esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[6] employed the dielectric barrier discharge (DBD) for studying the effects of plasma treatment on the morphology and sorption properties of hemp fibers under different conditions (40 W and 80 W powers at constant treatment time of 120 s). The wettability test of the plasma-treated samples showed a considerable increase compared to untreated fibers, which correlates with the chemical composition change of the processed hemp fiber surface, as demonstrated by the ATR FTIR results, and to the rise in fiber surface roughness owing to the intensive etching visible in the SEM images. Plasma treatment was proven to positively affect the water sorption properties of raw hemp fibers and could replace the use of chemicals for many fiber applications. [PERSON] et al. [17] studied the effect of the plasma treatment on the nanoscale surface structure of ramie fibers and their influence on fiber-polymer adhesion. They found filament-like nanostructures on the surface of the ramie fibers within 1 min of plasma treatment. These structures dominate the mechanical properties of the fibers. The consequent nanoscale surface roughness due to the microfibrillation of cellulose increased the surface area, which, in turn, improved the amount of water contained in it as well as the interactions between the fiber and polymer. Thus, using plasma surface modification for fiber-polymer composites can be advantageous for attaining a better interface integrity. The objective of this research study is to fill the aforementioned knowledge gap by examining the effects of various DBD plasma treatment parameters (treatment time, discharge power, and gas composition) on the surface properties and mechanical characteristics of HF and HF-loaded epoxy composites. Optimizing these parameters through response surface methodology (RSM) can give a significant information to customize the properties of HF composites for particular applications. These results enhance the development of HF-plasma-treated sustainable composite materials. The enhanced interfacial adhesion and mechanical properties obtained by plasma treatment could bring further applications for HF composites in the automotive, construction, and packaging sectors. In addition, DBD plasma treatment is environmentally friendly and in line with the increasing acceptance of green and sustainable manufacturing processes in the composites industry.
## 4 Experimental Results
### Ablation Study of Mutual Learning between Two Back-Projection Units  
Inspired by [20], we argue that mutual learning between the two back-projection units will improve their performance, as it facilitates information exchange. We performed an ablation study for the mutual-learning method, in which two back-projection units were learned independently, as shown in Figure 7. As illustrated in Table 3, the mutual-learning method performed better than the independent-learning method.
## 3 Temperature Prediction Model for Subgrade
### Setting Hyperparameters for the NARX Network Prediction Model  
The performance of the NARX network is significantly influenced by the determination of hyperparameters, i.e., input delay (\(dx\)), output delay (\(dy\)), and the neuron number in the hidden layer (\(N\!I\!n\)). Ideally, the delays should encompass all significant lags while being minimized to reduce the network's scale, thus avoiding decreased computational speed, increased risk of overfitting, and additional errors. This study assumes a single hidden layer with equal input and output delays. The approximate range for the neuron number in the hidden layer is estimated by the following empirical formula [46]:  
\[L<\sqrt{NX+NY}+\alpha \tag{3}\]  
where \(L\) represents the neuron number in the hidden layer, \(NX\) is the number of network inputs, \(NY\) is the number of network outputs, and \(\alpha\) is a constant between 0 and 10. Based on Equation (3), the range of the neuron number in the hidden layer can be from 2 to 11. All possible model combinations were compared using the trial-and-error method to determine prediction error, as shown in Figure 8.  
From Figure 8, it is evident that when the neuron number in the hidden layer is 10 and the input and output delays are 7, the model's Mean Squared Error (MSE) is minimized. This suggests that the NARX network model performs optimally when using seven-time step input and output vector sequences for rolling predictions, updating predictions as new information becomes available. Consequently, the NARX network model's input and output delays are set to seven, and the neuron number in the hidden layer is set to ten.
## 4 Performance Analysis
### Transaction Security Analysis
#### 4.2.5 Integrity of Blockchain Transaction  
The stealth address protects the recipient's identity by generating the temporary address for each transaction, meaning outside observers cannot trace the recipient's identity or transaction history through the address. Although the BTPP protocol uses the temporary address, the integrity of the transaction is still verified through the public ledger of the blockchain, ensuring the non-tamperability and integrity of the transaction. In addition, the BTPP protocol utilizes the Pedersen commitment combined with the Bulletproofs range proof to ensure the legitimacy of the transaction amount without revealing any specific information. Although the transaction amount is hidden, through the generated commitments and range proofs, network participants (such as miners or validators) can still verify the validity of the transaction. This means that the transaction still follows the basic rules of the blockchain, such as not exceeding the account balance and maintaining the integrity of the transaction. At the same time, through the note mechanism, the blockchain network can perform necessary compliance and integrity checks, such as preventing double-spending, without revealing the privacy details of the transaction.
## 2 Modeling and Problem Statement
### Dynamic Load Model  
The active and reactive powers in the dynamic load model are functions of the voltage magnitude and frequency at present and past time instants. For this purpose, several load models have been proposed in the literature, but the widely used one is the IM load model, because the physical system includes a large number of single-phase and three-phase IMs, and the IM model is included in stability simulation packages. In system studies, therefore, aggregated IM models are used. The IM loads are of the most significant aspects in terms of dynamical behaviors of the systems. As for the dynamic load behavior, the third-order IM model is employed. The equivalent circuit of the IM is shown in Figure 2.  
In Figure 2, \(V_{\text{S}}\) and \(I_{\text{S}}\) are the phasors for the IM terminal voltage and current, respectively; \(R_{s},\ R_{r},X_{s}\) and \(X_{r}\) are the resistances and reactances of the stator and rotor, respectively. \(X_{m}\) is the magnetizing reactance; \(s\) is the slip, which is \((\omega_{0}-\omega)/\omega_{0}\), where \(\omega_{0}\) is the synchronous speed and \(\omega\) is the rotor speed in electrical radians per second. The third-order model for the dynamic characteristics of an IM is represented by a differential equation representing the internal voltage \(\left(\overline{E^{\prime}}\right)\) in the \(d\)-\(q\) axis transformation, and a rotational equation representing the IM torque and the load torque.  
\[\frac{dE_{d}^{\prime}}{dt}=-\frac{1}{T_{0}^{\prime}}\left[E_{d}^{\prime}+\left( X-X^{\prime}\right)i_{q}\right]+\omega_{0}sE_{q}^{\prime} \tag{3}\]  
\[\frac{dE_{q}^{\prime}}{dt}=-\frac{1}{T_{0}^{\prime}}\left[E_{q}^{\prime}-\left( X-X^{\prime}\right)i_{d}\right]-\omega_{0}sE_{d}^{\prime} \tag{4}\]  
\[\frac{ds}{dt}=\frac{1}{2H}(T_{m}-T_{e}) \tag{5}\]  
Figure 2: Induction motor equivalent circuit.  
\[i_{d}=\frac{1}{{{R_{s}}^{2}}+{X^{\prime 2}}}\Big{[}{R_{s}}\big{(}v_{d}-E^{ \prime}_{d}\big{)}-X^{\prime}\Big{(}v_{q}-E^{\prime}_{q}\big{)}\Big{]} \tag{6}\]  
\[i_{q}=\frac{1}{{{R_{s}}^{2}}+{X^{\prime 2}}}\Big{[}{R_{s}}\Big{(}v_{q}-E^{ \prime}_{q}\big{)}-X^{\prime}\big{(}v_{d}-E^{\prime}_{d}\big{)}\Big{]} \tag{7}\]  
where  
\[T^{\prime}_{0}=(X_{r}+X_{m})/\omega_{0}R_{r}\]  
\[T_{e}=E^{\prime}_{d}i_{d}+E^{\prime}_{d}i_{q}\]  
\[X=X_{s}+X_{m}\]  
\[T_{m}=T_{m0}\Big{[}A(1-s)^{2}+B(1-s)+C\Big{]}\]  
In the above equations, \(T^{\prime}_{0}\) is the transient time constant; \(X\) and \(X^{\prime}\) are the synchronous reactance and transient resistance, respectively; \(E^{\prime}_{d}\) and \(E^{\prime}_{q}\) are d and q-axis transient voltages; \(l_{d}\) and \(l_{q}\) are d and q-axis stator currents; \(v_{d}\) and \(v_{q}\) are d and q-axis terminal voltages; \(H\) is the inertia constant of IM; \(T_{m}\) and \(T_{e}\) are mechanical and electromagnetic torque, respectively. The mechanical torque constants, \(A\), \(B\) and \(C\), are critical with respect to short-term voltage stability. \(T_{m0}\) is the torque when the motor is at synchronous speed.
## 4 Discussion
### Correlation between the Reinforcement Index and the Mechanical Properties of Concrete  
#### 4.1.1 Correlation of the Reinforcement Index and the Compressive Strength of Concrete  
Figure 7 shows the correlation between the Reinforcement Index and the compressive strength of PPFRC. The value of \(f_{c}=42.16\) MPa of mixture B-1.2%-50 was removed because the DFFITS parameter resulted in a value of 1.7 (greater than 0.89). The regression shows a Pearson correlation coefficient of \(-0.79\), resulting in a considerable correlation. The \(p\) value for the Pearson test is 0.011, which is less than the 0.100 defined for this research. Therefore, the null hypothesis of no correlation between the variables is rejected. According to Figure 6, the higher the Reinforcement Index, the lower the compressive strength. Additionally, for each 10-unit increase in the Reinforcement Index, the compressive strength decreases by 0.45 MPa. [PERSON] et al. (2018) [42] and [PERSON] et al. (2013) [43] reported similar results and found a 1-7% reduction in the compressive strength of concrete due to the increment of the \(RI\). However, different trends in this correlation were found in the literature. [PERSON] et al. (2017) [30], [PERSON] et al. (2011) [31], [PERSON] et al. (2008) [32], [PERSON] y [PERSON] (2019) [33], [PERSON] et al. (1996) [34], [PERSON] and [PERSON] (2018) [35], and [PERSON] et al. (2022) [8] have concluded that the addition of plastic macrofibers in low volumes does not have significant effects on compressive strength because it increases less than 10%. On the other hand, [PERSON] and [PERSON] (2009) [44] found that adding fibers in volumes exceeding 2% generates a decrease in compressive strength. One explanation for this phenomenon is that the fibers fundamentally work in traction. Since polypropylene fibers have a low modulus of elasticity, they provide little confinement to the concrete, limiting their influence on compression loads. These fibers occupy a space that does not resist the compressive forces, subtracting a resistant area from the section, which decreases the compressive strength of the section.  
Figure 7: Correlation between the reinforcement index and compressive strength.
## 1 Introduction
### Betaxolol  
The (S)-(-)-form of betaxolol (levobetaxolol), a \(\beta\)-blocker devoid of intrinsic sympathomimetic activity with a cyclopropoyethyl group (Figure 4), was found to be efficient in reducing intraocular pressure in patients affected with primary open-angle glaucoma and ocular hypertension. Marked differences in the activity of different enantiomers were encountered also in atenol, a carbamoylmethyl derivative used in the treatment of angina pectoris and in glaucoma [53; 54; 55].  
Figure 4: Chemical structure of betaxolol.  
Figure 3: Chemical structure of the (S)-(–)-isomer of penbutolol.  
Esmolol  
This \(\beta\)-blocker (Figure 5) with easily hydrolysable ester moiety is a cardioselective ultrashort-acting \(\beta_{1}\)-selective antagonist. The ester group present in the structure of esmolol undergoes rapid hydrolysis in blood, leading to quick onset of the pharmacological effect as well as its rapid abatement. Its metabolite, 3-[4-[2-hydroxy-3-(propan-2-ylamino)propoxy]phenyl]propionic acid, has 400 times lower activity towards \(\beta_{1}\)-receptors than esmolol [56]. Due to its rapid activity onset it is applied as racemate in life-threatening situations such as sudden cardiac arrest. It finds further use in the therapy of ventricular and supraventricular arrhythmias [57; 58]. The results of a study [59] suggest that the antiarrhythmic activity is connected to the inhibitory effect of sodium ions. It could be shown that the blocking activity resides with the (\(S\))-(-)-enantiomer, whereas the (\(R\))-(+)-isomer was found to be inactive. In studies with radioactive tracers esmolol exhibited 34 times higher activity towards \(\beta_{1}\)-receptors in comparison to \(\beta_{2}\)-receptors.  
In the work [60] stereoselective hydrolysis of esmolol in vitro and in vivo was studied. Dog and rat blood esterases hydrolyzed the (-)-enantiomer of esmolol faster than the (+)-enantiomer while rhesus monkey, rabbit, and guinea pig blood esterases hydrolyzed the (+)-enantiomer more rapidly. Human blood esterases did not demonstrate stereoselectivity. Dog liver esterases also showed stereoselectivity towards the (-)-enantiomer but dog skeletal muscle esterases did not.  
In the study [61] the stereoselective pharmacokinetics of enantiomers was compared after a single intravenous administration of \({}^{14}\)C-esmolol hydrochloride to dogs. The findings suggest that pharmacokinetics of the particular enantiomers differs slightly in dog, whereas there are no stereoselective differences in human blood kinetics.
## 3 Materials and Methods
### Satellite Network Architecture
#### 3.2.3 User Segment  
The user segment represents the end-user layer of the satellite network and consists of the _GNSS_, _IoT_, and _UAV_ (Figure 2). The _GNSS_ receives GPS L1 and L2 signals from _MEO_ satellites, which it uses to provide high-precision timing and positioning data. After being transformed to the NMEA format, the received signals are sent to users' applications, supporting various industries, such as vehicle navigation, maritime, and aviation. The _IoT_ collects sensor data for various applications, including environmental monitoring, smart agriculture, and wildfire detection. These data are transmitted to _GW\({}_{1}\)_ via the MIOTY and relayed to _LEO_ satellites using the CCSDS. In actual communication, the _IoT_ interacts with gateways through a user terminal; however, for simplicity in the architecture and modeling structure, this study integrates the _IoT_ into a unified representation.
## 2 Related Works  
OFDM and UFMC are multicarrier modulations which have constituted a subject of research interest since the last decade. OFDM is mainly used in many communication systems such as DVB-T [12], DVB-T2 [2], Advanced Television Systems Committee, third generation (ATSC 3.0) [12], Visible-Light Communication (VLC) [13], 4G [14], and 5G [15]. A carrier frequency offset (CFO) is a radiofrequency impairment induced by an oscillator frequency mismatch between the transmitter and the receiver, also known as Doppler shift. This section presents the literature review.
###### Abstract  
(1) Background: Multi-line transmit (MLT) beamforming has been proposed for fast cardiac ultrasound imaging. While crosstalk between MLT beams could induce artifacts, a Tukey (\(\alpha\) = 0.5)-Tukey (\(\alpha\) = 0.5) transmit-receive (TT-) apodization can largely--but not completely--suppress this crosstalk. Coded excitation has been proposed for crosstalk suppression, but only for synthetic aperture imaging and multi-focal imaging on linear/convex arrays. The aim of this study was to investigate its (added) value to suppress crosstalk among simultaneously transmitted multi-directional focused beams on a phased array; (2) Methods: One set of two orthogonal Golay codes, as well as one set of two orthogonal chirps, were applied on a two, four, and 6 MTL imaging schemes individually. These coded schemes were investigated without and with TT-apodization by both simulation and experiments; and (3) Results: For a 2 MLT scheme, without apodization the crosstalk was removed completely using Golay codes, whereas it was only slightly suppressed by chirps. For coded 4 MLT and 6 MLT schemes, without apodization crosstalk appeared as that of non-apodized 2 MLT and 3 MLT schemes. TT-apodization was required to suppress the remaining crosstalk. Furthermore, the coded MLT schemes showed better SNR and penetration compared to that of the non-coded ones. (4) Conclusions: The added value of orthogonal coded excitation on MLT crosstalk suppression remains limited, although it could maintain a better SNR.  
multi-line transmit; crosstalk artifacts; coded excitation; cardiac imaging +
Footnote †: journal: Applied Sciences
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 3 Leveraging Software Engineering for Sustainable Smart Cities with Electric Vehicles
### Overcoming Software Engineering Challenges in EV Smart Cities  
Managing complex systems is one of the significant challenges faced by software engineers in smart cities for electric vehicles (EVs). Smart cities are made up of various interconnected systems that need to work together seamlessly to achieve sustainable and efficient urban development. These systems include EV charging infrastructure, traffic management systems, public transportation systems, and more. Ensuring that these systems operate efficiently and effectively requires software engineers to have a deep understanding of the city's infrastructure and the technologies used within it.  
One of the primary challenges faced by software engineers in managing complex systems is ensuring interoperability between different systems. Each system may use different standards and technologies, which can result in semantic interoperability problems. To overcome this challenge, software engineers must develop systems that use a common data model and a common API. Additionally, distinctive identifiers should be taken into consideration to ensure that semantic data are accurately mapped and integrated. By doing so, software engineers can ensure that different systems can understand and interpret data in a consistent manner, promoting semantic interoperability.  
In addition to managing complex systems, software engineers must also address security and privacy concerns as cities become more connected and data-driven. As cities become more interconnected and data-driven, there is an increased risk of cyberattacks and data breaches. Software engineers must design systems that are secure and protect citizens' privacy while still enabling the city to collect and use data to improve its operations. This requires a thorough understanding of security and privacy best practices and the ability to implement them effectively in the software systems they develop.  
Managing complex systems and addressing security and privacy concerns are significant challenges faced by software engineers in smart cities for EVs. Software engineers must develop systems that enable semantic interoperability, which requires a common data model and API. They must also design systems that are secure and protect citizens' privacy while still enabling the city to collect and use data to improve its operations. By doing so, software engineers can help to build sustainable and efficient smart cities for EVs that are secure, private, and convenient for their users.
# Low Kinetic Energy Oxygen Ion Irradiation of Vertically Aligned Carbon Nanotubes
## 1 Introduction
Depending on the kinetic energy and the ion mass, the ions can traverse the nanomaterial without interaction with the target, contrary to bulk materials where all ions will interact with the sample [20]. Besides, ion irradiation of graphitic networks is of high interest in nano-engineering due to their ability of structural reorganization after irradiation, like no other material; during the reorganization new bonds around the defects are created restructuring the lattice [21]. Low kinetic energy ion irradiation of CNTs has been described mainly in theoretical works [22; 23; 24] and the limited experimental works reported are mostly in boron and nitrogen ion irradiation [19; 25; 26]. In this work, vertically aligned multi-walled carbon nanotubes (v-CNTs) are functionalized with oxygen groups using low kinetic energy ion irradiation, we evaluate the influence of the ion kinetic energy and irradiation time in the resulting oxygen functionalization. Vertically aligned nanotubes with their unidirectional electron transport facilitate the transducer operation in sensors [27]. Besides, their vertical geometry avoids the use of wet chemistry preventing contamination of the CNTs walls during integration of the active layer in the transducer. The v-CNTs were synthetized by thermal catalytic chemical vapor deposition technique. The structure and alignment of v-CNTs was observed before and after ion irradiation using scanning electron microscopy and transmission electron microscopy. The chemical functionalization of v-CNTs was characterized using X-ray photoelectron spectroscopy and Raman spectroscopy.
# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Representation Learning
#### 5.2.4 Sequence-Based Patient Representation  
The event sequence feature with a time stamp is the sequence feature generated by patient representation based on the sequence. For sequence-based patient representation learning, RNN and its derivatives (GRU, LSTM, etc.) are frequently utilized. Although RNN and its variants can do sequence modeling, especially LSTM, such networks have drawbacks when dealing with extended sequences. Although a two-way network design (such as Bi-LSTM) already exists, it increases training time and the two-way structure is just a two-way link, rather than parallel training.  
As a result, new research has begun to develop the transformer architecture, which includes a self-attention mechanism and embedding of location information. This paradigm is capable of valid bidirectional representation. [PERSON] et al. [66] have used it to learn patient representations and model clinical sequences. They exhibited a Med-BERT design that uses the same transformer framework as the original BERT. Moreover, numerous other researchers have studied it [67; 68; 69]. The transformer's training process is comparable to that of employing a neural network to model EHR data sequences, and it also encodes each clinical event matching to each timestamp as a unit, as well as the entire patient information as a full sequence. This differs from the previous variant in that RNN predicts the next output via recursion. The transformer accepts the unit sequence as a whole and learns basic information from it using a self-attention process.