discrete logarithm on an elliptic curve, which is known to be computationally hard.
3. Without knowledge of \(k_{A}\) or \(k_{B}\), an adversary cannot generate without solving the ECDLP.
4. Given that no known polynomial-time algorithm exists to efficiently solve the ECDLP, the security of the proposed key exchange protocol is upheld.  
Thus, the authentication mechanism remains secure against computational attacks targeting key recovery.  
Conclusion on Security Robustness  
The formal security verification and comparative analysis reinforce the robustness of the proposed ECC-based authentication scheme. By leveraging the hardness of ECDLP, incorporating secure key exchange mechanisms, and mitigating potential cryptographic threats, the model ensures strong security guarantees suitable for scalable IoT applications.
## 4 Results
### Parameterization of the SEBS
A comparison of ET Estimates between EC Measurements, the SEBS Simulations, and MODIS MOD16 Products  
By incorporating original eight-day MODIS products (NDVI, LST), eight-day GLASS Albedo products, and downscaled WRF estimates (air temperature, wind speed, air and vapor pressure, and downwelling shortwave and long-wave radiations), the parameterized SEBS was employed for simulating eight-day ET from 2000 to 2012. Comparisons amongst the eight-day average ET estimates (a total of 86 samples) during 2010 and 2011 from EC, SEBS, and MODIS products are provided in Figures 3 and 4. As compared to the two-year EC measurements, the parameterized SEBS generated a reliable eight-day average ET with \(R^{2}\) = 0.80 and RMSE = 0.21 mm\(\cdot\)day\({}^{-1}\). The MOD16-ET biased the simulations, with \(R^{2}\) = 0.35 and RMSE = 0.76 mm\(\cdot\)day\({}^{-1}\). The SEBS slightly underestimated ET during the forest growing seasons (summer and autumn) during 2010 but overestimated ET for the growing seasons during 2011. As is clear from the results, the MOD16-ET significantly underestimated ET for the growing seasons but significantly overestimated ET during the spring seasons when the SEBS matched EC measurements. Figure 4: A plot of the eight-day average SEBS simulated ET _versus_ EC measurements. Figure 3: A comparison of two-year, eight-day average evapotranspiration (ETs) betweenthe Surface Energy Balance System (SEBS) simulations and eddy covariance (EC) measurements.
# A Deep Learning Framework for Signal Detection and Modulation Classification
Through the signal detection network, we can roughly get the signal carrier frequency and start-stop time. After that, we use a series of traditional methods to convert these signals from the wideband into the baseband. To recognize MPSK, MAPSK, and MQAM signals, a multi-inputs CNNs is designed. Moreover, we adopt the signal vector diagram and eye diagram as the network inputs, which are more robust than in-phase and quadrature (IQ) waveform data and constellation diagram. This paper addresses the topic of DL based multi-signals detection and modulation classification. The main contributions of this paper are summarized as follows: (1) We propose a relatively complete DL framework for signal detection and modulation recognition, which is more intelligent than traditional algorithms. (2) We establish different signal representation schemes for several tasks, which facilitate the use of the built DL framework for detection and classification. (3) We propose a multi-inputs CNNs model to extract and map the features from different dimensions. The rest of this paper is presented as follow. In Section 2, we offer a detail introduction to the signal model and the dataset generation. Section 3 shows the DL framework for signal detection and modulation recognition. Section 4 confirms our initial experiment result from different aspects. Finally, our conclusions and directions for further research are given in Section 5.