esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things (IoT) and the Energy Sector
## 6 Future Trends
### Blockchain and IoT  
Current IoT systems mostly rely on centralized cloud systems [151; 152]. In most IoT applications, thousands of IoT devices and machines need to be connected, which is hard to synchronize. Moreover, due to the centralized and server-client nature of IoT when server is vulnerable, all the connected objects are easy to be hacked and compromised, which result in security concerns for the system and privacy issues for users [153]. Fortunately, Blockchain can be a solution for this challenge [154].  
Blockchain provides a decentralized and democratized platform with no need for third party's intervention. The consensus platform of blockchain requires every IoT node proves that it pursues the same goal as others. Verified transactions is also stored in the form of a block, which is linked to the previous one in a way information can never be erased. Moreover, the history of every single transaction at every node can be recorded and is accessible by everyone. Therefore, any member in blockchain becomes aware of any changes in each block immediately [155; 156; 157]. Moreover, due to the distributed ledger of blockchain, even thousands of IoT devices can be synchronized easily. The consensus algorithms of blockchain based on peer-to-peer networks can provide a secure distributed database [153]. Therefore, decentralized and private-by-design IoT that can guarantee the privacy can be promised by blockchain [158].  
More importantly, blockchain can store and share software updates between objects. There are innocuousness checking nodes that approve the accuracy of update information as a new node and guarantee its protection from any threats, once an update added to the blockchain as a valid block, it is impossible to erase or change it. Therefore, IoT-based platforms can be provided with updates availability and innocuousness through blockchain [159].  
In the energy sector, the application of blockchain will accelerate the IoT effectiveness by providing a decentralized platform for distributed power generation and storage systems enhancing energy security and efficiency. Real and high-qualified data can be exchanged freely between devices and people can directly have access to energy information without the involvement of any third party. Neighbors can simply trade energy with one another. Therefore, without involvement of authorities, not only trust will be enhanced among people, but also many costs of this connection to the centralized grids can be saved. Another advantage is that by monitoring the usage statistics of an area, Blockchain enables the energy distribution to remotely control energy flow to that particular area. Furthermore, blockchain-based IoT systems helps in the diagnosis and maintenance of equipment within smart grid [154].  
Currently, the direct application of blockchain technology in an IoT-based system is impossible due to lack of enough computational resources, insufficient bandwidth and the need to preserve power. However, cloud and fog computing platforms can ease the way for blockchain services in IoT [160].
## 4 Results  
The research includes the implementation of the autonomous control of a ship: in this case, the training model \"Blue Lady\". The tests presented here were carried out in the summer under atmospheric conditions recorded during maneuvering in the port on Lake Silm in Ilawa Kamionka. Wind measurements were recorded and the GPS mode was read. The recorded maneuver was composed of a preset route with x and y coordinates and the course of the vessel was given, which, according to the authors, is a level 3 implementation of MASS due to the vessel's independent execution of the maneuver without human intervention. The chapter presents a list of 13 coordinates of the trajectory that the ship had to perform. Velocity measurements and the operation of the training model's thrusters are presented.  
Figure 14 shows part of a map of Lake Silm. In the marked part of the port, the task was completed. The executed maneuver is marked in yellow. The trajectory consists of 13 set points. The specified coordinates start at the berth and are marked in red. The route of the training model \"Blue Lady\" is shown in Figure 15.  
The silhouette of the ship is shown in blue. Figure 15 shows how the maneuver was performed, including how the ship's silhouette was laid out; that is, the beginning of the executed trajectory started with a movement to the starboard (right) side. It then sealed forward. After completing the entire maneuver, the ship docked at the second berth with its port (left) side.  
During the trials, the following were recorded:  
* Low velocity results \(u,v,r\).
* Parameters of the anemometer.
* GPS modes.
* Ship's propulsion signals.  
The maneuver lasted 4000 s. The list includes graphs of longitudinal velocity \(u\) [m/s], lateral velocity \(v\) [m/s], rational velocity \(r\) [rad/s], instantaneous wind registration [m/s], and GPS [modes].  
Note that changing the GPS mode (independent of the author) from the value of 3 to the value of 2 causes the reading of the coordinate values to be able to change from a few centimeters up to 1 m. Therefore, as can be seen in Figure 16, between 400 and 900 s, the adjusted values of the longitudinal and transverse velocity change. The wind was at 3 m/s and, according to the author's observations, the lake wave was 2 to 4 cm high.  
Figure 14: Maneuvering MASS 3 with point trajectory—Lake Silm port. Red box we saw 2 first point for x, y and course.
## 2 Results and Discussion
### Comparison of the PPy-Coated Fiber with Commercial Fibers  
The physicochemical features of the fiber coating comprise the most important variable that affects the efficiency of the SPME sampling methods. The fiber coating should be well matched with the analyte, based on its chemical properties [29]. Accordingly, PA was suggested to be the best choice for nicotine sampling, among the commercial fibers. Therefore, PA commercial fiber was compared with the developed fiber for the analysis of nicotine in aqueous media, through both the DI- and EE-DI-SPME-GC-FID methods. The obtained data demonstrated that the nanostructured PPy-coated fiber was more effective than the PA commercial fiber for both methods. It was approximately four-times more effective than PA in the EE-DI-SPME-GC-FID strategy. The prepared PPy-coated fiber was used over 120 times without any remarkable change in its extraction efficiency (<5%).
## 4 Beamforming Based on INCM Reconstruction
### Complexity Analysis of Beamforming  
This beamforming method mainly includes SV estimation and INCM matrix reconstruction. The computational complexity of obtaining the SV by Equation (24) is \(\mathcal{O}(LPQM^{2})\). In the INCM reconstruction part, eigenvalue decomposition has a complexity of \(\mathcal{O}(M^{3})\), and matrix inversion has a complexity of \(\mathcal{O}(L^{3})\). Consequently, the overall complexity for reconstruction according to Equation (31) is \(\mathcal{O}(LM^{2}+ML^{2}+L^{3}+M^{3})\). Usually, \(L<M\), so the complexity simplifies to \(\mathcal{O}\big{(}M^{3})\). Similarly, the complexity for computing the weight vector is \(\mathcal{O}\big{(}M^{3})\). To improve the performance of the algorithm, \(PQ\) is usually greater than \(M\), resulting in an overall algorithm complexity of \(\mathcal{O}\big{(}LPQM^{2}\big{)}\).  
In our comparison, the proposed method is evaluated against several established algorithms: INCM-subspace algorithm [31], INCM-linear algorithm [27], INCM-volume algorithm [32], INCM-projection 1 algorithm, and INCM-projection 2 algorithm [26]. The complexity comparison is summarized in Table 1. The main complexity of the INCM-volume algorithm lies in INCM reconstruction problem and convex problem. Therefore, it has the computation complexity of \(\mathcal{O}\left(\max\big{(}SM^{2},M^{3.5}\big{)}\right)\), where \(S\) is the sampling point of the annulus surface. For the INCM-linear algorithm, the complexity is dominated by the solution of the convex problem, which is \(\mathcal{O}\big{(}M^{3.5}\big{)}\). Similarly, the INCM-subspace algorithm has a complexity of \(\mathcal{O}\big{(}LM^{3}\big{)}\). Besides, INCM-projection 1 has a complexity of \(\mathcal{O}\big{(}\max\big{(}SM^{2},JM^{2}\big{)}\big{)}\) and INCM-projection 2 has a complexity of \(\mathcal{O}\big{(}\max\big{(}TM^{2},LM^{3.5},SM^{2},JM^{2}\big{)}\big{)}\) in [26], where \(S,J,T\) stand for the number of sampling points in the desired signal region, noise region, and interference region, respectively. It can be observed that the proposed algorithm has a lower complexity than INCM-volume, INCM-linear, and INCM-projection2. Despite having a similar complexity to INCM-subspace and INCM-projection1, the proposed method performs better performance. Therefore, compared to other algorithms, the algorithm proposed in this paper has certain advantages.
## 2 Materials and Methods
### DEM Evaluation  
The Sentinel-1 DEM from the JNB optimization and logistic model was evaluated by the 2015 airborne Lidar campaign from the Korea Hydrographic and Oceanographic Agency ([[http://www.khoa.go.kr](http://www.khoa.go.kr)]([http://www.khoa.go.kr](http://www.khoa.go.kr)); accessed on 1 September 2021). The Sentinel-1 DEM was also compared to the Landsat DEM image generated by the waterline extraction method [9]. For comparison analysis, the 1 m airborne Lidar and 30 m Landsat DEMs on the Universal Transverse Mercator (UTM) projected coordinates were resampled to the 10 m spatial resolution of our Sentinel-1 DEM using bilinear interpolation. They were then converted into the WGS84 geographic coordinate system. The DEM evaluation was performed using common statistical indices, mean absolute error (MAE), and root mean square error (RMSE). The MAE is the average absolute values of the differences between the reference and the corresponding observation. The Root Mean Square Error (RMSE) is a quadratic scoring rule which measures the average magnitude of the error and gives a relatively high weight to large errors.  
\[\text{MAE} = \frac{|(y_{r}-y_{o})|}{n} \tag{3}\] \[\text{RMSE} = \sqrt{\frac{\sum(y_{r}-y_{o})^{2}}{n}} \tag{4}\]where \(n\) is the number of observations and \(y_{r}\) and \(y_{o}\) are the reference and observation values, respectively. The RMSE tends to be larger or equal to the MAE. Our evaluation might have been influenced by different DEM resolutions and resampling methods, but their sensitivity to the DEM error was beyond the scope of this study.
## 2 State of the Art on Characterization of Weld Geometries and Stress Concentrations  
### Characterization of Weld Geometries  
To this day, non-destructive testing of engineering structures is, to a large extent, focused on weld defect detection [3]. In addition, visual inspections are performed to ensure sufficient weld quality in accordance with standards such as ISO 5817:2014 [4], Volvo STD 181-0001 [5], or ISO/TS 20273 [6]; however, the extent of such investigations varies and is directly linked to the qualification and training of the person performing the tests. It is thus not surprising that the weld quality of different workshops varies significantly, even if the same preliminary welding procedure specification was applied [7]. Recent developments for both measurement systems and post-processing routines present new possibilities to perform automatic inline quality inspections and thereby fatigue strength assessment of welded joints [2].  
Standards to relate the fatigue strength of welded joints to measured geometrical features have existed for several years, e.g., [8; 9]; however, these standards are based on numerical calculations rather than measurements and detailed descriptions of weld geometries, c.f., [10; 11]. Nevertheless, they provide a suitable tool for a quick quality assessment based on manually measured geometries; yet, for automatic inline quality inspections, the relation between fatigue strength and geometrical parameters needs to be investigated. Hence, the understanding of the statistical variation of weld geometries are crucial to set up computer-aided measurement methods for welded joints. To date, these methods are usually based on 2D profiles of the weld [12].  
Weld toe geometries typically vary significantly along weld seams [13]. Thus, the optimal number of measurements per length (also called slices) has to be determined, see [14]. On the other hand, a large number of slices increases processing times, which should preferably be limited. In two recent studies, [PERSON] et al. [15] and [PERSON] et al. [16] investigated the effect of sampling rates on measured weld geometries; however, this leads to questions regarding the applicability of current weld quality standards, as the proposed resolution is significantly larger than the resolution of manual measurements. These standards are based on the idea of a few manual gauge measurements. Hence, new methods are required to describe weld qualities. These could, for example, be based on statistical distributions of weld geometry parameters, such as the IBESS procedure [17], and SCFs.
## 3 Materials and Methods
Letting \(\kappa_{g}=(L_{g}+C_{1}+C_{2}\Delta_{max}+M)\), we obtain  
\[\|\
abla F(x^{k}+s^{k})-\
abla m^{k}(x^{k}+s^{k})\|=\kappa_{g}\Delta^{k}.\]  
Hence, the first property in Definition 3 is verified. The second property can be obtained by using a similar process to that used in [62] (Proposition 19.1). Therefore, the model \(m^{k}\) is fully linear on \(B(x^{k};\Delta^{k})\). It follows from Proposition 1 that Algorithm 1 always builds a fully linear model on \(B(x^{k};\Delta^{k})\). In [61], [PERSON] and [PERSON] analyzed the convergence properties of a DFTR algorithm for an optimization problem of the form  
\[\min_{x\in C}F(x)\]  
where \(C\subseteq\mathbb{R}^{n}\) is closed and convex with a nonempty interior and \(F:\mathbb{R}^{n}\rightarrow\mathbb{R}\). The main algorithm developed in their paper, Algorithm 2.1, follows the same steps as those of our algorithm. Since a box constraint is a convex closed set with a nonempty interior, the convergence results that were proven in [61] may be applied to Algorithm 1. For completeness, we recall the three assumptions and convergence results of [61]. **Assumption 1**. : _The objective function \(F\) is bounded below and continuously differentiable. Furthermore, the gradient \(\
abla F\) is Lipschitz continuous with the constant \(L_{\
abla F}\geq 0\) in \(\cup_{k}B(x^{k};\Delta_{\max})\)._  
**Assumption 2**. : _There exists \(\kappa_{H}\geq 0\) such that \(\|H^{k}\|\leq\kappa_{H}\) for all \(k\)._  
**Assumption 3**. : _There exists a constant \(c_{1}\in(0,1)\) such that the computed step \(s^{k}\) satisfies \(x^{k}+s^{k}\in C\), \(\|s^{k}\|\leq\Delta^{k}\), and the generalized Cauchy decrease condition is_  
\[m^{k}(x^{k})-m^{k}(x^{k}+s^{k})\geq c_{1}\pi_{m}^{k}\min\left(\frac{\pi_{m}^{k} }{1+\|H^{k}\|},\Delta^{k},1\right).\]  
**Theorem 1**. : _Suppose that Algorithm 1 is applied to the minimization problem (5). Suppose that Assumptions 1-3 hold. Then,_  
\[\lim_{k\rightarrow\infty}\pi_{F}^{k}=\lim_{k\rightarrow\infty}x^{k}-\mathrm{ Proj}_{|\{\ell,\mu\}}(x^{k}-\
abla F(x^{k}))=0.\]  
To conclude this section, we provide details on the different choices made while implementing Algorithm 1.
## 3 Diagnosis System  
### Signal Preprocessing  
In order to facilitate feature extraction and recognition of ultrasonic signals, preprocessing needs to be performed on ultrasonic signals at first, which includes denoising, frequency shifting, framing, and windowing.  
The collected ultrasonic signals should be denoised to eliminate the negative effect of environmental noises on diagnosis. The wavelet denoising method is adopted to denoise the signals with a posterior median threshold rule.  
Frequency shifting is essential for the collected ultrasonic PD signals to make their MFCC features more distinguishable. According to the relationship of the actual frequency and mel frequency, the growth rate of the mel frequency gradually slows down with the increase of the actual frequency, as shown in Figure 1. This indicates that the sensitivity of the signal in the high-frequency domain gradually reduces when they are converted into mel frequency, which will vague the differences of MFCC features. The ultrasonic PD signal of GIS ranges from 20 to 80 kHz [45], obviously located in the high-frequency domain. Therefore, the reduced sensitivity has a significant negative impact on the accuracy of insulation defects diagnosis of GIS. Shifting the ultrasonic spectrum to a lower frequency range with higher sensitivity in mel spectral can solve the problem, and therefore the accuracy of the diagnosis can be enhanced. The collected ultrasonic signal is frequency modulated with a 38.4 kHz sinusoid to downshift it to be centered at 1.6 kHz.  
Since short signals are more stable than long signals, signal framing is a method to facilitate signal analysis with MFCC. Take the length of one frame of the ultrasonic signal the same as its period, which is 20 ms. In order to avoid excessive changes between two adjacent frames and ensure the continuity of the framed signal, there should be an overlapping area between two adjacent frames. In this paper, the overlap rate is 50%.  
The framed signal being windowed is indispensable for improving the continuity of the ends of each frame. Hamming window (11) is chosen because it can help prevent spectral leakage and reduce attenuation at FFT when MFCC features are extracted.  
\[\begin{split} W(n,a)&=(1-a)-a\times cos\Big{[} \frac{2\pi m}{N-1}\Big{]},\\ & 0\leq n\leq N-1\end{split} \tag{11}\]where \(N\) is the length of the Hamming window, and \(a\) is a specific parameter of the Hamming window because the value of \(a\) affects the performance of the Hamming window, generally \(a=0.46\).
# Mechanical Properties Analysis of WAAM Produced Wall Made from 6063 Alloy Using AC MIG Process
## 1 Introduction
Mechanical tensile tests, in particular the analysis of stress-strain behavior, provide crucial data for the evaluation of fatigue resistance and fracture performance--key aspects for the structural reliability of WAAM-manufactured parts. Despite numerous studies on the WAAM process for aluminum alloys, the mechanical performance of 6000 series alloys produced by pulsed AC-MIG welding has been investigated only to a limited extent. This study investigates the mechanical properties of alloy 6063 produced by WAAM using pulsed AC-MIG with different electrode-negative (EN) ratios with the aim of evaluating its suitability for structural applications.
## 2 Background Information
### Virtual Reality
This can save time and money in the production process, as well as improve the quality and reliability of products. Furthermore, VR technology can be used to simulate different scenarios and identify potential inefficiencies in the production process. This can help managers and engineers optimize the flow of materials, equipment, and workers, ultimately increasing efficiency and reducing wastage. Compared to other technologies, such as augmented reality or traditional simulation software, VR technology offers a more immersive and interactive experience. It allows workers to experience a realistic, three-dimensional environment that closely simulates the real world, which can improve learning and the retention of information. Additionally, VR technology can be used with a wide range of devices, including virtual reality headsets, which can provide a more immersive experience than other technologies. While there may be limitations and challenges associated with the use of VR technology in the digital factory, it has been shown to be effective in improving production processes, and it has several advantages over traditional methods and other technologies. The potential benefits of using VR technology in the digital factory, such as improved safety, efficiency, and collaboration, can outweigh the drawbacks, such as high initial costs and technical limitations. However, it is important for stakeholders to carefully consider the potential benefits and drawbacks and to address any ethical concerns that may arise.
## 4 Results and Discussion
### Effect of Vibration on the Shear Stress
#### 4.2.3 0.35 g Vibration at Different Frequency  
The comparison of the shear stress at the internal wall of the extrusion liquefier within ME obtained respectively by DMMFB and FEA is provided in Figure 10. No matter without or with vibration applied (the amplitude is 0.35 g and the frequency is, separately, 100, 200, 300, 400 and 500 Hz), the analytical and simulated shear stress have a good agreement.  
Table 8 details the effective value of the above shear stress for each condition. Again, the value decreases with vibration applied, and further decreases with the increase of vibration frequency.  
To sum up, introducing vibration into the extrusion process can reduce the shear stress at the internal wall of the liquefier, and increasing the vibration frequency will further reduce the shear stress, which helps improve the forming quality of ME products.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency (Hz)**} & \multicolumn{4}{c}{**Effective Value**} \\ \cline{2-5}  & **DMMFB (MPa)** & **Drop (\%)** & **FEA (MPa)** & **Drop (\%)** \\ \hline
0 & 0.89 & - & 0.88 & - \\ (No vibration) & 0.59 & 33.7 & 0.58 & 34.1 \\
100 & 0.51 & 42.7 & 0.50 & 43.2 \\
300 & 0.44 & 50.6 & 0.43 & 51.1 \\
400 & 0.39 & 56.2 & 0.37 & 58.0 \\
500 & 0.35 & 60.7 & 0.34 & 61.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effective value of the shear stress without and with 0.3 g vibration applied at different frequencies.  
Figure 9: The shear stress at the internal wall of the extrusion liquefier without and with 0.3 g vibration applied at different frequencies. (**a**) No vibration applied, (**b**) 100 Hz, (**c**) 200 Hz, (**d**) 300 Hz, (**e**) 400 Hz, (**f**) 500 Hz.
## 2 Materials and Methods  
A systematic review of peer-reviewed studies published in Pubmed (www.ncbi.nlm.nih.gov; last accessed on 10 January 2021), ISI Web of Science (www.webforknowledge.com; last accessed on 10 January 2021), and Sciencedirect (www.sciencedirect.com; last accessed on 10 January 2021) databases was performed covering the 20 years between January 2000 and December 2020. The keywords used for the search were: plant extract, plant by-product, essential oil, natural vitamins, synthetic vitamins, poultry, vitamin E, vitamin C, tocopherols, tocopheryl, antioxidants, and organic farming. Documents were selected firstly based on the title and then on the abstract. If the objective of the work and the tested parameters were in agreement with the selection criteria and consistent with the aim of the present review, the articles were downloaded and summarized in tables that were then described and discussed in the text. The selection criteria were as follows: (i) articles comparing the effect of PE or EO with a specific dose of synthetic vitamins or synthetic antioxidants in poultry; (ii) articles comparing the effect of BP from different agro-industries with a specific dose of synthetic vitamins or synthetic antioxidants in poultry; (iii) articles published in peer-reviewed journals; and (iv) exclusion of studies dealing with propolis, algae, and additives of animal origin. At the end of the selection, a total of 44 papers were retained for the review, of which 35 dealt with meat products and 9 focused on eggs. Of the 44 studies, 17 were conducted in Greece, and 19 papers compared rosemary extract/oil and/or oregano extracts/oil with vitamin E (VitE). The present review is divided into two sections (i.e., meat and eggs production) and according to the group of parameters evaluated: (i) slaughter traits; (ii) meat quality characteristics; (iii) lipid oxidation of poultry meat; (iv) microbial contamination; (i) egg production yield and traits; and (ii) lipid oxidative stability of eggs.  
Potential Plant Extracts and Plant By-Products as Alternative Sources of Vitamins for Animal Feeding: Impact on Productivity