esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art
### Heterogeneous Routing Protocols  
This set of protocols is applied to networks where the nodes that form them are of different natures, i.e., the network can be formed by fixed or mobile nodes on the ground and the aerial nodes of a FANET. This architecture has several advantages including increased coverage and performance. Within this set of protocols, Connectivity-Based Traffic Density Aware Routing Using UAVs for VANETs (CRUV) [55] stands out. CRUV relies on the existence of a DTN VANET supported by a FANET, which allows the interconnection of the DTN. In addition, there are also the following: UAV-Assisted VANET Routing Protocol (UVAR) [56], Position-Aware Secure and Efficient Routing (PASER) [57], Cross-Layer Link Quality and Geographical-Aware Beaconless (XLinGo) [58], and Secure UAV Ad Hoc Routing Protocol (SUAP) [9].
# Marine Internet for Internetworking in Oceans: A Tutorial
## 5 Internetworking Strategies of Marine Internet
### Versatile Auxiliaries of WANETs  
As mentioned in Section 4.6, there are many types of WANETs in oceans, which can be used jointly with integrated coastline networks (ICNs) to intensify service density and expand service coverage as well as provide opportunistic networking services, as discussed below.  
* A WANET is used to provide communication between nodes in the same WANET mainly for intra-vessel communication in oceans. This is particularly useful for cooperative operations among many vessels.
* In the coverage of an ICN, there may be small boats that cannot connect to it directly. If there are some ships able to link the ICN around these boats, they can connect these ships in order to access the ICN through WANETs. These boats themselves can also form a WANET. Similarly, the coverage of an ICN can be extended beyond its original coverage through WANETs so that vessels far away from the ICN may also have access to it. If necessary, a HAP can be developed occasionally for temporary operations.
* Opportunistic networking services can be provided via WANETs such as AANETs mentioned above without long-term connection between nodes. For example, aerial units communicate with marine Internet users to collect data, and then look for chance to deliver or relay data toward the destination. This kind of network is particularly useful when neither ICNs nor NANETs are available, and may become the only chance for a node to communicate with others.
# Sequential Polar Decoding with Cost Metric Threshold
## 1 Introduction  
Polar codes [1] achieve near-Shannon-limit performance while maintaining low encoding and decoding complexity, making them ideal for resource-constrained devices. However, their practical utility for short to moderate code lengths has been hampered [2] by the comparative inefficiency of the Successive Cancellation (SC) decoder. The Successive Cancellation List (SCL) decoder, introduced by [PERSON] and [PERSON] [3], follows a similar path-wise traversal strategy as SC but maintains a list of up to \(L\) candidate paths for further exploration. The Successive Cancellation Stack (SCS) decoder [4] offers improved error correction ratios and throughput; however, this achievement comes with the trade-off of heightened space complexity. Another noteworthy development is the SC-Fano decoding algorithm [5], which integrates sequential decoding concepts into the polar decoding traversal procedure. Importantly, some of the strategies employed in these decoding algorithms have their origins in the field of convolutional coding [6], showcasing the interplay between diverse coding techniques.  
In this study, we investigate an alternative approach to polar code decoding--the Creeper algorithm [7], originally explored for convolutional codes. The SC-Creeper decoder is engineered for low decoding complexity, making it amenable to resource-constrained environments. The SC-Creeper approach [8; 9] represents a novel decoding algorithm, offering an enticing blend of enhanced performance and low computational complexity, rendering it a compelling candidate for practical implementation. This work explores the utilization of the SC-Creeper algorithm for polar decoding and introduces a modified version, leveraging additional threshold values for special nodes in the coding tree, to significantly improve its error correction capabilities.
## 4 Results and Discussions
### Comparision with Other Author Results  
To verify the validity of the joint time-frequency analysis method, we used the proposed EEMD-WVD combined method to obtain the time-frequency characterization of vertical axle-box acceleration caused by areal polygonized wheel, as reported in [27]. The results (Figure 11a) reveal three thin horizontal lines in the spectrogram at the characteristic frequencies of 31, 710, and 740 Hz. Furthermore, axle-box vibration caused by a random polygonal wheel is also reflected in the time-frequency-energy spectrogram (Figure 11b), with the highest amplitude occurring around 740 Hz, the second highest occurring around 710 Hz, and the third highest occurring around 31 Hz. These results imply that the random polygonized wheel is dominated by harmonics 1, 23, and 24 around the wheel circumference, which is consistent with the field measurements reported in [27].  
As mentioned above, [PERSON] al. [18] based their research on detecting railway out-of-round wheel defects on the Hilbert-Huang transform (HHT). However, the HHT cannot assess the severity of wheel defects. The analytic results presented herein illustrate that EEMD-WVD allows us to detect wheel polygons not only by suppressing the WVD crossterms but also by retaining the time-frequency concentration and resolution.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 4 Receiver Design
### Semi-Analytical Performance Approximation  
From \(\mathbf{A}\mathbf{A}^{T}=\mathbf{I}_{NM}\) and \(\sum\limits_{l=0}^{NM-1}\mathbf{E}_{l+1}=\mathbf{I}_{NM}\), it can be proven that \(\sum\limits_{l=0}^{NM-1}\left(\mathbf{A}^{T}\mathbf{E}_{l+1}\left(\mathbf{A} ^{H}\right)^{T}\right)=\mathbf{I}_{KM}\); therefore, we can rewrite \(\mathbf{D}^{T}\) as  
\[\mathbf{D}^{T}=\sum\limits_{l=0}^{NM-1}\left(\mathbf{D}^{T}\mathbf{A}^{T} \mathbf{E}_{l+1}\left(\mathbf{A}^{H}\right)^{T}\right). \tag{21}\]  
From Equation (20) and Equation (21), we obtain the error \(\mathbf{\Delta}_{D}=\hat{\mathbf{D}}^{T}-\mathbf{D}^{T}\), given by  
\[\mathbf{\Delta}_{D}=\underbrace{\sum\limits_{l=0}^{NM-1}\left(\left(\mathbf{W} _{d,l}(\mathbf{W}_{a})^{H}\mathbf{H}_{l}-\mathbf{I}_{U}\right)\mathbf{D}^{T} \mathbf{A}^{T}\mathbf{E}_{l+1}\left(\mathbf{A}^{H}\right)^{T}\right)}_{\text{ Residual ISI}}+\underbrace{\sum\limits_{l=0}^{NM-1}\left(\mathbf{W}_{d,l}( \mathbf{W}_{a})^{H}\mathbf{N}\mathbf{E}_{l+1}\left(\mathbf{A}^{H}\right)^{T} \right)}_{\text{Channel noise}}, \tag{22}\]  
where we can identify two contributions: (1) the residual ISI, and (2) the part corresponding to the channel noise. From Equation (22), we can prove that the mean square error (MSE) is  
\[\text{MSE}=\mathbb{E}[\|\mathbf{\Delta}_{D}\|_{F}^{2}]=\sum\limits_{u=0}^{U-1 }\sum\limits_{l=0}^{NM-1}\text{MSE}^{(u,l)}, \tag{23}\]  
Figure 2: Block diagram of the receiver. ADC (analog to digital converter).  
where  
\[\text{MSE}^{(u,l)}=\sigma_{d}^{2}\Big{(}\mathbf{w}_{d,u,l}^{T}(\mathbf{W}_{a})^{H }\mathbf{H}_{l}-\mathbf{e}_{u}^{T}\Big{)}\Big{(}\mathbf{H}_{l}\mathbf{W}_{a}( \mathbf{w}_{d,u,l}^{T})^{H}-\mathbf{e}_{u}\Big{)}+\sigma_{n}^{2}\mathbf{w}_{d,u,l}^{T}(\mathbf{W}_{a})^{H}\mathbf{H}_{l}\mathbf{W}_{a}(\mathbf{w}_{d,u,l}^{T} )^{H}, \tag{24}\]  
where \(\mathbf{W}_{d,l}=\left[\mathbf{w}_{d,1,l},\ldots,\mathbf{w}_{d,U,l}\right]^{T}\) and \(\mathbf{e}_{u}\in\mathbb{C}^{U}\) is a unit vector with entry \(u\) equal to one, while all others are zeros. From Equation (24), we can obtain a semi-analytical BER approximation for an M-QAM constellation with Gray mapping, given by [35]  
\[\text{BER}=\frac{\alpha}{UNM}\sum\limits_{u=0}^{U-1}\sum\limits_{l=0}^{NM-1}Q \left(\sqrt{\beta\Big{(}\text{MSE}^{(u,l)}\Big{)}^{-1}}\right), \tag{25}\]  
where \(\alpha=4(1-1/\sqrt{M})/\log_{2}[M]\), \(\beta=3/(M-1)\), and \(Q(.)\) denotes the Q-function.
# Flavi Classification Algorithm for Heat Exchanger
## 2 Principles
### Measuring Alterating Magnetic Fields Using GMR Sensors  
When a primary T-EMF is applied to the bobbin coil as described above, an induced current and a secondary T-EMF are generated in the conductive HX tube. By the same principle, when the primary T-EMF in the opposite direction is applied to adjacent differential-type bobbin coils, the secondary T-EMF is canceled in the center of the coils and this generates an annular region where the output becomes zero. This secondary T-EMF balance collapses as it passes over the flawed area, as shown in Figure 1. At this instance, if a device that measure the magnetic flux density (MFD), such as a GMR sensor, is placed in the center of the differential-type bobbin coils and scanned inside the tube in the axial direction (z-direction), the MFD distribution can be measured.  
The electrical signal measured by a GMR sensor is represented by Equation (1) [9]. \(V_{G}\) represents the output voltage of a GMR sensor. The orientation of the GMR sensor is sensitive to changes in the z-axis MFD, resulting in the measurement of \(B_{Z}\), which is the MFD in the z-direction. \(C_{1}\) and \(C_{2}\) are constants representing the intrinsic characteristics of the GMR sensor, and \(p\) is the input voltage. Equation (1) shows that the output of the GMR sensor increases nonlinearly with respect to the MFD regardless of the magnetic poles.  
Figure 1: 3D illustration of coil and sensor array of bobbin-type magnetic camera and magnetic flux density measurement principle around a flaw.  
\[V_{G}(r,\phi,z)=C_{1}\times p\times(1-e^{-B_{z}^{2}})+C_{2} \tag{1}\]  
Furthermore, because of the flaw in the tube, the T-EMF changes both the amplitude phase angle of the output signal. The amplitude and phase angle can be calculated by multiplying the output signal obtained by Equation (1) with the input signal (sin \(\omega\)) and the 90\({}^{\circ}\) leading input signal (cos \(\omega\)), respectively, and then integrating the results. Figure 2 shows the procedure for calculating the real and imaginary parts of the output signal by synthesizing the cosine and sine signals. The real part, imaginary part, amplitude and phase angle of the output signal can be calculated using Equations (2)-(5). In this study, the amplitude and phase angle difference distributions measured by the GMR sensors along the inner wall of the tube specimen were graphed on a two-dimensional plane with the sensor array axis (\(\phi\)) and longitudinal (scan) axis (\(z\)).  
\[Re(r,\phi,z)=\int_{0}^{2\pi}V_{G}(r,\phi,z)\sin\omega t\ dt \tag{2}\]  
\[Im(r,\phi,z)=\int_{0}^{2\pi}V_{G}(r,\phi,z)\cos\omega t\ dt \tag{3}\]  
\[Z(r,\phi,z)=\sqrt{Re(r,\phi,z)^{2}+Im(r,\phi,z)^{2}} \tag{4}\]  
\[\Phi(r,\phi,z)=tan^{-1}\frac{Im(r,\phi,z)}{Re(r,\phi,z)} \tag{5}\]
## 5 Seismic Vulnerability Assessment of the Area
### Vulnerability Index Method as a Basis for Seismic Risk Evaluation  
Although the vulnerability index is an important indicator of seismic risk, it should be noted that it is useful for a relative comparison of the seismic performance of buildings in the case of equal seismic action. According to modern seismic regulations, such as Eurocode 8 [37], seismic action varies even for constant seismic hazard of the area because it depends on the soil type and the importance factor of the building. More precise information on the behavior of the building subjected to a certain earthquake action can be obtained either by evaluating the damage or by assessing the risk induced by an earthquake of a specific intensity. The former approach establishes a relation between the vulnerability index, the intensity of seismic action and structural damage using the post-earthquake damage observations [1,25] or, alternatively, performing non-linear pushover analyses [27,33]. Seismic risk can also be expressed in terms of the index of seismic risk (or index of seismic safety), expressed as the ratio of the peak ground acceleration achieved to the structural collapse and the demand ground acceleration.  
The present study aims to evaluate seismic risk both in terms of the damage index and the index of seismic risk using information about vulnerability of the buildings obtained by vulnerability index method. In order to calculate the damage index, the relation between the vulnerability index and the seismic capacity represented by peak ground acceleration for the sample of 18 buildings has been established. Non-linear static (pushover) analysis has been applied for the calculation of peak ground acceleration for different limit states of buildings (early damage, significant damage, and near collapse). Vulnerability index--peak ground relations enable the calculation of the damage index of the building, but also the calculation of the index of seismic risk for each limit states.  
Figure 7: Vulnerability index map of the test site. (**a**) Division into four vulnerability classes. (**b**) 10% division intervals.
## 1 Introduction  
The implant abutment is the connecting link between the implant platform and the crown. An implant prosthesis with a properly selected type of abutment ensures functional stability, adequate soft tissue support, and an emergence profile [1; 2].  
An implant prosthetic suprastructure may be a weak point due to excessive masticatory forces. Overloading can lead to abutment fracture, which prevents the implant and peri-implant complex from complications [3].  
Appropriate abutment selection according to the individual specificities of each patient is a leading factor in achieving high aesthetic results [4]. Titanium abutments are defined as the \"gold standard\" for all areas in the mouth [5; 6; 7; 8; 9]. Standardized titanium abutments are the most widely used due to their easy use and low cost [10; 11]. In 1996, [PERSON] [12] presented a clinical case report on the prosthetic restoration of an upper central incisor made with an individualized titanium abutment, in which the author emphasized the advantages of this type of restoration over standardized types. Despite efforts to unify and standardize prosthetic treatment, very often, factory solutions cannot meet the individual needs and personal requirements of each patient. In some clinical cases, a standard abutment cannot ensure the most favorable position for the future restoration and gingival architectonics [13; 14]. The VAD (virtual abutment design) concept, which is a part of CAD technology, is based on the understanding that the shape of an abutment is determined by the morphology of the restoration it will support [15]. A number of authors believe that customized abutments can eliminate many of the complications seen with factory abutments in prosthetic treatment [1; 12; 16; 17; 18]. Fracture of implant structures is a common problem [19]. The mechanical behavior of different types of abutments in laboratory settings can provide clear guidelines for their application in clinical settings and important information regarding their advantages and disadvantages [20].  
Several studies have reported that the in vitro testing of cyclic load and compressive force on an implant-prosthetic restoration complex leads to the deformation of standardized implant components and reduces their functional stability [21; 22; 23]. It is unclear whether the mechanical strengths of customized titanium alloy abutments and new CAD/CAM restorative materials have advantages compared with prosthetic restorations with standard titanium bases. The resolution of this would facilitate the choice of prosthetic components in implants and would create the conditions for more predictable and long-lasting results of prosthetics in implants. The purpose of this study is to evaluate whether customized abutments have better mechanical behavior in laboratory settings than standard ones. The null hypothesis is that customized abutments have better resistance to cyclic load and compression than factory abutments.
## 6 Performance Analysis
### Functional Performance  
We evaluated the functional performance of the SNs in a fully configured IoT network. We used seven SNs to collect data from different types of sensors and send them to the IoT server through two data sinks. The system was also configured to form the network in two different ways: star networks (SN to DS direct link) and tree networks with the DS using any one SN as the RN, where one SN does not have direct wireless connectivity with the DS. Data from a SN were acquired at three-second intervals, which is a very high rate compared to 15-min intervals; thus, more than 800 packets were collected during the two-day experiment. Figure 10a shows the pH and turbidity data taken from the South Saskatchewan River gathered directly from two different SNs to the DS and via the RN, respectively. Figure 10b shows the air-quality data received from the temperature, pressure, uncalibrated TVOC (total volatile organic compound measure), and humidity sensor on the river bank. Data delivery success rate between the SN and DS was 100%, and through the RN was 99%.
# An Extended Proxy-Based Sliding Mode Control of Pneumatic Muscle Actuators
## 2 Dynamics of PMA  
So far, except the FESTO AG Company's demonstrations, PMAs have not been standardized as commercial products and in applications containing such actuators. Therefore, it is of great importance to choose suitable PMA that fits the application and use the correct mathematical PMA model. The high nonlinearities due to the existence of the pressurized air, the elastic-viscous material and the geometric features of PMA are the first problem that a control engineer has to deal with in order to derive and use a proper PMA mathematical model.  
Two main categories for the mathematical models of a PMA are prevalent: the theoretical and the phenomenological models. The theoretical models, which are derived from the law of energy conservation, describe the PMA behavior based on quasi-static states without the inclusion of explicitly temporal information. However, this approach limits its application for real-time control. This is because it is too complex in structure and requires too many parameters that are difficult to obtain during experimentation.  
In this study, we adopt a phenomenological model as a combination of effects from nonlinear friction, spring and contraction components to describe the dynamic behavior of a PM pulling a mass against gravity (shown in Figure 1).  
The coefficients related to these three elements depend on the input pressure of the PM. The equations describing approximately the dynamics of a PM are given by:  
Figure 1: The FESTO PMA and their three-element models.  
\[M\ddot{x}+B(P)\dot{x}+K(P)x=F(P)-Mg \tag{1}\]  
\[K(P)=K_{0}+K_{1}P \tag{2}\]  
\[B(P)=B_{0i}+B_{1i}P(\text{inflation}) \tag{3}\]  
\[B(P)=B_{0d}+B_{1d}P(\text{deflation}) \tag{4}\]  
\[F(P)=F_{0}+F_{1}P \tag{5}\]  
where \(M\) is the mass of PMA. \(g\) denotes the acceleration of gravity. \(P\) is used to denote the input pressure. \(x=0\) corresponds to the fully deflated position of PMA, and the coefficients \(K(P)\) and \(B(P)\) are pressure-dependent representing the spring and the damping elements, respectively. The contractile element presented the effective force \(F(P)\). The damping coefficient depends on whether the PM is inflated and deflated.  
From Equation (1), the dynamics of PMA can be rewritten as the following standard single-input-single-output (SISO) second-order nonlinear system:  
\[\ddot{x}=f(x,\dot{x})+b(x,\dot{x})u(t)+d(x,\dot{x},t) \tag{6}\]  
where  
\[f(x,\dot{x})=\frac{1}{M}\left[F_{0}-B_{0}\dot{x}-K_{0}x-Mg\right], \tag{7}\]  
\[b(x,\dot{x})=\frac{1}{M}\left[F_{1}-B_{1}\dot{x}-K_{1}x\right], \tag{8}\]  
and  
\[\left\{\begin{array}{ll}B_{0}=B_{0i},\,B_{1}=B_{1i}&if\,\,\,\dot{x}>0\,\,\, \text{(inflation)}\\ B_{0}=B_{0d},\,B_{1}=B_{1d}&if\,\,\,\dot{x}<0\,\,\,\text{(deflation)}\\ B_{0},\,B_{1}\,\,\,\text{hold}&if\,\,\,\dot{x}=0\end{array}\right. \tag{9}\]  
In Equation (6), it should be noticed that all the uncertainties including modeling errors and external disturbances are lumped into a single term \(d(x,\dot{x},t)\).
# A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things
## 3 Blockchain Platforms
### Algorand  
Algorand was introduced in 2019 by [PERSON] and specifically engineered to prioritise reduced latency, security, and decentralisation [64]. This is achieved by employing inventive consensus mechanisms and cryptographic methodologies. Security components in the Algorand blockchain are cryptographic Sortition (Verifiable Random Function), PoS consensus mechanism, and other encryption mechanisms (Hash Function and Edwards-Curve Digital Signature (EdDSA)). Algorand is suitable for various blockchain applications, including financial services, supply chain management, and IoT [65]. The Algorand Blockchain is gaining prominence as a promising technology for the Internet of Things (IoT) owing to its self-sustaining and decentralised characteristics [66]. It has been implemented in low-powered IoT devices such as the Raspberry Pi 4 Model B and the STMicroelectronics STM32 MP157A-DK1 [66]. A multi-level blockchain structure and consensus algorithm have been put forth to enhance the security and dependability of IoT data management [24,67]. Additionally, blockchain-based data verification schemes can safeguard data integrity in IoT by employing distributed data authentication and lightweight mining processes, as proposed by [PERSON] et al. [65] in their work. [PERSON] et al. [68] used Algorand together with Messaging and Queues Telemetry Transport (MQTT), and Transport Layer Security (TLS), to solve a major authentication issue in an IoT electrical energy system.
## 3 Materials and Methods
### Analysis of the Volatile Fraction
#### 3.4.2 GC-MS Analysis of EOs  
The qualitative chemical compositions of the EOs from the 9 varieties were evaluated through an Agilent 8890 GC, with a single quadrupole Agilent 5977B Mass Spectrometer (MSD) (Santa Clara, CA, USA), and a PAL RTC 120 autosampler (CTC Analytics AG, Zwingen, Switzerland). The non-polar HP-SMS (5% phenylethylpolysiloxane; length: 30 m, 0.25 mm i.d., 0.25 \(\upmu\)m f.t.) and the polar DB-WAX (polyethylene glycol; length: 60 m, 0.25 mm i.d., 0.25 \(\upmu\)m f.t.) columns were employed as stationary phases. Helium (He) flow rate was 1 mL/min. The oven temperature programs were set as below: for the HP-5 MS column, 60 \({}^{\circ}\)C for 5 min, then up to 220 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, and on to 280 \({}^{\circ}\)C at 11 \({}^{\circ}\)C/min, for 15 min, and finally to 300 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min for 0.5 min; for the DB-WAX column, 60 \({}^{\circ}\)C for 5 min, increased to 220 \({}^{\circ}\)C at 4 \({}^{\circ}\)C/min, and later to 250 \({}^{\circ}\)C at 11 \({}^{\circ}\)C/min, for 15 min. The EOs were diluted 1:100 in \(n\)-hexane (LC-MS) and the injection was done in split mode (1:200). Data were acquired in SCAN mode (40-400 \(m\)/z) and analyzed through MSD ChemStation software (Agilent, Version G1701 DA D.01.00), using the NIST Mass Spectral Search Program for the NIST/EPA/NIH EI and NIST Tandem Mass Spectral Library v. 2.3. A mix of \(n\)-alkanes (C\({}_{8}\)-C\({}_{30}\), Supelco, Bellefonte, CA, USA) was injected to calculate linear retention indices (RI), and EO constituents were identified by checking the correspondence between their RI and mass spectra (MS) and those of commercial libraries, in particular ADAMS in the case of the analysis performed with the HP5-MS column, and NIST 17 when the DB-WAX column was used [62; 63]. Relative abundance (peak area percentages) was obtained by normalization without using correction factors.