esa-sceva/satcom-chunk-collection huggingface dataset:
# Automation of a Hybrid Control for Electrohydraulic Servo-Actuators with Residual Dynamics
## 3 Problem Statement and Illustration
### Illustration of the Problem Statement  
Given that the state variables are the same for both subsystems, it is important that these variables have the same values at the switching times to avoid any damage. To illustrate the switching problem that may occur, some simulation results are presented. Let us define the desired trajectory of effort \(F_{d}\) by a sinusoidal signal with some pulsation as follows:  
\[F_{d}(t)=F_{0}+F_{\max}\times\sin(wt),\]  
where \(F_{0}=Mg+ky_{d}\big{(}t_{sp}\big{)}\).  
The desired trajectory of the position \(y_{d}\) is defined by the polynomial function shown in Figure 3a. \(y_{d}\big{(}t_{sp}\big{)}\) is the value of the desired position at the static phase (\(t_{sp}\)).  
In the simulation tests, the switch from (\(S_{1}\)) to (\(S_{2}\)) occurs when the position is established at the constant value, which is defined by the static phase of the desired position trajectory. In Figure 3, each switching from one subsystem to another is indicated by a green dotted line. The first switching takes place at slightly over 2 s. At more than 8 s, the second commutation one occurs.  
Figure 3: Illustration of the switching problem: (**a**) evolution of position (red) and its desired trajectory (blue); (**b**) evolution of force (red) and its desired trajectory (blue); (**c**) evolution of velocity (red) and its desired trajectory (blue); (**d**) evolution of acceleration (red) and its desired trajectory (blue).  
At the end of this static phase, the switch is performed to (\(S_{2}\)). Figure 3b-d shows huge visible peaks on the curves of effort, velocity and acceleration. The peak recorded for effort presents maximum outracing of about 600 N. Such a value can easily damage the system. These peaks are due to the manner of switching, which occurs before the dynamic residual convergence.
## 2 Related Works
Meanwhile, although existing research has attempted to integrate digital twin technology with wind turbine operational monitoring, the current digital twin models are not well integrated with mechanistic models and intelligent prediction models, which constrains the level of operational monitoring and fault prediction for wind turbines. Therefore, this paper takes a typical wind turbine gear transmission system as the research object to systematically study an intelligent fault warning method based on digital twin and multi-source data fusion. It designs the overall architecture of the digital twin for the wind turbine gear transmission system and focuses on the construction methods for the geometric model and mechanism model of the digital twin system. A CNN-LSTM-Attention-based digital twin fault prediction model is proposed by combining a convolutional neural network (CNN), a long short-term memory (LSTM) network, and an attention mechanism based on the Transformer architecture. This model utilizes the CNN to extract high-dimensional local features from complex multi-source operational data and leverages LSTM to capture the fault evolution patterns across time steps, enabling long-term temporal dependency modeling. Finally, through the learning of attention components, the model achieves more rational and adaptive weight allocation, focusing on key feature information to generate higher-precision prediction outputs. This study aims to achieve more accurate fault prediction and real-time, efficient monitoring and maintenance.
## 3 Transmitter Hardware Impairments or 'RF Features' Overview  
A first step in building the equivalent block diagrams for a GNSS transmitter (genuine or spoofer) was to identify the possible sources of hardware (HW) impairments at the transmitter side for wideband-signal transmitters, based on works in [35; 36; 37; 38; 39; 40; 41; 42; 43] and analytical thinking. Five sources of HW impairments were identified in GNSS transmitters, as follows:  
1. **Phase noise (PN)**: PN is unavoidable in any wireless transmitter, as it is introduced by the transmitter clock instabilities; atomic clocks on-board genuine GNSS transmitters are intuitively expected to have lower phase noise than the clock of spoofers and other malicious transmitters [36; 37; 38]. PN models are discussed in Section 3.1;
2. **Power amplifier (PA) non-linearities**: non-linearities close to the saturation region for PAs (and especially for high-power amplification needs as it is the case of GNSS transmitters) can represent an important HW feature to distinguish between different  
Figure 3: The proposed methodology for an RFF algorithm applied to GNSS pre-correlation sampled data.  
transmitters. In addition to non-linearities, possible memory effects of the PA can also create differentiating features at the transmitter. PA models are discussed in Section 3.2;
3. **I/Q imbalance**: the I/Q imbalance in a transmitter is introduced in the translation of the baseband signals to passband signals due to the facts that the phase shift is not perfectly at \(90^{\circ}\) in the analogue domain and that the analogue gain is not perfectly matched for I and Q components. I/Q imbalance models are discussed in Section 3.3;
4. **Digital-to-analog converter (DAC) non-linearity**: signal distortions are also possibly produced by the non-linear DAC operation at each transmitter. DAC models are discussed in Section 3.4;
5. **Band-pass filter (BPF)** passband and out-of-band ripples: the transmitter BPF filter also puts its 'fingerprint' on the transmitted signal and can act as a smoother of the other HW features. BPF models are discussed in Section 3.5.  
Each of these identified HW impairments is further detailed in the subsequent subsections.
# Matching of Energy Provisions in Multihop Wireless Infra-Structures
## 4 Matching of Energy Provision: Basic Concepts
The matching degree at \(BS(i)\) is defined as:  
\[MDegree(i)=1-\left|E_{0}(i)/E_{0-BS}-E_{CR}(i)/E_{CR-BS}\right| \tag{2}\]  
The maximum value of matching is \(MDefree=1\), The maximum value is attained when the ratio of energy provision at a BS to the average energy provision is the same as the ratio of the average energy consumption rate at the BS to the average energy consumption rate. **Proposition 2**. : _The optimal energy provision achieving the maximum network lifetime \(T_{network}\) corresponds to \(MDefree=1\) for all BS._  
Proof. : By definition \(T_{BS}(i)E_{CR}(i)=E_{0}(i)\). If \(MDefree(i)=1\) then \(T_{BS}(i)=E_{0}(i)/E_{CR}(i)=E_{0-BS}/E_{CR-BS}\). As a result, all BSs that have \(MDefree=1\) will have the same lifetime. If all BS have \(MDefree=1\) then all BS have the same lifetime \(T_{BS}(i)=T_{BS}(j)=...T(N)\). Consequently, MDefree = 1 for all BS achieves maximum network lifetime. Next, we introduce the relative energy consumption ratio and the energy differentiation factor. The relative energy consumption ratio is written as:  
\[\mu(i)=\frac{E_{CR}(i)}{\sum_{i=1}^{N}E_{CR}(i)}E_{0-all},1\leq i\leq N \tag{3}\]  
where \(E_{0-all}\) is the total energy provision, defined as the product of \(E_{0-BS}\) and \(N\), \(E_{0-all}=E_{0-BS}N\). The energy differentiation factor is written as:  
\[Diff(i)=\frac{E_{CR}(i)}{\sum_{i=1}^{N}E_{CR}(i)/N} \tag{4}\]  
Note that we can rewrite the relative energy consumption ratio in terms of the energy differentiation factor as follows, \(\mu(i)=Diff(i)E_{0-BS}\)  
**Proposition 3**. : _The energy provision solution of \((E_{0}(1),E_{0}(2),...,E_{0}(N))=(\mu(1),\mu(2),...,\mu(N))\) where each \(\mu(i)\) is defined as in Equation (3), results in the perfect energy matching of all BSs._  
Proof. : For each BS(_i_), let \(E_{0}(i)=\mu(i)=\frac{E_{CR}(i)}{\sum_{i=1}^{N}E_{CR}(i)}E_{0-all}\). We can obtain that \(E_{0}(i)/(E_{0-all}/N)=\frac{E_{CR}(i)P}{(\sum_{i=1}^{N}E_{CR}(i)P)/N}\) that leads to perfect energy matching of \(MDegree(i)=1\) for each BS(_i_), \(1\leq i\leq N\)The Proposition 3 expresses the basic principle of optimal energy provision in a WMI with a given total energy provision and a set of energy differentiation factors \((Diff(1),Diff(2),...,Diff(N)\) at the BSs in the WMI.
## 4 Clinical Applications of AI to CHF Management
### Monitoring Devices and AI-Powered Platforms
\begin{table}
\begin{tabular}{c c c} \hline  & **AI Tools** & **Function** \\ \hline \multirow{4}{*}{Telemedicine and Mobile Health} & \multirow{4}{*}{Veta Health} & Allow remote monitoring and management of heart failure patients \\ \cline{2-3}  & & Uses AI and machine learning algorithms to remotely manage heart failure patients monitoring their blood pressure levels and achieving weight loss \\ \hline \multirow{4}{*}{Monitoring devices} & \multirow{4}{*}{KardiaMobile} & Continuously monitor heart rhythm and are already used for detecting atrial fibrillation \\ \cline{2-3}  & & iRhythm Zio XT \\ \hline \multirow{4}{*}{AI-powered platforms} & \multirow{4}{*}{LINK-HF} & CardioMEMS & Measure pulmonary artery pressure \\ \cline{2-3}  & & ReDS & Detect the extent of pulmonary congestion \\ \cline{1-1} \cline{2-3}  & & \multirow{2}{*}{ReCD ECG, 3-axis accelerometry, skin impedance, body temperature, and posture} \\ \cline{1-1} \cline{2-3}  & & \multirow{2}{*}{Medicomp-Quippi} & Help healthcare providers personalise treatment plans \\ \cline{1-1} \cline{2-3}  & & & and drug dosing \\ \hline \multirow{4}{*}{Natural Language Processing} & \multirow{4}{*}{Linguamatics’ NLP software} & Extract data from electronic health records to provide insights on heart failure patients \\ \cline{1-1} \cline{2-3}  & & EHR analytics platform & Analyse electronic health records to support the diagnosis and management of heart failure \\ \hline \end{tabular}
\end{table} 
Table 1: Selected examples of AI-powered tools and their potential function.
## 2 Analysis of Two-Step Method
### Convergence Conditions
For obtaining these conditions, we perform conformal mapping of the interior of the unit circle \(\{\zeta:|\zeta|<1\}\) to the set \(Q=\{q:\text{Re}(q)<0\}\) with use of the following function: \[\zeta=\frac{q+1}{q-1}.\] (9) After substitution of (9) into (8), the following equation is obtained: \[h\lambda q^{2}+2(1-\beta_{1}+\beta_{2}\lambda h)q+2(1+\beta_{1}-\beta_{2} \lambda h)-h\lambda=0.\] (10) The conditions on coefficients of (10) guarantee roots \(q_{i}\in Q\) are provided by the Routh-Hurwitz criterion [30; 31]. The Hurwitz matrix for (10) is written as \[ \begin{pmatrix}2(1-\beta_{1}+\beta_{2}\lambda h)&h\lambda\\ 0&2(1+\beta_{1}-\beta_{2}\lambda h)-h\lambda\end{pmatrix} .\] The conditions of the Routh-Hurwitz criterion lead to two inequalities: \[1-\beta_{1}+\beta_{2}\lambda h>0,\] (11) \[2(1+\beta_{1}-\beta_{2}\lambda h)-h\lambda>0.\] (12) Inequity (11) is valid \(\forall\lambda\in[l,L]\), \(\forall h>0\) according to the ranges of values of \(\beta_{i}\) stated in the conditions of the theorem. Inequity (12) is rewritten as \[h<\frac{2(1+\beta_{1})}{\lambda(1+2\beta_{2})},\] and it is valid for values of \(h\) chosen from Inequity (7). This, condition (7) guarantees that \(q_{i}\in Q\), and as a consequence that \(|\zeta_{i}|<1\) under the stated conditions. This leads to the convergence of (6) for any \(x^{0}\in\mathbb{R}^{d}\).
## 6 Discussion and Conclusions
These BER levels are acceptable and allow to provide DVB-S2 services. gGiven that the study assumed the worst case when the satellite bent pipe communication link used minimum powers for both uplink and downlink directions, and the earth stations for each transponder were located on the border of the satellite footprints, the obtained results show compatibility since in practice the system won't operate constantly at minimum powers for such a scenario and BER levels would be even higher. Additionally, the authors would like to underline that for the 6425-7125 MHz band 5G most likely will have a lower deployment rate compared to the predictions used in the study. These predictions were based on the predictions presented in ITU-R, however previous experience of a similar study in the 26 GHz band showed that in practice deployment rate was much lower compared to the assumptions made in ITU-R. It should be also noted, that the study assumed all countries within the satellite footprint implement 5G in the 6 GHz band, however, while the majority of these countries have plans for 5G in the 6 GHz, in practice not all of them may have plans to utilize this band for 5G, and thus the deployment density may be also lower. Since the study showed compatibility for the highest estimated deployment rate, for the lower deployment rate which will most likely be in practice, the compatibility situation will be even better. Consequently, 5G base stations may be deployed at any region within the services areas of fixed satellite service without any additional restrictions. Conceptualization, A.P. and A.K. ; methodology, A.K. and A.P. ; software, A.P. and A.K. ; validation, A.P. and A.K. ; formal analysis, A.P. and A.K. ; investigation, A.P. ; resources, V.T. and E.D. ; data curation, A.P. and A.K. ; writing--original draft preparation, A.P. ; writing--review and editing, A.P., A.K., V.T. and E.D. ; visualization, A.P. ; supervision, A.P. ; project administration, V.T. and E.D. ; funding acquisition, V.T. and E.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors would like to thank Radio Research and Development Institute for providing valuable technical resources to conduct this study, as well as Gazprom Space Systems and Roscosmos for extensive consultation and important suggestions regarding assumptions used in this study. The authors declare no conflict of interest.
# SARS-CoV-2 Detection Using Optical Fiber Based Sensor Method
## 11 Conclusions  
In this article, a novel plastic optical fiber sensor U-shaped probe sensing method is envisaged for detection of SARS-CoV-2, commonly known as COVID-19 virus, that also has the capability to detect new variants such as Omicron. This specific fiber-optic probe for SARS-CoV-2 can provide quick diagnosis via insertion into the oropharyngeal and nasopharyngeal tract thanks to the synergistic integration of improved photonics and the addition of appropriate biomarkers. The sensing approach based on intensity modulation appears to be very cost effective with minimum logistics. This one-of-a-kind adaption is extremely simple to operate and does not necessitate the usage of trained personnel. The envisaged prototype version can be simply connected to a national health services system for reporting, self-analysis and limiting the COVID breakout, eliminating the inconveniences that patients have while visiting established/registered clinics/labs/hospitals. The prototype may be adjusted to perform quantitative assessments via controlled standardization versus intensity magnitudes against spiking SARS-CoV-2 infection, in addition to qualitative assessments. The results presented in terms of relative intensity and intensity suggest that plastic over fiber of 200 microns in diameter has better detection accuracy compared to a higher diameter. It was also shown that the detection of other variants of COVID-19 will be applicable.  
Figure 12: The proposed prototype will have a following shape. The U-shaped optical fiber probe connected to the laser and photodiode while green light shows a COVID positive, and COVID negative is reported by red light. (**a**) Shows front view while (**b**) shows lateral view of the prototype.  
**Author Contributions:** M.U.H., wrote the original draft of the manuscript; M.U.H., methodology and administration--review; M.U.H. and M.K., review and editing; M.U.H. and M.K., supervision and validation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** This was a study on secondary analysis and as such not subject to an ethical review.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the cited studies used in our analysis and no information of the subjects was made public. No additional samples for the purpose of this study were collected. Patients were informed of the research and their nonobjection approval was confirmed.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank Islamabad Medical and Dental College (IMDC) and Akbar Niazi Teaching Hospital (ANTH), Bharakahu, Islamabad, Pakistan for their lab setup facilities, data provision and assistance from their radiology, medicine and emergency department.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests that could have appeared to influence the work reported in this paper.
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Protocol Specifications
\(G\) first verifies \(ID_{C}\) is from Message 2 and then verifies \(sid\) derived by Equation (6). \(G\) computes \(ck\) with Equation (7) and uses it to verify the \(tag\). At last, \(G\) constructs Message 9 and sends it to \(C\) as an acknowledgement. Locally, \(G\) computes key \(sk\) with Equation (8) as the final outcome of the protocol. \[sk=prf(k,2||sid)\] (8)
10. \(C\): validate Message 9 and compute \(sk\) \(C\) parses Message 9 as \(sid\), \(ID_{G}\) and MAC \(tag\). \(C\) verifies \(ID_{G}\) is from Message 1, validates \(sid\) and then use \(ck\) to verify \(tag\). \(C\) also computes the shared secret key \(sk\) with Equation (8). Message 8 and Message 9 are created by \(C\) and \(G\), respectively and validated by \(G\) and \(C\), respectively, to complete the 3 PKD protocol to confirm the matching session information, and consistency in the provided key material from \(P\). Therefore, all three parties reach an agreement that the delegation has been properly granted and confirmed by \(C\), \(P\) and \(G\), and derive \(sk\) as the final outcome of the protocol. Figure 9: Message flow of key provisioning protocol. At this point, the protocol concludes with the successful establishment of a secret key between \(G\) and \(C\) to represent their trust relationship delegated by \(P\). Stated another way, at completion of the protocol, all three parties confirm they reached a three-party agreement on the delegation. This is typically referred to as a matching conversation. For the agreement on the delegation, the result of the protocol is that:  
1. \(C\) agrees: \(P\) and \(G\) are corresponding parties in the delegation as \(P\) and \(G\); and \(P\) and \(G\) know that \(C\) agrees;
2. \(P\) agrees: \(G\) can be authorized to receive the delegation on \(C\); and both \(C\) and \(G\) know that \(P\) agrees;
3. \(G\) agrees: \(P\) has successfully authorized the delegation to \(C\), and \(C\) is indeed \(P\)'s child; \(C\) and \(P\) know that \(G\) agrees.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 2 Materials and Methods
### TCP Sockets
#### 2.3.2 Data Reception by TCP  
Received packets must be transferred from the network layer to the transport layer for the TCP header and data processing by the following functions.  
1. \(\mathrm{tcp\_v4\_rcv}\): which verifies the packet integrity, it checks if the packet is properly destined, process the transport layer checksum and removes the IP header.
2. \(\mathrm{tcp\_v4\_do\_rcv}\): which verifies that the received packet has a complete header and checks the current TCP connection state.
3. \(\mathrm{tcp\_rcv\_established}\): If the current TCP connection state is \"ESTABLISHED\", it processes the received packets and copies the data to the user space. If the packets are error-free, a fast process denominated \"fast path\" is executed, otherwise a \"slow path\" process is executed.
# Research on Seismic Signal Denoising Model Based on DnCNN Network
## 2 Design of Seismic Signal Denoising Model Based on DnCNN  
Seismic signal denoising is done to remove various kinds of noise from the noisy seismic signal and restore the original seismic signal, as shown in Formula (1):  
\[x=y+v \tag{1}\]  
In Formula (1), \(x\) is the seismic signal with noise, \(y\) is the original seismic signal without noise, and \(v\) is various kinds of noise contained in the seismic signal. The ultimate goal of seismic signal denoising is to accurately recover clean data \(y\) from noisy data \(x\) by using the DnCNN to obtain an estimate of the original seismic signal \(y_{0}\) and make \(y_{0}\approx y\).
### Problems and Challenges  
#### Obtaining Vital Signs from the Composite Sensor Kit  
The most important aspect of any biomedical device is data acquisition and the techniques involved in signal filtering, smoothing, and signal processing whilst making sure that no information has been lost in acquiring the samples. In the Methods section, the hardware and the software used to obtain vital sign readings from the human subjects was discussed. The parameters such as sampling frequency and the filtering (pass bands) parameters, had to be set in order to ensure that the required amplitude peaks were captured, whilst making sure that the outliers were detected and eliminated. Another important aspect in biomedical instrumentation is the removal of motion artefacts and noise due to muscular movements, and the effect of environmental conditions on the sensors.  
The BP and the respiratory rate (RR) can be calculated from the readings from the ECG and SpO2 sensors using the pulse transit time (PTT), the pulse arrival time (PAT), and the pulse delay time (PDT). [35] These are estimated blood pressure values, which can be used in the absence of proper digital blood pressure kits. The external BP sensor kits can provide accurate blood pressure readings, though it may not be possible to integrate and synchronize these readings with the ECG and SpO2 sensor readings. Since the readings from the individual sensors have to be sampled in a single sweep and timestamped, it is important that all the sensors be synchronized. The RR and BP calculated using the PTT can be accurate, and they may serve as a good starting point. The RR and BP can then be used as features that are extracted from the waveforms, and they can be used for further statistical analysis and for trauma value calculations [5; 36].