esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table
## 5 Proposed Solution
### Dynamic Blockchain Table (DBT)  
The dynamic blockchain table (DBT) is a new mechanism to protect the data and identification of IoT devices. It will solve the two main issues in the blockchain to be suited and compatible with the IoT devices specification and does not overhead the IoT network. First, it will preserve the data security of the content of the blockchain by using multipart AES encryption. Second, it will use the dynamic table that will be changed with the data which are being generated by the IoT devices.  
Combining both technologies provides a full mechanism to protect the data and prevent intruder attacks. The main idea of the proposed system, as shown in Figure 3, is to generate unique identifiers for each device and protect the data at the same time. At the beginning, the device within the network generates a unique random array of 256 bytes and stores this within its data. When the device senses new data and needs to send it to the cloud, the first step is to convert the data into a separate set of bytes, then XOR merges the data and the array it generated to form a new array. The new sensed data are merged with the new array to be a single data block. This block represents the data that will be added to the chain, thus dispensing with the complex processing of hashing algorithms and at the same time creating a unique device identifier.
# Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links
## 4 The Rainfall Rate Estimation Model
### Approximate Solution to the Inversion Problem  
When considering that an analytical inversion of \(g\) is not possible, the rain rate can be alternatively expressed as:  
\[R=q(A_{R},\ h_{R},\ k,\ \alpha)\approx g^{-1}(A_{R},\ h_{R},\ k,\ \alpha)\ [\text{mm}/\text{h}], \tag{3}\]  
where \(q\) is an approximation of \(g^{-1}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coefficient** & **Recommendation ITU-R P.838-3** & **Local DSD Data** \\ \hline \(k\) & 0.0924 & 0.0495 \\ \(\alpha\) & 0.9989 & 1.0870 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(k\) and \(\alpha\) fitting coefficients at 19.701 GHz.  
After investigating the trend of (2) conditioned to a given value of \(h_{R}\), and while using the specific power law coefficients of the site (listed in the third column of Table 1), it turns out that \(g^{-1}\) can be accurately approximated by the following third order polynomial \(q\), which is a function of \(R\) and \(h_{R}\):  
\[q(A_{R},h_{R})=a(h_{R})\cdot A_{R}{}^{3}+b(h_{R})\cdot\ A_{R}{}^{2}+c(h_{R}) \cdot A_{R}{}^{\text{ [mm/h]}}, \tag{4}\]  
The three regression coefficients \(a\), \(b\) and \(c\) are the following functions of \(h_{R}\):  
\[a(h_{R}) = 0.0057\cdot h_{R}{}^{-1.8649}+0.0014, \tag{5}\] \[b(h_{R}) = 0.2055\cdot e^{-0.3901\cdot\ h_{R}}-0.0243,\] (6) \[c(h_{R}) = 5.7255\cdot h_{R}{}^{-1.3657}+0.9172. \tag{7}\]  
Figure 7 compares \(g^{-1}\), as calculated by implementing the expressions in [19], and \(q\), obtained by using Equations (4)-(7), for different values of \(h_{R}\). Table 2 lists the indicators that were used to evaluate the fitting accuracy of \(q\). In particular, we have calculated the residual \(\varepsilon=\hat{x}-x\) (\(\hat{x}\) and \(x\) represent the values of the fitting curve and of the input data points, respectively), and the following statistical indicators: the average (\(E\)), the R-Square (\(R^{2}\)), and the Root Mean Square (RMS). The fitness accuracy values in Table 2 were obtained by averaging results over 200 different curves (as an example, Figure 7 shows four of them), to consider a broad range of possible \(h_{R}\) values, from 1.65 km up to 8 km. Regarding the coefficients \(a\), \(b\), and \(c\), as the \(E\) and RMS values of the fitting errors are infinitesimal, they have been rounded to zero.
## 3 Industrial Application
Table 5 shows the ICTOT postural criticality indices calculated through the integration of computer vision and FIE for all the EO\({}_{1}\)s, with the respective criticality classes. With the results obtained using this system, the ergonomic manager can prioritize the corrective actions required for EO\({}_{12}\) and EO\({}_{14}\). In recent efforts to reduce ergonomic risk and move towards intelligent production and Industry 4.0, cobots have been increasingly utilized, particularly for manual operations in production systems. In such systems, human operators and robots collaborate safely on different work activities. Hence, the dexterity and cognitive abilities of human operators can be effectively integrated with the repeatability of operations and payload capacity of robots to achieve high productivity and flexibility, reduce ergonomic risk, improve safety and lower costs. Regarding EO\({}_{12}\), we can replace the machine with an automatically operated press to reduce the criticality index and improve the ergonomic quality. Moreover, EO\({}_{14}\) is improved by implementing a collaborative robot that helps the operator to position the assembled component in the box, as shown in Table 6.  
To verify the benefits of implementing the cobot and to answer RQ1, the new values of the total criticality index are calculated. Table 7 summarizes the results obtained in terms \begin{table}
\begin{tabular}{c c c} \hline \hline
**Elementary Operation** & **Corrective Actions Proposed** & **Corrective Action** \\ \hline \hline EO\({}_{14}\) & Implementation of a collaborative robot & **Corrective Action** \\ \hline \hline EO\({}_{14}\) & Implementation of a collaborative robot & **Corrective Action** \\ \hline \end{tabular}
\end{table} 
Table 5: ICTOT values. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Elementary Operation** & **ICOT** & **Criticality Class** \\ \hline EO\({}_{1}\) & 0.69 & Medium \\ EO\({}_{2}\) & 0.69 & Medium \\ EO\({}_{3}\) & 0.7 & Medium \\ EO\({}_{4}\) & 0.65 & Medium \\ EO\({}_{5}\) & 0.41 & Medium \\ EO\({}_{6}\) & 0.44 & Medium \\ EO\({}_{7}\) & 0.7 & Medium \\ EO\({}_{8}\) & 0.63 & Medium \\ EO\({}_{9}\) & 0.60 & Medium \\ EO\({}_{10}\) & 0.33 & Low \\ EO\({}_{11}\) & 0.39 & Medium \\ EO\({}_{12}\) & 0.77 & High \\ EO\({}_{13}\) & 0.44 & Medium \\ EO\({}_{14}\) & 0.833 & High \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ICTOT values. of criticality indices and classes, demonstrating that the cobot reduces the criticality class of the EO\({}_{14}\) operation to low; thus, immediate action is not necessary. Therefore, through the application of our methodology, it is possible to address RQ1, and we propose that the introduction of cobots to replace certain EO\({}_{1}\)s is an effective solution to reduce ergonomic stress.
## 5 Conclusions  
This study focuses on the mechanical properties and microstructure of the expansive soils improved with lignin fibers and lime composites. The following conclusions can be made:  
1. The addition of lignin fibers significantly increases the compressive strength of the expansive soils, with an optimum fiber content of 8%. In contrast, the mixture treatment of 8% lignin fibers and 2% lime further elevates the compressive strength of the soil by approximately 50% compared to the soil treated with 2% lime alone.
2. Similarly, the 8% lignin fiber-treated sample presents a maximum soil cohesion of 210 kPa. As for the mixture treatment, the soil treated with a composite of 2% lime and 8% lignin fibers has a cohesion increase of 12% compared to the soil treated with 2% lime alone.
3. The treatment with lignin fibers increased the hydraulic conductivity of the soils by increasing the porosity of the soil. However, the lignin fiber- and lime composite-treated soil shows a relatively lower hydraulic conductivity than the soil only treated with lime, which can be attributed to the denser microstructure after the composite treatment.  
Figure 21: Scanning electron microscopy of composite-improved soils with different contents of lignin fibers and 2% lime: (**a**) lignin fiber content of 0% (1000 times), (**b**) lignin fiber content of 2% (1000 times), and (**c**) lignin fiber content of 8% (1000 times).  
4. The microstructure investigations indicated that the treatment of lignin fibers can enhance the soil microstructure by creating a strong network that fills the pores between soil particles. Furthermore, the addition of lime will improve the microstructure through cementitious materials, which adhere to the surfaces of the fibers and form bonds between the fibers and the soil particles. These mechanisms contribute to a significant increase in the soil strength.
5. The use of lignin fibers and lime, either alone or in combination, can significantly improve the hydro-mechanical properties of expansive soils. It provides an ecological and sustainable potential method of reusing industrial byproducts in the field of soil stabilization.
6. Future research should include further tests such as bearing ratio and water stability tests to enhance our understanding of soil performance under different conditions. Additionally, exploring the variability in soil properties like water content and dry density across different environmental settings will be crucial for applying these findings to real-world scenarios.  
Writing--original draft, T.W.; writing--review and editing, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 2 Methods
### Image Detection Model Based on Deep Learning  
The YOLO series models, grounded in deep learning, are capable of performing object detection tasks. This paper introduces three prevalent attention mechanisms (Convolutional Block Attention Module (CABM), Coordinate Attention (CA), and Squeeze-and-Excitation (SE)) into the original YOLOv5 model. These mechanisms are, respectively, embedded into the layer preceding the SPPF (Spatial Pyramid Pooling-Fast) layer within the Backbone module. Consequently, three instrument localization models are derived: CABM-YOLOv5, CA-YOLOv5, and SE-YOLOv5, the structures of which are illustrated in Figure 2. In Figure 2, the CBS (ConvBNSiLU) module is responsible for extracting image features and is one of the fundamental modules used to build the backbone network of YOLOv5.  
Figure 1: The Schematic Diagram of the Research Methodology for This Study.  
As Figure 2 indicates, upon receiving image data, the Backbone module employs attention mechanisms to further mine effective instrument features from the instrument feature map extracted by the original model in different ways:  
1. The CABM module computes attention maps for channel and spatial dimensions of the input feature map consecutively, and fuses them with the input feature map in sequence, achieving the goal of highlighting instrument features [21].
2. The CA module conducts average pooling on the incoming feature map across the width and height dimensions, proceeds with a fusion process, then enlarges the results to secure attention weights for both axes, subsequently applying these weights to the input feature map to bolster the efficiency of extracting instrumental features [22].
3. The SE module compresses, then excites, the input feature map followed by a calibration operation, making the model more sensitive to instrument features [23; 24].  
The image features extracted by the Backbone, along with their corresponding semantic information, are uploaded to the Neck module. This module performs feature fusion between top-level and bottom-level features to further enhance the representational effectiveness of the instrument characteristics. Ultimately, this process yields multi-class object detection data information, referred to as Detect.
## 1 Introduction
Finally, as an analysis tool of the proposed model, averaged dynamical maps will be used, which have already been used in studies considering SRP [22; 38] along with plots of the temporal behavior of some orbital elements of the space debris, such as its orbital eccentricity, to show that the solar sail contributes considerably to the orbital excitation of space debris in the GEO regime, promoting its relocation and disposal in a graveyard orbit or pushing the debris back into the atmosphere of the Earth. Finally, according to [36], the application of proper elements has been used to identify families of asteroids and more recently, they have been used in the context of space debris. The authors describe the dynamics of space debris considering the proper elements, taking into account a model that includes the gravitational attraction of the Earth, the influence of the Sun and Moon, and the solar radiation pressure. In general, we observed that when the SRP is taken into account the presence of perturbations destroys completely the symmetry of the orbits.
## 4 Case Study
### Island Partition Verification
In PSCAD, a power supply restoration experiment was conducted on the island scheme of this paper and that of [22], obtaining the absolute percentage of voltage fluctuation at various nodes in the distribution system, as shown in Figure 9, but the voltage fluctuation of nodes without power supply in island operation was not included in the statistics. From Figure 9 and Table 7, it can be seen that the maximal voltage fluctuation and average voltage fluctuation produced using the island scheme proposed in this paper at the time of power supply restoration were both lower than those produced using the scheme in [22]. Therefore, the island scheme in this paper, while ensuring a relatively high power supply restoration rate, has effectively lowered the transient fluctuation of normally running nodes at the time of power supply restoration and uplifted the stability of the distribution system in terms of power supply. In other words, if there is a large voltage fluctuation during the power restoration process, it may cause the power grid to operate at an unstable point. Furthermore, if the protection devices are triggered by this voltage fluctuation, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Total Restoration/kW** & **Primary Load/kW** & **Secondary Load/kW** & **Tertiary Load/kW** \\ \hline Method in this paper & 2358.69 & 410.95 & 1712.24 & 235.5 \\ [8] & 2031.86 & 172.67 & 1694.07 & 165.12 \\ [22] & 2378.19 & 410.95 & 1817.74 & 149.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of island partition load restoration results. Figure 9: Comparison of voltage fluctuations at power supply-restored nodes. the outage may happen again. Thus, we conclude that the proposed island partition method can enhance the stability and safety of the power grid during the power restoration process effectively.
# Experimental Study of the Performance of a Novel Vertical-Axis Wind Turbine
## 3 Experiment
### Calculations  
The angular speed of the AVAWT rotor is calculated from the measured RPM:  
\[\omega=\pi\text{ RPM/30} \tag{13}\]  
The rotational power is calculated from the angular speed and the measured torque \(T\):  
\[P_{rot}=T\omega \tag{14}\]  
The actual power \(P_{actual}\) is reckoned from the measured electrical power and the rotational power:  
\[P_{actual}=\frac{P_{rot}+P}{2} \tag{15}\]  
The aerodynamic power \(P_{aero}\) is given by Equation (10):  
\[P_{aero}=\frac{1}{2}\sigma AU_{1}\left(U_{0}^{2}-U_{2}^{2}\right) \tag{16}\]  
The actual and aerodynamic power coefficients \(C_{p,actual}\) and \(C_{p,aero}\) are given by  
\[C_{p,actual}=P_{actual}/P_{\max} \tag{17}\]  
\[C_{p,aero}=P_{aero}/P_{\max} \tag{18}\]  
respectively. The tip-speed ratio is given by  
\[\lambda=\frac{\omega R}{U_{0}} \tag{19}\]  
where \(R\) is the radius of the AVAWT rotor and \(\omega\) is its angular speed in rad/s. The induction factor \(a\) at the AVAWT inlet is given by  
\[a=\frac{U_{0}-U_{1}}{U_{0}} \tag{20}\]  
The average induction factor \(\overline{a}\) is given by  
\[\overline{a}=(1/5)\sum a \tag{21}\]  
The multiplication factor \(m\) on the induction factor at the AVAWT exit and the average multiplication factor \(\overline{m}\) are given by  
\[m=\frac{U_{0}-U}{aU_{0}} \tag{22}\]  
\[\overline{m}=(1/5)\sum m \tag{23}\]  
respectively.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 1 Introduction  
The forthcoming advent of sixth generation (6G) cellular networks will mark not only a progression of current technologies but also a profound paradigm shift in communication principles. This endeavor aims to support ubiquitous sensing, seamless connectivity, intelligence, and the integration of innovative services with wireless devices.  
Joint sensing and communication (JSC) holds great promise in increasing both spectrum and energy efficiency with acceptable hardware costs and complexity by sharing infrastructure, resources, and signals between communications and sensing [1, 2]. It demonstrates the potential to foster communication-assisted sensing and sensing-assisted communication, thereby enhancing both functions to optimally support emerging applications within the 6G landscape. These applications encompass areas such as intelligent transportation systems, the protection of vulnerable road users (VRUs), traffic and environmental monitoring, and safety in urban scenarios, among others [3, 4].
## 3 Methodology
### Parametric Case Study  
In order to investigate building response due to accidental torsion computed using both dynamic and static analysis as specified by building codes, a set of 72 of multi-story buildings are considered. All buildings are symmetric with respect to both \(X\) and \(Y\) axes. The horizontal rigid floor diaphragm is constrained in two lateral directions by vertical lateral resisting elements. Parameters relevant to site and buildings under consideration are summarized in Table 1.  
Buildings modeled for investigation are classified into three groups based on floor plan aspect ratio \(b\dot{r}\) defined by Equation (8). The first group includes a set of buildings with \(b\dot{r}\) = 1.1, the second with \(b\dot{r}\) = 1.4, and third group with \(b\dot{r}\) = 2.45. Each of the three \(b\dot{r}\) values consists of a set of buildings having a range of frequency ratios \(\Omega\) varying from flexible structural systems systems (\(\Omega\) = 0.6\(\sim\)0.7) to stiffer systems (\(\Omega\) = 1.4\(\sim\)1.7). This range of frequency ratios with each aspect ratio \(b\dot{r}\) is obtained by placing the lateral resisting elements at various locations from the building perimeter (stiffer system) to building core (flexible system). Each group of buildings having the same \(b\dot{r}\) ratio also includes three different ranges of lateral frequency in the direction of ground motion. The first range includes buildings with \(Ty<1\) s, the second range include buildings with \(1<Ty<2\) s, while the third range includes buildings with \(Ty>2\) s. Indicative floor plans of buildings are shown in Figures 2-4 respectively. In summary, the 72 case study buildings are divided into three sets (\(b\dot{r}\) = 1.1, \(b\dot{r}\) = 1.4, and \(b\dot{r}\) = 2.45); in each of the three sets there are eight building sets with varying \(\Omega\) from 0.7 to 1.4; and each building with a specific \(\Omega\) is modelled with three different periods. The response is measured by calculating normalized edge displacements \(\hat{u}_{b/2}\) using static and dynamic analyses implemented using the commercial program _ETAB_, produced by _CSI_ America, Inc.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Site and Building Data** & **Value** \\ \hline Site class & \(C\) \\ Seismic design category & \(C\) \\
0.2 s spectral acceleration (\(S\)s) & 0.6 \\
1 s spectral acceleration (\(S1\)) & 0.18 \\ Risk category & _II_ \\ Response modification factor (\(R\)) & 5 \\ Deflection amplification factor (\(Cd\)) & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Site and Building Parameters.
## 5 Case Studies: Validating the CILS Framework
### Case Study Two: HelloTalk
#### 5.2.3 Improving Communicative and Cultural Competence with CILS  
The integration of the CILS within HelloTalk has demonstrated substantial benefits in enhancing communicative and cultural competence. This case study reveals how the CILS's advanced AI features enable learners to engage more effectively in real-time language practice. Through HelloTalk, users access tailored feedback on their communicative efforts, which incorporates both linguistic correctness and cultural appropriateness. This dynamic adjustment of feedback according to user interactions leads to an improvement in language fluency and cultural understanding. Statistically, users have shown a 30% increase in communicative efficiency and a 20% enhancement in cultural awareness. The AI-driveninsights provided by the CILS help pinpoint the nuanced differences in language usage across cultures, enabling learners to navigate complex intercultural dialogs more adeptly.
## 3 Results
### Analysis of the Spatiotemporal Influencing Factors of RSEI in the Shendong Mining Area
#### 3.3.3 \(Rsei\) Change Curve in Different Land-Use Types  
The multi-year average \(RSEI\) values for different land-use types, ranked from highest to lowest, are as follows: forestland (0.698); grassland (0.698); unused land (0.691); cultivated land (0.684); built-up land (0.681); and water bodies (0.643). These \(RSEI\) values reflect the overall ecological quality of these areas, with forestland and grassland exhibiting the highest ecological quality, highlighting their important role in maintaining ecological balance and supporting biodiversity. Notably, as shown in Figure 7, all land-use types show a significant increasing trend in \(RSEI\) over the study period (1990-2023), with each land-use  
Figure 4: Spatial Changes Analysis of \(RSEI\) in the Shendong Mining Area from 1990 to 2023.  
type showing a consistent upward trend in _RSEI_. The cultivated land category demonstrates moderate improvement (R2 = 0.39), with ecological quality steadily increasing, likely due to sustainable agricultural practices or partial ecological restoration efforts. Forestland and grassland, with an R2 value of approximately 0.41, show strong ecological improvements, reflecting the effectiveness of ecological restoration measures and ongoing afforestation efforts. The unused land, with an R2 value of 0.47, shows the most significant improvement among the categories, indicating that restoration efforts for degraded lands have yielded positive results. This trend suggests that areas previously impacted by industrial activities or land degradation are gradually recovering, likely due to effective land management and rehabilitation measures. In contrast, built-up land shows slower improvements (R2 = 0.36), reflecting the ongoing challenges posed by urban development to ecological recovery in these areas.  
Figure 5: (**a**) Sankey diagram of land-use conversion; and (**b**) spatial distribution of land-use type conversion.
## 2 Data
### Next Generation Weather Radar (NEXRAD)  
The NEXRAD network consists of 160 high-resolution polarimetric Doppler weather radars that operate at S-band (10 cm). Here, we obtained level-II archived data from the NCEI for radars located over the coastal U.S. These datasets include the base data quantities of reflectivity, mean radial velocity, and spectrum width, as well as the dual-polarization data of differential reflectivity, correlation coefficient, and differential phase. The data are reported in line with the radar scan time, in intervals of 4.5, 5, 6 or 10 min, depending on the Volume Coverage Patterns in operation. Further analysis of the data, which provides metrics for the storm's microphysical and dynamical characteristics, is described in the following section. Data were obtained from KHGX radar station based on Houston, TX, USA, for 28 May 2016 from 02:45:41 UTC to 04:01:15 UTC.