esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to the significant multipath and Doppler effects in the underwater acoustic (UWA) channel, the quality of the received signal is degraded, which seriously affects the performance of UWA communication. The paper proposes a time reversal UWA communication method combined with a symbol-based Doppler compensation (SBDC) technique to solve those problems. A single element time reversal mirror (TRM) is used to realize channel equalization and mitigate the inter-symbol interference (ISI) resulting from multipath propagation. The SBDC technique is subsequently used to compensate Doppler effects in the received signal, thereby reducing the bit error rate (BER) and improving the communication performance. In order to verify the performance of the proposed communication method, some simulations with real sounding channels were performed. Moreover, a field UWA communication experiment was conducted in the Songhua River (Harbin, China). The UWA communication experiment achieves nearly error-free performance with a communication rate of 100 bit/s in the bandwidth of 2 kHz. The results of the experiment demonstrate the feasibility and robustness of the proposed UWA communication method.  
U 88/18/3279; 12.3390/s18103279
## 2 Network Virtualization  
In traditional networks, the users have requested services through a very complex and static infrastructure. All the components, such as nodes, links, and resources, were strongly dependent on the hardware layer. Moreover, the traditional architectures are mainly characterized by their high cost, non-flexibility, and non-scalability. In fact, introducing new physical equipment and/or functionality requires more time, more cost, and more resources since it is needed to respect the different protocols, standards, and technologies already installed in this complex architecture. To this end, network virtualization [4] has gained more and more attention because it makes the network not only independent from the hardware but also enables services with low cost and with more flexible resources management, setting, and control. For instance, this technology solves many networking challenges in today's data centers, helping organizations centrally program and provision the network.
## 4 Simulation Results
### Effectiveness Validation of the Proposed STPAP Beamforming Algorithm
#### 4.1.2 Signal Acquisition in the BD2 Software Receiver  
As mentioned above, the input signal received by the DPSA is firstly down converted into baseband, whose time domain samples and single-sided amplitude spectrum are shown in Figure 6. As the interferences are generated by the DSSS technique in the simulation, they have the same single-sided amplitude spectrum forms with the BD2 signal.  
Using the proposed adaptive algorithm in Section 3.2 for the proposed STPAP beamforming algorithm and meanwhile setting the step-size \(\upmu\) as \(5\times 10^{-7}\), the iterative error signals are shown in Figure 7, from which it can be seen that the error values tend to stabilize as the number of iterations increase. After that, the time domain samples and single-sided amplitude spectrum corresponding to the output baseband signal can be depicted in Figure 8.  
Furthermore, the output baseband signal is sent into the BD2 software receiver. Assume that \(\varsigma\) denotes Doppler frequency and \(\tau\) represents chips. The acquisition results are depicted in Figure 9, in which the two desired BD2 navigation satellites have been acquired successfully.  
Figure 5: Array patterns when parameters are respectively fixed as those of the two desired BD2 signals using the proposed STPAP beamforming algorithm. (**a**) Beam forms in the space domain and time domain towards the two BD2 signals. (**b**) Beam forms in the polarization domain towards the BD2 signal of PRN 1. (**c**) Beam forms in the polarization domain towards the BD2 signal of PRN 2.  
Figure 6: Time domain samples and single-sided amplitude spectrum of the input baseband signal. (**a**) Time domain samples. (**b**) Single-sided amplitude spectrum.  
According to the simulation results in Section 4.1, we can find that the proposed STPAP beamforming algorithm is effective in canceling interferences and forming beams towards the desired  
Figure 8: Time domain samples and single-sided amplitude spectrum of the output baseband signal using the proposed STPAP beamforming algorithm. (**a**) Time domain samples. (**b**) Single-sided amplitude spectrum.  
Figure 7: Iterative error signals changing with the iterative times.  
Figure 9: Acquisition results corresponding to the desired BD2 satellites using the proposed STPAP beamforming algorithm: (**a**) PRN 1; (**b**) PRN 2.  
BD2 satellites in the joint space-time-polarization domain. Moreover, the received signal processed by the proposed algorithm can be acquired by the BD2 software receiver.
# Bio-Corrosion Behavior of Ceramic Coatings Containing Hydroxyapatite on Mg-Zn-Ca Magnesium Alloy
###### Abstract  
Ceramic coatings containing hydroxyapatite (HA) were fabricated on a biodegradable Mg\({}_{66}\)Zn\({}_{29}\)Ca\({}_{5}\) magnesium alloy through micro-arc oxidation by adding HA particles into the electrolytes. The phase composition and surface morphology of the coatings were characterized by X-ray diffraction and scanning electron microscopy analyses, respectively. Electrochemical experiments and immersion tests were performed in [PERSON]'s solution at 37 \({}^{\circ}\)C to measure the corrosion resistance of the coatings. Blood compatibility was evaluated by in vitro blood platelet adhesion tests and static water contact angle measurement. The results show that the typical ceramic coatings with a porous structure were prepared on the magnesium alloy surface with the main phases of MgO and MgSiO\({}_{3}\) and a small amount of Mg\({}_{3}\)(PO\({}_{4}\))\({}_{2}\) and HA. The optimal surface morphology appeared at HA concentration of 0.4 g/L. The electrochemical experiments and immersion tests reveal a significant improvement in the corrosion resistance of the ceramic coatings containing HA compared with the coatings without HA or bare Mg\({}_{66}\)Zn\({}_{29}\)Ca\({}_{5}\) magnesium alloy. The static water contact angle of the HA-containing ceramic coatings is 18.7\({}^{\circ}\), which is lower than that of the coatings without HA (40.1\({}^{\circ}\)). The in vitro blood platelet adhesion tests indicate that the HA-containing ceramic coatings possess improved blood compatibility compared with the coatings without HA. Utilizing HA-containing ceramic coatings may be an effective way to improve the surface biocompatibility and corrosion resistance of magnesium alloys.  
coating; magnesium alloy; hydroxyapatite; micro-arc oxidation; corrosion resistance; blood compatibility +
Footnote †: journal: Applied Physics Letters
## 4 Conclusions  
The effect of three CDA modes on the improvement of the BSFC and emission levels was analyzed on a medium-speed high-power diesel engine D-49. A numerical study was performed by associating the 1D simulations in the GT-SUITE and 3D CFD simulations in the AVL ESE DIESEL. A predicted friction model was established to estimate friction losses. The impact of CDA operations on the work parameters of the diesel engine was investigated with 1D simulations. The emission formation in the combustion chamber under different operations has been evaluated with 3D CFD simulations. The main conclusions are summarized as follows:  
1. The BSFC reduction is obtained in the engine operating range from 1000 to 3000 N-m at 450 rpm and 556 rpm. CDA with intake and exhaust valves closed reduces the BSFC by 11 and 14% at engine speeds of 450 rpm and 556 rpm at a load of 1000 N-m.
2. The BSFC reductions are attributed to the increase in the indicated efficiency in the active cylinder and the reduction in the motoring losses, the pumping losses, and friction losses of the valvetrain in the deactivated cylinder. Fixed cylinder deactivation will increase the hydraulic friction loss in the deactivated cylinders.
3. At low speeds and light engine loads, CDA with all valves closed without EGR reduces CO and soot emissions attributed to the improved fuel-air mixing formation and increased combustion temperature in the active cylinder, which in turn results in an increase in NOx emission.
4. At 450 rpm and 1000 N-m, CDA combined with EGR control dramatically reduces the BSMOx emission to 4.2 g/(kW-h) at the cost of only a 0.8% increase in the BSFC, while the BSCO emission is maintained in low levels.
5. The investigation confirms the benefit of combining CDA with all valves closed and EGR as an effective method to reduce the BSFC and engine emission levels simultaneously for a medium-speed high-power engine, without the significant modification of engine construction.  
Steady states were considered in the study. In the next research stage, it is meaningful to study the engine transient response process and vibration characteristics during cylinder deactivating and reactivating for the medium-speed high-power engine with high inertia components by using the simulation models developed and validated in the present study.  
Conceptualization, A.K.; methodology, A.K. and Y.L.; software, Y.L. and B.S.; validation, A.K., Y.L. and B.S.; formal analysis, Y.L. and A.K.; investigation, Y.L., A.K. and B.S.; writing--original draft preparation, Y.L. and A.K.; writing--review and editing, B.S.; visualization, Y.L.; supervision, A.K.; project administration, A.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experimental Setup
### Software Implementation
#### 3.2.4 Real-Time Validation and Feedback Loop  
Predicted power consumption is continuously validated against actual consumption data recorded by the AMI monitoring devices installed in the building. At the prediction time, the discrepancy between predicted and actual consumption is analyzed, providing feedback for ongoing model calibration. This iterative process helps improve the accuracy and reliability of the MC-LSTM model by allowing it to adjust to real-time fluctuations in energy consumption, including unexpected changes due to weather conditions or operational shifts.
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 2 Related Works
Thus, focusing on diverse QoS requirements, we propose QCORS to construct the uncongested routing topology dynamically and adopt the Lyapunov theory to solve the optimization problems.
## 2 Instrument Design
### Block Diagram
#### 2.1.4 Temperature-Power Control  
The ELBARA II instrument is controlled _via_ two embedded computers. The temperature and power controller (TPC) is responsible for generating and monitoring the power used by the radiometer,  
Figure 3: Layout of the calibration assembly (CA).  
controlling the Peltier heating and cooling element attached to the CA, and starting the main instrument computer (IC). The IC is responsible for scheduling system operations and acquiring data, communicating with the outside world, and controlling the radiometer-unit. The TPC is connected to the main IC _via_ a serial line connection and acts after booting up the system as a slave to the IC. The IC polls the TPC for measurement values of the different temperature sensors attached to the TPC.  
The total power dissipation by the radiometer electronics is about 45 W. The TPC uses a proportional-integral-derivative controller (PID) algorithm to adjust the current applied to a Peltier thermo-electric cooler (TEC) to ensure that the system temperature \(T_{0}\) is maintained at a constant value. Readings from the temperature sensors (precision of 0.003 K) positioned to monitor the actual CA temperature, the air temperatures inside and outside the electronics enclosure, and the heat sink temperature are used as inputs to the PID algorithm.  
The TEC dissipates heat from the interior of the radiometer enclosure to the external environment _via_ a heat sink attached to a fan controlled by the TPC. The TEC can also be used as a heat pump to heat the radiometer enclosure if necessary. If the heat sink is at the same temperature as the radiometer interior, the minimum cooling capacity of the TEC is 72 W. If the TEC is operated as a heat pump, it can generate up to 96 W. With this design, \(T_{0}\) can be maintained for the duration of the measurement cycle (\(\approx\)30 seconds) within \(\pm\)0.05 K of a fixed value, typically in the range of 10 K to 20 K above the external temperature.
# A Machine-Learning-Based Cyberattack Detector for a Cloud-Based SDN Controller
## 3 Cyberthreat Analysis and Protection Use Case
### Cryptomining Attack Detection  
One of the most prevalent contemporary networking threats is the misuse of computing resources for cryptomining attacks. As described in [7], cryptomining entails the validation of transactions on a decentralized cryptocurrency blockchain. A cryptomining attack involves the creation of a botnet, which consists of compromised devices that act as miners to validate transactions and earn digital currency rewards, such as Ethereum (ETH) and Monero (XMR), for the attacker.  
The attacker may exploit devices already infected with malware or infect new devices to enlist their resources to create the botnet for cryptomining. The attacker may use various methods, such as spreading malicious links on social networks, phishing attacks, and disseminating malicious applications, to infect devices.  
Additionally, the attacker must choose the cryptocurrency to mine and the cryptomining pool to join to validate transactions. A mining pool is a service that enables miners to pool their resources to validate transaction blocks and receive rewards. After obtaining all the necessary components, the attacker can establish the botnet to mine cryptocurrencies for their gain.  
According to [32], the network is the most effective place to detect cryptomining traffic promptly and accurately. However, detecting such activity on the network can be challenging due to encryption methods that protect the payload and obscure its contents. For instance, attackers can leverage the Secure Sockets Layer/Transport Layer Security (SSL/TLS) encryption protocol to conceal the cryptomining protocol within the encrypted communication's payload. This renders traditional techniques, such as Deep Packet Inspection (DPI) or Cryptomining Pool Domain Name Identification (in the case of using Server Name Indication (SNI) or web proxies) ineffective in detecting cryptomining activity in current networks. Consequently, more advanced techniques are required to equip cybersecurity professionals to deal with these real-world situations.  
To address these challenges, ML techniques can be employed to train models that accurately identify the presence of cryptomining traffic in real time, even when encrypted, by leveraging network and transport-level data-flow characteristics [32]. Accordingly, in this study, an ML model was trained using a substantial set of network features derived from network flow statistics to detect cryptomining activity for both encrypted and unencrypted links with a high degree of accuracy. This ML model forms part of the Cybersecurity TeraFlowSDN NetApp, which enables the detection of cryptomining traffic in the network's data plane in real time, thereby facilitating appropriate remediation measures to safeguard the network.
## 2 Materials and Methods
### Frequency Feature Assessment  
Frequency domain analysis was employed to extract the features used in this paper [15; 16; 17] as inputs. Each individual beat obtained from signals was expressed as a finite series in accordance with previous research works [18; 19], expressed by  
\[x(t)=\frac{A_{0}}{2}+\{\sum_{n-1}^{k/2}A_{n}cosn\omega t_{s}+\sum_{n-1}^{k/2}B _{n}cosn\omega t_{s}\}, \tag{1}\]  
where \(\omega\) represents the angular frequency and \(t_{s}\) represents the sampling time interval. Fourier coefficients were determined for each pulse as  
\[A_{n}=\frac{2}{k}\sum_{s-0}^{k}x_{s}cosn\omega t_{s} \left(\text{for }n=0,1,\ldots,\frac{k}{2}\right), \tag{2}\] \[B_{n}=\frac{2}{k}\sum_{s-0}^{k}x_{s}sinn\omega t_{s} \left(\text{for }n=0,1,\ldots,\frac{k}{2}\right). \tag{3}\]  
This facilitated the calculation of the Phase angle (\(P_{n}\)) and the Amplitude of n harmonics (\(Ah_{n}\)) present in each beat spectrum, defined as \(Ah_{n}=\sqrt{A_{n}^{2}+B_{n}^{2}}\) and \(P_{n}=arctan(Bn/An)\). To determine the Amplitude proportions (\(C_{n}\)), the formula \(Ah_{n}/Ah_{0}\times 100\%\) was used, considering a range of n values from 1 to 4, since that is  
Figure 2: Distribution of subjects in each age group for the Local Clinical In Vivo Dataset.  
the amount of harmonics with representative harmonic amplitude. Additionally, the Coefficient of variation (\(CV_{n}\)) of \(C_{n}\) was determined.  
Harmonic distortion (HD) serves as a measure of the BAPW characteristics through Discrete Fourier transform (DFT). By computing the squared magnitudes of the Fourier coefficients (\(|A_{k}|^{2}\)) and their complex conjugates, HD quantifies the energy ratio above the fundamental frequency and the energy at the fundamental frequency within the waveform.  
\[HD=\frac{\sum_{k=2}^{6}|A_{k}|^{2}}{|A_{1}|^{2}}. \tag{4}\]  
Fourier coefficients beyond the fourth order were disregarded, as they had little influence on the resulting HD value. Finally, [PERSON]'s power spectral density [20] was computed for each individual beat, enabling the extraction of related features, such as Mean frequency of the power spectrum (MeanF), Median frequency of the power spectrum (MedF), Average band power (AP), Occupied bandwidth at 99% (OB), Half-power bandwidth at 3 dB (HB). The Mean and Standard deviation (SD) of each feature was determined. Any values that deviated by more than two SD from the mean for each beat analyzed were eliminated from the signal dataset.
## 1 Introduction  
Time series often exhibit strong trends and seasonal variations presenting challenges in developing effective forecasting models. How to effectively model time series in order to improve the quality of forecasts is still an outstanding question. State space and Autoregressive Integrated Moving Average (ARIMA) models are the two most widely-used approaches to time series forecasting, and provide complementary methodologies to the problem. While exponential smoothing methods are based on a description of trends and seasonality in the data [1; 2; 3], ARIMA models aim to describe the autocorrelations in the data [4; 5; 6; 7]. The ARIMA forecasting framework, originally developed by [PERSON] et al. [8], involves an iterative three-stage process of model selection, parameter estimation and model checking. A statistical framework for exponential smoothing methods was recently developed based on state space models called ETS (Error, Trend and Seasonality) models [9]. Identifying the proper autocorrelation structure of a time series is not an easy task in ARIMA modeling [10]. Identifying an appropriate state space model for a time series can also be difficult. However, the usual forecast accuracy measures can be used for identifying a model provided the errors are computed from data in a test set that were not used for model estimation. In this work, a cross-validation procedure is used to identify an appropriate state space model and an appropriate ARIMA model for a time series. The data are split into a training set and a test set. The training set is used for estimating the model and the test set is used to measure how well the model is likely to forecast on new data. This approach is exemplified in the paper with a case study of retail sales time series of different categories of women's footwear from a Portuguese retailer that, by exhibiting complex patterns, present challenges in developing effective forecasting models. Sales forecasting is one of the most important issues that is beyond all strategic and planning decisions in any retail business. The importance of accurate sales forecasts to efficient inventory management at both disaggregate and aggregate levels has long been recognized [11]. Aggregate time series are usually preferred because they contain both trends and seasonal patterns, providing a good testing ground for developing forecasting methods, and because companies can benefit from more accurate forecasts. The remainder of the paper is organized as follows. The next section presents a brief description of the state space models and the ARIMA models and also introduces the usual forecast error measures. Section 3 describes in detail the steps of the model identification procedure and Section 4 presents the results of its application to a case study of retail sales of different categories of women's footwear. Finally, Section 5 offers the concluding remarks.
# Venus Magnetotail Long-Term Sensing Using Solar Sails
###### Abstract  
Propellantless propulsion systems, such as the well-known photonic solar sails that provide thrust by exploiting the solar radiation pressure, theoretically allow for extremely complex space missions that require a high value of velocity variation to be carried out. Such challenging space missions typically need the application of continuous thrust for a very long period of time, compared to the classic operational life of a space vehicle equipped with a more conventional propulsion system as, for example, an electric thruster. In this context, an interesting application of this propellantless thruster consists of using the solar sail-induced acceleration to artificially precess the apse line of a planetocentric elliptic orbit. This specific mission application was thoroughly investigated about twenty years ago in the context of the GeoSail Technology Reference Study, which analyzed the potential use of a spacecraft equipped with a small solar sail to perform an in situ study of the Earth's upper magnetosphere. Taking inspiration from the GeoSail concept, this study analyzes the performance of a solar sail-based spacecraft in (artificially) precessing the apse line of a high elliptic orbit around Venus with the aim of exploring the planet's induced magnetotail. In particular, during flight, the solar sail orientation is assumed to be Sun-facing, and the required thruster's performance is evaluated as a function of the elliptic orbit's characteristics by using both a simplified mathematical model of the spacecraft's planetocentric dynamics and an approximate analytical approach. Numerical results show that a medium-low-performance sail is able to artificially precess the apse line of a Venus-centered orbit in order to ensure the long-term sensing of the planet's induced magnetotail.  
solar sail; Venus's induced magnetosphere; magnetotail observation; preliminary trajectory design; apse line artificial precession; GeoSail mission; non-Keplerian orbits +
Footnote †: journal: Applied Sciences