esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
This explains why the values for elastic properties are smaller in Table 3.  
Second, we examine the difference between horizontal and vertical properties. From Table 3, we observe that that the homogenized \(E_{h}\) not only has a more stable mean value (reflected in e(\(\upmu\))), but also has smaller normalized variations (reflected in COV). Both aspects are also graphically evident when comparing Figures 10b and 13b. This can be attributed to whole regions spanning the RVE horizontally, acting as rather deterministic parallel springs for the horizontal loading case. For fracture properties, while a lower variation is observed for \(\sigma_{h,max}\) compared to that for \(\sigma_{v,max}\) in Figures 11b and 14b and \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Horizontal**} & \multicolumn{2}{c}{**Vertical**} \\ \cline{2-5}  & \(E_{h}\) & \(\sigma_{h,max}\) & \(E_{p}\) & \(\sigma_{v,max}\) \\ \hline Mean-based: e(\(\upmu\)) & 0.000604 & 0.00766 & 0.00146 & 0.0166 \\ \hline Variation-based: COV (\(L_{SVE}=140\)\(\upmu\)m) & 0.00460 & 0.0230 & 0.00997 & 0.0162 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of values used to examine mean- and variation-based RVE sizes for all four homogenized properties. in Table 3, one needs to refer to the actual strain-stress responses in Figures 9 and 12 to have a clearer understanding of the differences of the two loading beyond just the maximum stress. From the strain-stress responses, we observe a lower variation in the elastic slope, \(E_{h}\), compared to that in \(E_{v}\) (consistent with the COV values for \(L_{SVE}=\)140 \(\upmu\)m in Table 3). In contrast, it is now the horizontal direction that has a higher variation in response post-elastic regime.
## 2 Material and Methods
### Electrochemical Analyses  
The corrosive dissolution consisted of 50 mL of 0.6 M NaCl, the salt being supplied by Merck-Sigma-Aldrich. The exposed areas were approximately 0.8 cm\({}^{2}\), being defined with a punch made on electrical isolation tape and shielded with epoxy resin to avoid any crevices. All items were provided by RS Components Ltd. All electrochemical analysis techniques were conducted using a potentio/galvanostat (Interface1010E, Gamry Instruments Inc.) and three-electrode cell. The reference electrode was Ag/AgCl 3 M KCl (EDT Direct Ion Ltd., Dover, UK) while the counter electrode was a platinum wire of 0.7 mm diameter (Cooksongold (Heimerle + Meule Group)). The samples formed the working electrode. The potentio/galvanostat was handled with Gamry Framework version 7.8.4 software and the data were evaluated using Gamry Echem Analyst version 7.8.4 software.  
Three electrochemical analysis techniques were utilised to evaluate the corrosion resistance of the samples, these being asymmetrical electrochemical noise (AEN), potentio-dynamic polarisation curve (PPC), and electrochemical impedance spectroscopy (EIS). AEN was carried out with two passive techniques, these being open-circuit potential (OCP) and zero ammeter resistance (ZRA). Both techniques were conducted at the same time with 0.5 s acquisition time for 2 h. PPC was set with 0.167 mV/s potential rate, \(-\)0.3 V vs. potential at open circuit as initial potential, 2 V vs. Ag/AgCl 3 M KCl as final potential, and 10 mA/cm\({}^{2}\) limit current density. The potential at open circuit was measured for 2 h immersion. EIS setups were conducted using a frequency range from 10\({}^{-2}\) Hz to 10\({}^{5}\) Hz, 10 mV (root mean square), and 10 points per frequency decade. EIS analyses were conducted at 2 h, 24 h, 48 h, 72 h, and 96 h of immersion. The potential was defined in the same way as the PPC case. All testing was repeated at least three times for each sample to confirm the validity of the results.
## 5 Conclusions
Therefore, the implementation of sophisticated noise reduction techniques during data pre-processing could be critical before employing the FastICA algorithm. Moreover, noise levels can also influence the strategic placement of sensors. In this study, sensors were positioned at closely spaced cross-sections. Yet, for long-span bridges where cross-sections are more widely distributed, noise could potentially obscure signals of small amplitude that are indicative of structural responses. Such interference might undermine the effectiveness of the proposed method. Consequently, the sensor layout, particularly the maximum distance between cross-sections, must be meticulously planned to preserve the method's effectiveness. Additionally, the vibration modes to be considered may vary among different bridge types, with long-span bridges requiring a tailored approach. The number of cross-sections from which bridge responses are measured as inputs should be determined before applying the proposed method, which is also related to the layout of sensors. Addressing these challenges will be a priority in forthcoming research. Conceptualization, methodology, software, validation, and writing--original draft preparation, X.L. ; investigation and writing--original draft preparation, C.W. ; resources and data curation, Y.X. ; writing--review and editing, supervision, and funding acquisition, L.S. ; investigation and writing--original draft preparation, W.Z. All authors have read and agreed to the published version of the manuscript. The author(s) disclosed the receipt of the following financial support for the research, authorship, and publication of this article: Shanghai Qi Zhi Institute (Technology Cooperation Project of Shanghai Qi Zhi Institute. Grant No. SQZ202310); Fujian Provincial Department of Science and Technology, China (Optimization design and key technology research of urban bridge cluster monitoring. Grant No. 2023Y0040); and National Natural Science Foundation of China (Grant No.52278313; and Grant No. 5231101864). Not applicable. The data presented in this study are available upon request from the corresponding author, and requests should be due to [PERSON]. Author [PERSON] was employed by the company Fujian Provincial Construction Engineering Quality Testing Center Co., Ltd., and Fujian Academy of Building Research Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 Applications in Medical and Food Systems
### Food Applications
#### 6.2.1 Juices and Beverages  
_Withania-somifera_-fortified beverage blends could be utilized for its nutritional quality in functional fruit beverages and the developed stored product was stable and acceptable for 90 days at room temperature [137].
## 4 Results and Discussion
### Results Using K-Fold Cross-Validation  
To show the adequacy and efficacy of the models, this study performs 10-fold cross-validation with both BoW and TF-IDF features. The 10-fold cross-validation is applied after annotating the dataset using the Textblob technique. The results with 10-fold cross-validation are shown in Table 16. Results indicate that models perform significantly in 10-fold cross-validation and SVC achieves the highest 0.94 accuracy score with +/ \(-\)0.03 standard deviation using the SMOTE technique and both BoW and TF-IDF features. SVC and RF also perform significantly better without applying the SMOTE technique with a 0.93 accuracy score and 0.04 standard deviation with both BoW and TF-IDF features.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 2 General Configuration of SAA  
The basic configuration and geometry of the SAA are presented in Figure 1, where \(\theta_{i}\) and \(\phi_{i}\) represent the elevation and azimuth of the \(i\)th element, respectively. \(\theta_{d}\) and \(\phi_{d}\) denote the elevation and azimuth of the incident plane wave, respectively, while \(s_{d}\) is the source signal. The radiating antenna elements are evenly distributed over a sphere of radius \(r\). There is a conventional distribution for an antenna array on a geodesic sphere depending on 1 of the 13 Archimedean solids or the 5 platonic solids [3; 11]. Here, the fundamental merit is that all elements encounter the same neighborhood. Conversely, other various distributions could be determined based on the beam-scanning requirement and the number of antenna elements in use. For an icosahedron distribution along the elevation axis, the elements are organized in circular bands. In each circular band, the locations in azimuth are clearly estimated. Furthermore, any far-field observation point can be defined as \(p(r,\theta,\phi)\).  
The SAA is in active mode, where each element is supplied with an amplifier. To receive a beam pattern in a specific direction \((\theta_{0},\phi_{0})\), the elements lying within the predetermined cone angle along the beam direction are in active mode. These elements are activated using accurate phases estimated from the equations in [10; 32; 33]. Furthermore, the SAA configuration (as in Figure 2) is 3-D symmetrical, which is an advantage in the analysis of the spatial signal. Both its size and geometry produce the directivity and beam-scanning level in the azimuth and elevation angles needed from the array. For more steerability, a multifold planar configuration or, in a more general form, an SAA is preferred [3; 4; 5; 6; 7]. The directivity of an SAA is almost the same when an active source is moved over the surface of the SAA.  
Figure 1: Spherical array geometry system for localization. \(\theta_{i}\) and \(\phi_{i}\) represent the elevation and azimuth of the \(i\)th element, respectively. \(\theta_{d}\) and \(\phi_{d}\) denote the elevation and azimuth of the incident plane wave, respectively, while \(s_{d}\) is the source signal.  
Figure 2: _Cont._
## 2 Literature Review
The table outlines each approach's key characteristics, encryption method used, and limitations, providing a clear understanding of how they perform. These developments demonstrate how HE and its variants can make a difference in securing IoT datasets for safe and efficient machine learning applications that retain user privacy. Machine learning algorithms can easily integrate it, allowing for data analysis and prediction without compromising data privacy. The integration of HE and machine learning opens a very promising approach to dealing with privacy and security issues in smart home sensor datasets, opening a pathway toward more advanced data mining techniques securely and confidentially. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper Title** & **Encrypption Method** & **Key Results** & **Limitations** \\ \hline \multirow{4}{*}{Lattice-Based HE for Privacy-Preserving [18]} & \multirow{4}{*}{Lattice-based fully homomorphic encryption} & Efficient smart meter data encryption and aggregation; allows computations on encrypted data & Key management complexity; potential inefficiency in large-scale deployments \\ \cline{2-4}  & & & Efficient data privacy preservation in smart home systems with distributed consensus on encrypted data & Complex key management and potential scalability issues in large networks \\ \hline \multirow{4}{*}{Smart Grids + Paillier HE [20]} & \multirow{4}{*}{Paillier homomorphic encryption for smart grid data Aggregation} & Improved data privacy and encryption efficiency in smart grids; reduced encryption time compared to other methods & High computational complexity and potential performance bottlenecks in large-scale smart grid data \\ \hline \multirow{4}{*}{Lattice-Based and HE in Smart Grid [21]} & \multirow{4}{*}{Lattice-based homomorphic encryption for smart grid data aggregation} & Enhanced consumer privacy and data confidentiality in smart grids; resilient to several attacks & Requires low computational overhead but may be vulnerable to lattice-based attacks \\ \hline \multirow{4}{*}{GuardML [12]} & Hybrid Homomorphic Encryption (HHE) with PASTA for PPML in constrained devices & Demonstrated practicality of HHE in MLaaS with minimal accuracy loss; low computation and communication overhead & Slight reduction in model accuracy compared to plaintext; potential scalability challenges in large deployments \\ \hline \multirow{4}{*}{HETAL [14]} & CKKS HE scheme, optimized for transfer learning by encrypting client data before training & Achieves accuracy near non-encrypted training; optimized matrix multiplication improves performance significantly & High computational overhead, especially for encrypted matrix multiplication performance significantly \\ \cline{2-4}  & & & Achieved function approximation on encrypted data, balancing accuracy and privacy & Limited to small-scale models; performance degrades with increased model complexity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of HE-based approaches in IoT-based datasets.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 1 Introduction  
A 5G system puts forward stricter requirements on positioning accuracy and performance of user equipment (UE) position than a 4G wireless cellular communication network system. The confidence of positioning accuracy of each positioning performance level is 95% [1]. This means that 3 GPP will deeply couple 5G communication and positioning capabilities and continue to add value to 5G-enabling vertical industries.  
The Industrial Internet of Things is a major vertical focus area for NR R16 [2, 3], and is shaping the future of many commercial applications. The fourth Industrial Revolution (Industry 4.0) triggered by IIoT will bring significant revenue expansion through the new digital service provider market [4]. The IIoT network will be scalable in terms of connections and number of devices and will use key LTE and 5G new radio (NR) capabilities, such as ultra-reliable low-delay communication, large-scale machine-type communication, 5G positioning, time-sensitive communication, and to a lesser extent, enhanced mobile broadband.  
In Industry 4.0, there is a growing need to develop indoor navigation and localization systems for future living and working environments. The market opportunities of Indoor-Localization-related systems are expected to be in the order of USD 10 billion by 2024 [5]. The presence of 5G has the potential to improve indoor positioning estimation to sub-meter accuracy [6] and offers opportunities for a large number of applications in manufacturing [7].  
In the R16 version, 5G can achieve an indoor positioning accuracy of 3 m and outdoor positioning accuracy of 10 m [8]. The R16 can achieve accuracy of less than 5 m outdoors and less than 3 m indoors by using high time resolution of 5G bandwidth and multi-beam Angle information. However, there is still a gap between centimeter-level high-precision positioning requirements. In R17, critical 5G positioning capabilities will be improved to meet the needs of more stringent use cases such as centimeter-level accuracy, while reducing positioning delay and increasing positioning efficiency to expand capacity [1]. Low power devices with high accuracy positioning (LPHAP) is one of the enhanced features of R18 defined 5G-Advanced [9], which includes sub-meter accuracy to further reduce latency and terminal power consumption, enabling 5G positioning to meet the requirements for higher accuracy and delay in more end markets [6; 10], as summarized in Table 1.  
This illustrates the areas that the strict requirements of IIoT for 5G cellular positioning with higher precision, richer terminal types, and significantly reduced terminal side power consumption are further focused on. Among the numerous requirements, the most important one is to reduce the power consumption of the positioning device while maintaining the positioning accuracy, so as to occupy the growing positioning market and expand the potential IIoT Settings use cases, supporting the need to add new use cases, such as factory automation and power distribution.
## 3 Results
### A Simulation Study  
In the simulation study, a synthetic graph is generated from stochastic block model (SBM) with probability matrix \(M=\begin{pmatrix}0.5&0.05\\ 0.05&0.4\end{pmatrix}\)[17]. Each block has 20 vertices. The network is shown in Figure 3.  
Since the true community structure is known, we can test the algorithm at different parameters. Figure 4 shows the inclusion probabilities of the local community of vertex 1 by using three constant tolerance parameters, \(\epsilon=0\), 0.02, and 0.04. It is apparent that the algorithm can detect the underlying hierarchical structure. Meanwhile, as \(\epsilon\) increases, the inclusion probability also increases for vertices in the other block. This is due to the fact that a broader region in the network is being probed because there is a higher chance of crossing the bridges between the blocks.  
As discussed in Section 2.4, the running time scales quadratically with the network size. It is thus feasible to apply the algorithm to a larger network. Moreover, in order to assess the quality of the detected local community, we computed the average inclusion probability \(\bar{P}_{v_{0}}\) of all vertices in the same block as the seed \(v_{0}\). A higher value indicates a good quality of the detected community. The same probability matrix \(M\) is used on networks with different sizes, and the result is shown in Table 1. Since all values are higher than 90%, the proposed algorithm performs well on finding the underlying local community structure.  
Figure 4: Plot of inclusion probabilities of vertex 1 in the simulated network in Figure 3. Three tolerance parameters (\(\epsilon=0,0.02\), and 0.04) are used. As \(\epsilon\) increases, more of the network is probed.  
Figure 3: A synthetic network generated from the stochastic block model. Red and blue colors represent two underlying blocks, i.e., communities.
## Evaluation of High-Precision Sensors in Structural Monitoring  
**[PERSON]**  
Geomatics Engineering Department, Civil Engineering Faculty, Istanbul Technical University, Maslak 34469 Istanbul, Turkey; E-Mail: [EMAIL_ADDRESS]; Tel.: +90-212-285-3821; Fax: +90-212-285-3821  
_Received: 27 September 2010; in revised form: 12 November 2010 / Accepted: 30 November 2010 / Published: 2 December 2010_  
**Abstract:** One of the most intricate branches of metrology involves the monitoring of displacements and deformations of natural and anthropogenic structures under environmental forces, such as tidal or tectonic phenomena, or ground water level changes. Technological progress has changed the measurement process, and steadily increasing accuracy requirements have led to the continued development of new measuring instruments. The adoption of an appropriate measurement strategy, with proper instruments suited for the characteristics of the observed structure and its environmental conditions, is of high priority in the planning of deformation monitoring processes. This paper describes the use of precise digital inclination sensors in continuous monitoring of structural deformations. The topic is treated from two viewpoints: (_i_) evaluation of the performance of inclination sensors by comparing them to static and continuous GPS observations in deformation monitoring and (_ii_) providing a strategy for analyzing the structural deformations. The movements of two case study objects, a tall building and a geodetic monument in Istanbul, were separately monitored using dual-axes micro-radian precision inclination sensors (inclinometers) and GPS. The time series of continuous deformation observations were analyzed using the Least Squares Spectral Analysis Technique (LSSA). Overall, the inclinometers showed good performance for continuous monitoring of structural displacements, even at the sub-millimeter level. Static GPS observations remained insufficient for resolving the deformations to the sub-centimeter level due to the errors that affect GPS signals. With the accuracy advantage of inclination sensors, their use with GPS provides more detailed investigation of deformation phenomena. Using inclinometers and GPS is helpful to be able to identify the components of structural responses to the natural forces as static, quasi-static, or resonant.  
keywords: structural deformations; inclination sensor; GPS sensor; spectral analysis; engineering structures; geodetic monuments
## 4 Results
### Notification Experience Comparison
#### 4.2.1 Importance Versus Experience  
Our first comparative analysis aimed to understand the relationship between user perceptions of push notifications based on their importance versus the nature of the experience  
Figure 4: Scale cards distributed among characteristics.  
(see Figure 5). The \(Y\)-axis represents the level of importance the participants have attributed to notifications while the \(X\)-axis represents the perceived experience of the notifications.  
While most of the characteristics reside in similar proximities and are positioned close to each other with a modest positive experience and relatively low in importance, a distinctive divergence in the characteristics of Unexpected--Reactive is present. It particularly indicates an inclination towards notifications perceived as important tending to correspond to negative experiences, highlighting a need for design considerations to prevent user annoyances and frustrations.  
There is a common pattern appearing from the characteristics indicating an inverse relationship between notification importance and notification experience. There is a reoccurring and clear preference for Calm--Proactive and Calm--Reactive characteristics in terms of their positive notification experience and perceived importance.  
Overwhelming--Reactive notifications, while sometimes considered important, also tend to correspond to negative experiences. The divergence in frequency and perceived quality among the characteristics highlights the complexity of how users perceive push notifications overall, suggesting that a one-size-fits-all approach to notification design is insufficient.
## 4 Verification by Experiment
### Experimental Setup  
A simply supported steel beam was employed to validate the effectiveness of the improved moment-area method. The elastic modulus and density of the steel were \(E\) = 200 GPa and \(\rho\) = 7800 kg/m\({}^{3}\), respectively. The cross section parameters of the specimen were: width of 100 mm and height of 12 mm. The overall length and net span of the simply supported beam were 2700 mm and 2400 mm, respectively. Seven basalt fiber-packaged FBG sensors (namely E1 to E7) were arranged on the bottom side of the test beam, of which the gauge length was 30 cm. The center of the first sensor (E1) was 30 cm from the left support. Three laser-displacement meters (namely L1 to L3) were also deployed for comparison. L1 and L3 were installed at 1/4 and 3/4 span of the test beam, respectively. L2 was arranged at mid-span of the test specimen. Detailed information about the test beam is provided in Figure 11.  
The test site overview of the specimen is shown in Figure 11(a). It is noteworthy that a new form of support was applied to constrain the vertical displacement of supports. Considering vibration load, these supports made it approach the practical situation. More details of supports are shown in Figure 11(b).  
Figure 11: Experimental setup and sensor placement.  
Figure 10: Design and manufacturing of fiber-packaged fiber Bragg grating (FBG) sensors.