esa-sceva/satcom-chunk-collection huggingface dataset:
# High Reliability Evaluation and Lifetime Prediction of 50 GHz Athermal AWG Module
## 2 The Principle of Temperature Independence  
### Principle of AWG  
Figure 1 shows the waveguide circuit structure of an arrayed waveguide grating (AWG), in which input waveguides, input slab waveguides, arrayed waveguides, output slab waveguides, and output waveguides are arranged on a substrate. The optical demultiplexer operates as follows. When a WDM signal containing multiple optical signals with wavelengths \(\lambda_{1}\)-\(\lambda_{n}\) enters the input waveguides, it is diffracted and diffused by the input slab waveguide and transmitted to the arrayed waveguide. This signal consists of waveguides placed side by side in each waveguide. The signal emitted from the input slab waveguide propagates, and adjacent waveguides are placed at a fixed optical path length difference of \(\Delta\)L. Therefore, the signals propagating from each waveguide show a phase difference. Signals passing through the arrayed waveguide enter the output slab waveguide and are diffracted and diffused, but the signals passing through each waveguide interfere with each other and are virtually all diffracted in the direction where the wavefronts are aligned. The phase condition in which the wavefronts are aligned is as follows.  
\[n_{s}\,D\sin\varnothing+n_{c}\Delta\mathrm{L}=m\lambda \tag{1}\]  
where \(n_{c}\) is the effective index of arrayed waveguides and \(n_{s}\) is the effective index of refraction of slab waveguides; \(\varnothing\) is the diffraction angle; \(D\) is the distance between the arrayed waveguides; \(\lambda\) is the wavelength; and \(m\) is an arbitrary integer and the order of diffraction.  
Therefore, signals with different wavelengths converge at different points at the output port of the output slab waveguide, and by placing an output waveguide at each of these points, signals with different wavelengths can be sent to different waveguides to extract signals with wavelengths \(\lambda_{1}\)-\(\lambda_{n}\).
## 2 Proposed Structure: Analysis and Design Methodology
### Design Methodology
If this is the case, we have two totally independent differential passbands, which represents the ideal case when designing filters with dual-band operation. Figure 5: Differential excitation return loss (\(|S_{11}^{dd}|\)) of the structure composed by a couple of top and middle resonators like those depicted in the insets when they are weakly excited: (**a**) \(l_{2}^{b}+l_{3}^{b}+l_{4}^{b}+l_{5}^{b}\) is varied; (**b**) \(l_{1}^{t}+l_{2}^{t}\) is varied. In both figures, the remaining physical parameters (see Figure 2) have been kept constant. Fractional bandwidth is related to the coupling level between resonators. The closer the resonators are the larger the fractional bandwidth is. Referring to Figure 2, the relevant geometrical parameter that controls the coupling between resonators for the ones located on the top layer is \(s_{1}^{t}\), whereas \(s_{1}^{b}\) is the responsible for the coupling level between resonators in the middle layer. To verify this, fractional bandwidth as a function of \(s_{1}^{t}\) and \(s_{1}^{b}\) are shown in Figure 7a,b, respectively. From Figure 7, it is clear that, as it happened with the center frequencies and the differential return loss, the fractional bandwidth of the two differential passbands are also independent from each other. This fact confers the designer full possibilities to adapt the design to other frequencies or other bandwidths that might be necessary for other requirements and specifications. In order to clarify the design process of the balanced dual-band bandpass filter proposed in this paper, the following summary is given below:  
1. First, resonator dimensions must be obtained in order to fit their resonance frequencies to the desired passband center frequencies. Since these specific resonators are half-wavelength open loop resonators, their resonance frequencies are mainly determined by their lengths, which can be easily obtained by using well known equations [45] or with the help of the electromagnetic simulator. 2. Next, the fractional bandwidth of each differential passband is set to the desired one by properly adjusting the values of \(s_{1}^{t}\) and \(s_{1}^{b}\). Figure 6: \(|S_{11}^{dd}|\) for different values of (**a**) \(l_{\text{over}}\) and (**b**) \(s_{2}^{t}\). Figure 7: FBW with as a function of the resonators gap: (**a**) \(s_{1}^{t}\) is adjusted; (**b**) \(s_{1}^{b}\) is adjusted. Finally, the matching level is adjusted by setting the values of \(s_{2}^{t}\) and \(l_{\text{over}}\). The proposed filter is finally designed and ready to be fabricated and measured.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 4 Vision-Aided PDR System Design
### Visual Place Recognition
#### 4.1.2 Ranking Search  
To find the most similar location, because the same location can be viewed from different perspectives, our database needs to store images of different perspectives for the same location for searching [38]. Here, photos are stored every 20 degrees for a location, with a total of 18 images in one location. The image acquisition method is shown in Figure 7.  
Every time a photos is searched, its encoding o is compared with the images in the database. For the same place, the similarity in a certain direction is the greatest, and the side angle is similar to the picture to be compared, but it is lower than the opposite angle. In this way, if there are multiple pictures of the same location and different angles in the search list in Recall@10 (R@10 denotes the top ten database candidates), then it can increase the certainty of the exact location and angle in this way,  
Figure 6: Visual place recognition network structure.  
which reduces accidental location errors. According to this method, the positioning recognition rate of the image can be enhanced.
## 1 Introduction
A nonadaptive solution (i.e., an elastic net) [8] is a mixture of both ordinary \(\ell_{1}\) and \(\ell_{2}\). In particular, it is a useful model when the number of predictors (p) is much larger than the number of observations (n) or in any situation where the predictor variables are correlated. Table 1 presents the important properties of the regularizers. As seen in Table 1, \(\ell_{2}\) and the ordered \(\ell_{2}\) regularizers are suitable methods for highly correlated, high-dimensional grouping data rather than \(\ell_{1}\) and the ordered \(\ell_{1}\) regularizers. The ordered \(\ell_{2}\) encourages grouping, whereas most of \(\ell_{1}\)-based methods promote sparsity. Here, grouping signifies a group of strongly correlated variables in high-dimensional data. We used the ordered \(\ell_{2}\) regularization in our method instead of \(\ell_{2}\) regularization because the ordered \(\ell_{2}\) regularization is adaptive. Finally, ADMM has a parallel behavior for solving large-scale convex optimization problems. Our model also employs ADMM and inherits distributed properties of native ADMM [9]. Hence, our model is also distributed. [PERSON] et al. [5] did not provide any details about how they applied ADMM in the ordered \(\ell_{1}\) regularization. In this paper, we propose \"Parameter Estimation with the Ordered \(\ell_{2}\) Regularization via ADMM\" called ADMM-O\(\ell_{2}\) to find relevant parameters from a model. \(\ell_{2}\) is a ridge regression; similarly, the ordered \(\ell_{2}\) becomes an ordered ridge regression. The main contribution of this paper is not to present a superior method but rather to introduce a quasi-version of the \(\ell_{2}\) regularization method and to concurrently raise awareness of the existing methods. As part of this research, we introduced a modern ordered \(\ell_{2}\) regularization method and proved that the square root of the ordered \(\ell_{2}\) is a norm and, thus, convex. Therefore, it is also tractable. In addition, the regularization method used an ordered elastic net method to combine the widely used ordered \(\ell_{1}\) penalty with modern ordered \(\ell_{2}\) penalty for ridge regression. The ordered elastic net is also proposed by the scholars in this paper. To the best of our knowledge, this is one of the first method to use the ordered \(\ell_{2}\) regularization with ADMM for parameter estimation and variable selection. In Sections 3 and 4, we explain the integration of ADMM with the ordered \(\ell_{2}\) and further details about it. The rest of the paper is arranged as follows. Related works are discussed in Section 2, along with a presentation of the ordered \(\ell_{2}\) regularization in Section 3. Section 4 describes the application of ADMM to the ordered \(\ell_{2}\). Section 5 presents the experiments conducted. Finally, Section 6 closes the paper with a conclusion.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 5 HARRF LNA Design
### X-Band LNA Design Procedure
#### 5.1.2 Noise Figure Design Considerations  
In designing LNAs, the noise factor (\(F\)) or noise figure (NF) is of paramount importance. For a transistor, the noise factor can be modelled by:  
\[F(\Gamma_{S})=F_{\text{min}}+R_{n}\big{|}\Gamma_{S}-\Gamma_{\text{opt}}\big{|}^{2}, \tag{40}\]  
where \(F_{\text{min}}\) is the minimum noise factor achievable by the device, \(\Gamma_{S}\) is the source reflection coefficient, \(R_{n}\) is the equivalent noise resistance, and \(\Gamma_{\text{opt}}\) is the reflection coefficient at which \(F_{\text{min}}\) occurs. By employing inductive source degeneration in the first stage and carefully designing the matching networks, the LNA is able to achieve a noise figure close to \(F_{\text{min}}\).
## 4 Experimental Results
### Al 6061-T6 with One Rough Surface and One Smooth Surface  
Next, we examined the case where either the transmission or reception surface was rough for Al 6061-T6 specimens, as shown in Figure 1b. Figure 9a shows the absolute and relative nonlinearity parameters for a specimen with a rough transmission surface (Ra = 0.5, 1.0, 1.5, 2.2, and 2.7 \(\upmu\)m) and smooth reception surface (Ra = 0.5 \(\upmu\)m). The absolute and relative nonlinearity parameters exhibit no noticeable changes with the increase in the surface roughness. Figure 9b shows the absolute and relative nonlinearity parameter results for specimens with a rough reception surface (Ra = 0.5, 1.0, 1.5, 2.2, and 2.7 \(\upmu\)m) and smooth transmission surface (Ra = 0.5 \(\upmu\)m). In other words, the positions of the transmitting and receiving transducers are reversed. The absolute nonlinearity parameter does not change noticeably as the surface roughness increases, whereas the relative nonlinearity parameter increases as the surface roughness increases beyond a certain level (Ra > 1.5 \(\upmu\)m). These variations in the nonlinearity parameter are because of the increased attenuation and scattering at higher frequencies and the difference in the receiving sensitivity caused by the frequency, as described above. These experimental results indicate that the roughness of the reception surface has more influence on the nonlinearity parameter measurements. We inferred that the amplitude of the ultrasonic wave, which is smaller in the receiving part than in the transmitting part, was significantly influenced by the surface roughness.  
Figure 8: (**a**) Received pulse-echo signals and (**b**) spectrum of the received signal.  
Figure 9: Experimental results of the absolute and relative ultrasonic nonlinearity measurements on (**a**) rough transmission surface and (**b**) rough reception surface.
## 5 Adsorption Isotherms Models
### Freundlich Adsorption Isotherm Model  
In the Freundlich isotherm model, maximum adsorption takes place on the surface of an adsorbent by forming multiple layers. This isotherm is related to heterogeneous surfaces. It is assumed that multiple layers are formed on the surface of the adsorbent, and solute particles are constantly bound to the surface of the adsorbent.  
This isotherm can be described by the equations given below.  
\[\mathrm{q_{e}=K_{f}\,C_{e}^{1/n}} \tag{1}\]  
Linear equation;  
\[\mathrm{logq_{e}=logK_{f}+logC_{e}} \tag{2}\]  
The quantity of solute adsorbed onto the adsorbent at adsorption equilibrium is represented by \(\mathrm{q_{e}}\) (mg/g, mmol/g); \(\mathrm{C_{e}}\) (mg/L, mmol/L) is the solute concentration in  
Figure 16: Schematic representation of possible adsorption mechanism for As\({}^{5+}\) removal on the surface of PAN/GO/\(\gamma\)- Fe\({}_{2}\)O\({}_{3}\) nanofiber Membrane. Reproduced with permission from reference [124].  
solution at adsorption equilibrium; K\({}_{\rm f}\) (mg/g) indicates increased adsorption ability of the adsorbent; and n (L/mg) is the Freundlich isotherm constant. The value of 1/n indicates the feasibility of the process. If its value is less than 1, it means adsorption is chemical; and if its value is more than 1, it indicates the process of adsorption is physical, if 1/n =0, it means the process is irreversible.  
The limitation of this model is that it is applicable over a certain range of pressures and concentration only. This model fails when the concentration of the adsorbent is very high. It is a purely empirical model without any theoretical basis [135].
## 1 Introduction
Antenna arrays with a large number of antenna elements will facilitate both the path loss compensation and the management of the inter-user interference, by taking advantage of sophisticated precoding solutions such as the Signal-to-Leakage-plus-Noise ratio (_SLNR_), Zero-Forcing (ZF) [15], or Minimum Mean Square Error (MMSE) [16] precoding schemes. In particular, _SLNR_ precoding [17] addresses the minimization of the interference caused by a user device (leakage) to all other devices in the corresponding frequency or time resource, while maximizing the received desired signal power for every user. A generation of _SLNR_ precoding to MIMO orthogonal frequency division multiplexing (OFDM) multi-user systems is studied in [18]. In [19], the authors prove the equivalence between MMSE and _SLNR_ precoding schemes under equal power allocation (EPA). In [20] an enhanced _SLNR_ precoding, called layer _SLNR_, deals with the case of multiple data streams for each user, by minimizing the inter-layer interference both from the same user and from different users. The application of MU-MIMO techniques to realistic 3-D channel models is somewhat limited in the literature. In this paper, we focus on a Multi-User Multi-Layer downlink communication scheme: we adopt a SLNR precoder at the transmitter with an approach similar to [20], and we implement a MMSE receiver or combiner at each receiver. The adopted communication channel is the 5G-compliant 3 GPP spatial channel model described in the Technical Report 38.901 entitled \"Study on channel model for frequency spectrum above 6 GHz\" [21].
# AI-Based Prediction of Myocardial Infarction Risk as an Element of Preventive Medicine
### Literature Review  
A review of the scientific literature revealed 225 publications with the keywords'myocardial infarction' and 'artificial intelligence' between 1986 and 2021. The number of publications on the subject is growing rapidly: 176 were published in the last 10 years (78.22 per cent), and 156 in the last five years (69.33 per cent). Thirty (13.33 per cent) of these were reviews, but only two were meta-analyses. There is therefore a lack of cross-sectional papers that allow far-reaching prognostic conclusions to be drawn that are also relevant to individual, specific cases. Advances in hardware and software have led to changes in the diagnosis of myocardial ischemia, particularly in the areas of:  
* rapid assessment of cardiac function.
* the ability to detect small changes in the myocardium.
* the combination of anatomical and functional assessments of coronary artery stenosis using a single method which was previously not possible in a non-invasive manner.
## 2 Description of Flywheel Energy Storage Systems
### Structure and Components of FESS: Priciples and Components of FESS
#### 2.2.3 Power Electronics Interface  
A bi-directional converter and an electrical machine are used to accomplish energy conversion in a FESS. DC-AC, AC-DC-AC, AC-AC or a combination of these power electronics topologies can be used with FESS applications. The back-to-back (BTB) or AC-DC-AC switching combination, connected to a DC link capacitor, is the most widely used power converter configuration with FESS. In the BTB topology, the converters are the three-phase bridged semiconductor switch type, often controlled using the pulse width modulation technique (PWM) [35]. Through the modulation of the width of rectangular pulses applied to the gates/bases of power semiconductors, space vector pulse width modulation (SVPWM) is used to control inputs to power electronic converters, and can therefore effect changes in electrical energy, such as the conversion from DC to sinusoidal single phase or three phase AC [9; 41]. The operation of the MG and flywheel is controlled by a machine side converter, whereas a grid side converter is used to maintain the DC link voltage.  
DC-DC boost converters are employed in cases where the FESS' discharge speed is low, and the output voltage needs to be maintained. They would be connected between the BTB converters at the DC link to regulate the output voltage. A switch can be used to bypass the DC-DC boost converter during the charging phase [42].  
Current ripples at the DC bus can be attenuated by using higher switching frequencies for both rectifier and inverter. An improved sinewave shape is produced due to the reduction of higher order harmonics because of faster converter switching times. This, in turn, can increase control bandwidth, but as a consequence, there will be additional switching losses. AC filters introduced on the converter AC side can assist to further reduced harmonics. Filters can also assist in the reduction of current ripples, winding deterioration, and losses [43; 44].
## 2 Materials and Methods  
### Applied Steel and Specim Preparation  
Recent research has focused on examining the residual stress distribution characteristics of the weld nugget in spot welding, a process widely used in automotive structural welding. This study used high-strength steel with a yield stress of 1300 MPa, developed specifically to reduce the weight of car bodies. The mechanical properties of the automotive high-strength steel employed are presented in Table 1. The steel exhibits a high yield strength (approximately 1300 MPa) and a tensile strength of approximately 1500 MPa, making it one of the strongest steels recently applied to automotive structures.  
The welding conditions used for specimen preparation are detailed in Table 2. A DR-type Cu-Cr electrode with a 6 mm tip was utilized, alongside an electrode force of 3730 N, a current of 3400 A, a squeeze time of 5 cycles/50 Hz (0.10 s), a holding time of 10 cycles/50 Hz (0.20 s), and a welding time of 14 cycles/50 Hz (0.28 s). These conditions were selected to achieve a nugget size of \(3\sqrt{t}\). Figure 2 illustrates the specimen geometry, which had a total length of 100 mm and comprised sheets with a thickness of 1.6 mm. To simulate gaps that may occur during actual car body welding, a spacer of the same thickness (1.6 mm) was inserted between the sheets. The spacer was secured using tack welding at both ends via spot welding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Yield Stress (MPa)** & **Tensile Stress (MPa)** & **Elongation (\%)** \\ \hline Mart steel & 1300 & 1500 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of martensitic steel.  
Figure 2: Schematic of specimen dimensions.  
Figure 3 shows the Vickers hardness distribution of \(3\sqrt{t}\) (\(t\): thickness) nugget size in SPOT welding. In this study, the hardness of the weld nugget was found to be higher than that of the parent metal, indicating superior strength in the nugget region. Conversely, the heat-affected zone (HAZ) exhibited a slight reduction in strength compared to the parent metal. Specifically, the nugget's strength was approximately 117% of the parent metal's strength, the HAZ adjacent to the parent metal was about 80%, and the HAZ near the nugget was around 122%. These findings suggest that the HAZ, particularly the region closer to the parent metal, may be more susceptible to failure due to its comparatively lower strength. This variation in hardness and strength across different weld regions is consistent with observations in similar studies. For instance, research on resistance spot welding of Zn-Al-Mg galvanized steel reported that the fusion zone (nugget) exhibited higher hardness due to the formation of lath martensite, while the HAZ showed varying hardness levels depending on its proximity to the nugget and base metal [20].
# On the Design and Implementation of a Blockchain-Based Data Management System for ETO Manufacturing
## 2 Background and Related Works
### Sequential Aggregate Signature  
An aggregate signature is a cryptographic primitive that can aggregate different signatures of multiple signers into a single signature [48]. Since the size of the aggregate signature remains the same as that of a single signature, it can effectively reduce the communication cost when an authenticated message must be forwarded from one partner to another. A sequential aggregate signature is a variant that supports data aggregation that depends on the order of the parties. It relies not only on the public data but also on the order of all previously aggregated data. The sequential aggregate signatures play a key role in situations such as verifying routing information or certificate chains, where the verification of the order of signature steps is important.  
In the ETO-type production model, the sequential execution of discrete production process routes needs to be guaranteed. By integrating the sequential aggregate signature into the blockchain, verification and traceability of the execution steps of the production process route is ensured. Generally, a sequential aggregate signature scheme consists of three parts: key generation algorithm, signature aggregation algorithm, and aggregate verification algorithm. Specifically, when the signer receives an ordered set of public keys PK=(pk1,...pki) and messages M=(m1,...mi), and an aggregate signature corresponding to the sequence, the signer uses its own private key, \(sk\), to derive a new aggregate signature on its message, m, using an aggregation algorithm, which takes m#M and pk#PK as input parameters. In our proposed scheme, the sequential aggregate signature designed by [PERSON] in [49] is adopted; this is mainly constructed based on bilinear mapping, and its security has been proved theoretically [49,50].