esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discuss
### Prospects, Limitations and Future Work  
During the derivation of the formula, it is assumed that rigid test blocks experience pure rolling on a flat slope, without considering the possibility of collisions, jumping, and fragmentation during the rolling process. It also does not account for the presence of significant irregularities and obstacles on the slope, limiting its applicability. When estimating the rolling speed of rockfalls in practical engineering applications, the formula's utility is constrained by the difficulty in ensuring the smoothness of natural slope surfaces. Collisions, jumps, and fragmentation on the slope can lead to actual rolling speeds leaning towards or even below the calculated low values. Additionally, for smaller-sized rockfalls, the rolling friction coefficient of the slope in the formula may not accurately quantify the impact of the slope on the rockfall's rolling speed. In future research, this study will explore the mechanisms through which different shaped test blocks are influenced by collisions, jumps, and fragmentation, providing a formula that quantifies the relationship between rockfall shape and these motion characteristics.
## 3 Results
### Monte Lema (Near Lugano) Radar at an Altitude of 1626 m  
The _Monte Lema_ radar (lat.: 46.0408\({}^{\circ}\); long.: 8.8332\({}^{\circ}\)) is located at an altitude of 1626 m near Lugano. Six Sun-tracking observations are available so far: the corresponding values are listed in Table 3. The average solar flux of these five different days is 21.98 \(\pm\) 0.50 dBsfu. As expected, the dispersion associated with the radar-retrieved values is larger: \(\pm\)0.52 (\(\pm\)0.54) dB for the horizontal (vertical) polarization. In absolute terms, the retrieved average value by means of the radar H (V) channel is -0.18 (\(-\)0.27) dB smaller, which corresponds to an average mutual bias between H and V equal to +0.09 dB. Similarly to radar WEI, Lema also shows a small underestimation. For a more accurate estimate, we have to subtract the noise component from the Sun signal (Section 4) and eventually compensate for clear sky atmospheric attenuation.
## 2 Study Area and Data
### Study Datasets
#### TanDEM-X 90 m DEM (TDX90)  
The TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X) is an earth observation radar mission to acquire a precise 3D global Earth's land surface elevation dataset with homogeneous quality and unprecedented accuracy [12; 37]. This goal was achieved by extending the TerraSAR-X (TSX) mission by a second satellite of TanDEM-X (TDX), which flew in twin formation with baseline distances of typically 300-500 m. Thus bi-static interferometry is realized by transmitting pulses from the antenna of one of the satellites and receiving the backscattered signals simultaneously from two satellites. This reduces or removes the impacts of significant temporal decorrelation and atmospheric disturbances of the traditional repeat-pass InSAR on the acquisition of highly accurate cross-track interferograms [11; 38].  
The twin-satellite constellation TSX and TDX acquired the data for the global DEM from December 2010 to January 2015, and the finalization of the global DEM dataset is in September 2016. In 2018, the TanDEM-X 90-m DEM was released by DLR (Deutsches Zentrum fur Luft-und Raumfahrt) to the scientific community and it is now freely available. TanDEM-X 90-m DEM was derived from the global DEM with a 0.4 Arc seconds posting (approximately 12 m at the equator) [39; 40]. The DEM product with 12-m spatial resolution is not free of charge; thus, we assessed the freely available TanDEM-X 90-m DEM product in this paper, which was downloaded from the website provided by German Aerospace Center (DLR) ([[https://geoservice.dlr.de/web/dataguide/tdm90/](https://geoservice.dlr.de/web/dataguide/tdm90/)]([https://geoservice.dlr.de/web/dataguide/tdm90/](https://geoservice.dlr.de/web/dataguide/tdm90/))).
## 8 Application of CGRR Index in Power System Operating Decisions
### System Operation Using CGRR Criterion
The total regulating margin is increased to 355 MW, and the resulting CGRR is 0.002742, as shown in the third row of Table 9. A re-dispatch is again considered since the CGRR still exceeds the criterion value. An economic load transfer evaluation is carried out by transferring part of the load from the class A units 11, 12, 13, and 14 to class B units 8, 9, and 10. The economic re-dispatch of the 14 generating units to lower the operating risk is shown in the tenth column of Table 8. The new CGRR is 0.000058, which is within the acceptable criterion. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Unit**} & \multirow{3}{*}{**MCR**} & \multirow{3}{*}{ \begin{tabular}{c} **Ramping** \\ **Capability** \\ \end{tabular}  } & \multicolumn{3}{c}{**13 Units ELD**} & \multicolumn{3}{c}{**13 Units**} & \multirow{3}{*}{**14 Units ELD**} & \multicolumn{3}{c}{**14 Units**} \\  & & & & & & & **Re-Dispatch** & & & **Re-Dispatch** \\ \cline{3-10}  & & & & & & & & & & & \\ \cline{1-1}  & 50 & 50 & 50 & 0 & 50 & 0 &  
Table 9 summarizes the CGRR results obtained during the evaluation process using the recursive algorithm of Figure 4. The total regulating margin and the operating costs associated with the different schedules considered during the evaluation process is also shown in the table. The study results show that the dispatch of 13 generating units that are committed using the N-1 criterion to meet the load cannot satisfy the CGRR criterion. The CGRR criterion can only be satisfied by committing an additional generating unit with an increased cost, as shown in Table 9. The table also shows the increase in operating cost as the dispatch is changed from ELD in order to decrease the operating risk. The first row represents the operating decision that would be taken using the existing deterministic methods, which results in an operating cost of 19,505 $/h. The operating risk is however very high. The fourth row in Table 9 represents the operating decision taken using the proposed probabilistic methods, which results in a low operating risk that satisfies the CGRR criterion. The operating cost is however increased by 250 $/h. Table 9 also shows the CGRR and cost results when two additional units are committed. It can be seen that this operating decision is not justified by the high operating cost. The results from this study show that an increase in operating reliability or a decrease in CGRR can be achieved with an increase in operating costs. A useful contribution of the proposed approach is that it can quantify the operating reliability benefit that can be compared with the operating cost, and therefore provide useful indicators to value-based operating decisions.
# Vibration Energy Harvesting from Plates by Means of Piezoelectric Dynamic Vibration Absorbers
## 1 Introduction
A strategy was proposed to minimize the vibrations of the host structure and to maximize the harvested power. Experimental results dealing with the effect of a cantilever piezoelectric dynamic absorber mounted on a vibrating rigid beam were presented by [PERSON] et al. in 2021 [21]. [PERSON] et al. in 2022 [22] extended the previous analyses by considering the coupling between a flexible beam and a cantilever harvester. Both the cantilever harvester and the flexible beam were modeled by adopting the modal expansion approach. The number of modes of vibration of the host structure and of the cantilever harvester that were needed to accurately model the system's dynamics was investigated. Results showed that considering only the first mode of the host structure and of the cantilever harvester is enough to capture the main features of the system. In 2012, [PERSON] and [PERSON] [23] improved the analysis of a 2-DOF system composed of a host vibrating structure and an energy harvesting DVA by considering both random excitation and system uncertainties. Results showed that uncertainties in the natural frequency lead to a decrease in the harvested power. Recently, [PERSON] et al. [24] presented experimental results dealing with a flexible beam equipped with a cantilever harvester and excited by a shaker. They developed a detailed integrated mode taking into account shaker dynamics. Good agreement between experimental and analytical results was obtained. The effect of a piezoelectric DVA on a vibrating plate was not addressed in the above-mentioned research. Hence, the first aim of this paper is to study vibration energy harvesting from a vibrating plate by means of a cantilever piezoelectric dynamic vibration absorber (CPDVA). The second aim of this paper is to study the performance of a CPDVA installed on a plate excited by distributed loads that are very common in actual structures. The third aim of this paper is to make a comparison between the performances of a simple piezoelectric layer and a CPDVA mounted on the same plate. The paper is organized as follows. In Section 2, the mathematical model of a vibrating plate with a CPDVA is developed: plate dynamics are modeled with the modal expansion approach, whereas the harvester is modeled with a lumped parameter approach that takes into account the first mode of vibration. In Section 3, some calculated results are presented that show the effect of harvester tuning and positioning on the generated voltage. The experimental setup is described in Section 4. A prototype CPDVA was built using a commercial cantilever harvester M2814P2C-01 made by Smart Material GmbH. This device was installed in a clamped-clamped-free-free plate excited by concentrated and distributed loads. Section 5 deals with experimental results and focuses on the comparison between the voltage generated by the CPDVA and the one generate by a simple piezoelectric layer directly bonded to the plate. Finally, conclusions are drawn in Section 6.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 2 Characteristics of the 5G System  
Until now, the implementation of next generations of mobile networks has involved, inter alia, the use of new radio techniques or the addition of new network elements. However, the roll-out of 3G was not dependent on whether the operator was already providing 2G services or not. These technologies work well together, but are functionally independent of each other. It is a bit different with 5G systems. The operation of this technology is functionally strongly related to the LTE (4G) network. The signal range that can be achieved using higher frequencies that are intended for 5G is important, such as the so-called C-Band, i.e., the 3400-3800 MHz range, or the mmWave (millimeter wave), i.e., 26-28 GHz [2; 6; 7; 8].  
The 3.6 GHz band allows the use of MIMO (multi-input multi-output), and at the same time is a compromise between propagation and capacity resulting from spectral resources, especially in combination with the 700 MHz band improving uplink. This band would be used to build a cover layer for eMBB (enhanced mobile broadband) services for several of the largest Polish cities, including communication routes between them. This band can also be used to introduce services requiring reliable transmission and a particularly low latency (URLLC--ultra reliable low latency communications) in applications requiring the transmission of particularly large amounts of data, e.g., high-definition images for medical or navigation purposes.  
The 28 GHz band has a limited area of use, especially due to also meeting the requirements for transmission from the user to the base station (\"uplink\"). It can be used for broadband internet hot spots and pico cell cMTC (massive machine-type communications)/URLLC applications. Due to its large capacity and the possibility of allocating large spectrum resources, this band can also be used to provide internet access as part of the fixed wireless access service.  
An important conclusion resulting from the assumptions for the 5G system is that the reduction of propagation losses at the 3.6 GHz and 28 GHz frequencies takes place mainly in the base station thanks to the higher antenna gain and other techniques. Consequently, for 3.6 GHz (compared to 1.8 GHz) the downlink will have better coverage than the reverse uplink. The phone has limited dimensions and limited power, so it cannot use the same optimization procedures as the base station [9; 10].  
To sum up, the implementation of the fifth-generation network in the NSA (non-standalone) model requires close cooperation between 4G and 5G technologies. An important element of cooperation is the fact that 4G uses lower frequency bands with better propagation properties. In the SA (standalone) model, the higher frequency bands also need the range support of the lower frequencies--with the difference that 5G technology is also activated on the lower band [4; 5; 11].
## 2 Sensor  
### Electromechanical Resonator  
An oscillating cantilever with the quality factor \(Q\), the angular resonance frequency \(\omega_{\text{res}}=2\pi f_{\text{res}}\), and the angular driving frequency \(\omega=2\pi f\) can be described by the differential equation of motion of a damped driven harmonic oscillator  
\[\frac{1}{\omega_{\text{res}}^{2}}\frac{\text{d}^{2}z(t)}{\text{d}t^{2}}+\frac{1 }{Q\omega_{\text{res}}}\frac{\text{d}z(t)}{\text{d}t}+z(t)=\hat{Z}\cos(\omega t) \tag{1}\]  
where \(z(t)\) is the time-varying position of the cantilever tip and \(\hat{Z}\) is the deflection amplitude [30] (p. 454). Solving for the differential equation's steady state results in the well-known solution in the form of the complex-valued unitless frequency response of a damped driven harmonic oscillator [30] (p. 454) [31] (p. 427) [32]  
\[G(f)=\frac{1}{1-\left(\frac{f}{f_{\text{res}}}\right)^{2}+j\frac{f}{f_{\text{ res}}Q}}=|G(f)|\cdot e^{j\;\gamma(f)} \tag{2}\]  
where \(j^{2}=-1\) is the imaginary number. For low-loss electromechanical resonators (\(Q>100\)) that are operated in the vicinity of their resonance frequency, the expression for the frequency response can be simplified, yielding  
\[\hat{G}(f)=\frac{1}{2\left(1-\frac{f}{f_{\text{res}}}\right)+j\frac{1}{Q}}=| \hat{G}(f)|\cdot e^{j\;\gamma(f)} \tag{3}\]  
with the amplitude frequency response  
\[|\hat{G}(f)|=\frac{1}{\sqrt{4\left(1-\frac{f}{f_{\text{res}}}\right)^{2}+ \left(\frac{1}{Q}\right)^{2}}} \tag{4}\]  
and the phase frequency response  
\[\hat{\gamma}(f)=\arctan\left(-\frac{1}{2Q\left(1-\frac{f}{f_{\text{res}}} \right)}\right). \tag{5}\]  
Although the simplified frequency response (Equation (3)) is not a good approximation of the original frequency response (Equation (2)) far outside the resonance frequency, both the amplitude frequency response and the phase frequency response are well reproduced in the vicinity of the resonance frequency, as shown in Figure 2 for the exemplary cantilever that is described in more detail below in Section 2.3.  
Figure 2: Comparison of amplitude (**a**) and phase frequency responses (**b**) between the original expression for the frequency response of a damped harmonic oscillator (Equation (2)) and the simplifiedexpression according to Equation (3). In the vicinity of the resonance frequency, the simplified frequency response is a good approximation for quality factors \(Q>100\). The depicted frequency responses are based on the parameters specified in Table 1.
# Overview of Distributed Machine Learning Techniques for 6G Networks
## 5 Distributed Machine Learning for 6G
### Federated Distillation  
Despite the fact that FL is intrinsically efficient in terms of communications, it nevertheless necessitates the sharing of huge models. A significant number of parameters are frequently used in modern DNN architectures (e.g., the GPT-3 model [66; 67] is the latest architecture for language processing operations, and has 175 billion parameters, which corresponds to about 350 GB) [68; 69]. The complete exchange of parameters is a costly operation that obstructs real-world communications, especially when primary resources are limited. Federated distillation was developed in order to address this issue.  
Federated distillation only swaps the outputs which are significantly smaller than the model. In a classification procedure, for example, each device executes its own local iterations for each class while saving the average value of the model output. These local average outputs are then loaded into the parameter server at regular intervals, and the local outputs for each class are then aggregated and averaged. The global result of the average of the outputs is then downloaded from each device, which then executes its own local iterations with its own loss function, and a regulator that measures the difference between its output prediction and the global average, to pass it to its local model. _Knowledge distillation_ (KD) is the name given to this approach of regulation [3].
## 3 Methodology  
### Data Classification Framework  
Our proposed framework for sensitive data classification and grading consists of four key modules, as illustrated in Figure 3. These modules are the preprocessing module, the classification and grading module, the result presentation module, and the comprehensive analysis module.  
The preprocessing module is responsible for classifying data into two categories: structured data and unstructured data. Depending on the data type, the classification and grading module applies different processes. For structured data, we designed a specialized model called the structured sensitive data classification and grading model (UP-SDCG). On the other hand, for unstructured data, we devised both coarse-grained and fine-grained data classification and grading schemes. The coarse-grained approach is based on text classification, while the fine-grained approach relies on entity recognition. The result presentation module displays unstructured data with details such as the Category, Type and Entity Type of the text, presented with granularity ranging from coarse to fine. For  
Figure 3: Data classification framework. The highlighted module in the figure is the main focus of this work.  
structured data, the module inputs the classification and grading results for each column. Finally, in the comprehensive analysis module, we utilized the obtained classification and grading results to build data asset maps and other capabilities that provide users with a deeper understanding of the data landscape.  
In the following sections, we present our proposed model, UP-SDCG, which focuses on the classification and grading of structured sensitive data.
# An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
## 5 Conclusions  
The research work discussed in this paper focuses on key distribution schemes for static and dynamic wireless sensor networks. The novelty of this scheme is that the proposed key distribution and update strategy is particularly suitable for sensing networks in which the nodes are in motion. In addition, we evaluate the design scheme in terms of key storage capacity and the communication load generated during key exchange and security. Compared to the traditional classical key distribution scheme, our proposed new scheme is less complex to implement, reduces the cache capacity requirements of the nodes, and obtains better connection security and resistance to attacks. It can be concluded that our results are particularly suitable for wireless mobile sensing networks with high capacity, low power consumption, and high reliability requirements, such as environmental monitoring networks, energy IoT networks, and smart warehouse management systems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scheme Features** & **[PERSON] [30]** & **[PERSON] [32]** & **[PERSON] [34]** & **[PERSON] [40]** & **Our Scheme** \\ \hline Public-key encryption & — & \(\surd\) & \(\surd\) & — & \(\surd\) \\ \hline Key pre-distribution & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline Mobility of sensors & — & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) \\ \hline Perfect resilience against node capture & \(\times\) & — & — & \(\times\) & \(\surd\) \\ \hline Mutual authentication & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) \\ \hline Resistant to eavesdropping attacks & — & — & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline –: Not involved. \(\surd\): Support. \(\times\): Not Support. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Security comparisons of different key distribution solutions.  
Figure 8: The probability of resilience against sensor capture in different schemes.  
**Author Contributions:** Conceptualization, Y.C. and Y.L. ([PERSON]); methodology, Y.C.; software, Y.L. ([PERSON]); validation, Y.C. and Z.Z.; formal analysis, Y.C.; investigation, Y.L. ([PERSON]); resources, Y.C. and Y.L. ([PERSON]); data curation, Y.L. ([PERSON]); writing--original draft preparation, Y.C.; writing--review and editing, Y.C.; supervision, Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Research Startup Foundation of Jinling Institute of Technology under Grant number [JIT-B-201726].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.