esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Analysis and Simulation of the Frequency Synthesis Chain
### Simulation Results  
#### 3.2.1 Transient Analysis  
The PLL ensures the phase alignment of the output clock with the reference source. Although the phase of the reference source can experience instantaneous distortion, the PLL requires a certain amount of time to adjust its phase. Thus, the phase-settling time plays a crucial role in evaluating the frequency synthesizer [25]. Simulations of \(H(s)\) using Equation (15) were conducted based on the theory presented in Section 2. The parameter values used are as follows: \(M_{1}\) = 20, \(M_{2}\) = 200/7.36823, \(M_{3}\) = 46, \(k_{d1}\) = 1 V/rad, \(k_{d2}\) = 1 V/rad, \(k_{o1}\) = 90 Hz/V, and \(k_{o1}\) = 2.2 MHz/V. Since the frequency synthesizer consists of two PLLs, the parameters \(k_{p}\) and \(\tau\) can be adjusted for both PLLs.  
Assuming that the parameters \(k_{p2}\) and \(\tau_{2}\) are fixed simplifies the calculation of the modulation signal gain applied in the frequency synthesis chain. The given parameter values were substituted into Equation (15), and the step responses of the system are  
Figure 4: Functional block diagram of the active loop filter PLL.  
depicted in Figure 5. Figure 5a illustrates the settling times of the phase step response for various values of \(k_{p1}\) (ranging from 2 to 4 in step of 2/3), while \(\tau_{1}\) remains fixed at \(2\times 10^{-4}\) s. The settling times obtained are 1.52 ms, 1.07 ms, 822 us, and 674 us, respectively. For comparison, Figure 5b demonstrates the effect of varying \(\tau_{1}\) (ranging from \(2\times 10^{-4}\) s to \(8\times 10^{-4}\) s) while \(k_{p1}\) remains fixed at 2.  
The simulation results indicate that increasing \(k_{p1}\) leads to a decrease in the settling time of the signal. Conversely, keeping \(k_{p1}\) constant and increasing \(\tau_{1}\) results in an increase in both the overshoot and settling time of the signal. Therefore, reducing \(\tau_{1}\) not only shortens the transient response duration but also suppresses phase fluctuations. However, reducing \(\tau_{1}\) widens the bandwidth of the active loop filter, which introduces more noise. Hence, for practical design purposes, the loop filter gain \(k_{p1}\) should be appropriately increased, while \(\tau_{1}\) is appropriately decreased.  
Figure 6 demonstrates that varying parameter \(k_{p2}\) has a minimal effect on the settling time. Thus, the settling time of the frequency synthesizer is primarily determined by PLL1.
# Experimental Study on the Bearing Capacity of Gas Oil-Contaminated Coastal Sand
## 2 Laboratory Investigation
### Physical Modeling Tests
The vertical force was applied to the foundation using a pneumatic jack, connected to the foundation through a load cell (see Figure 3). To monitor foundation settlements, an LVDT was used and fixed to the pneumatic cylinder's loading shaft. In the subgrade preparation, two distinct relative densities of 30 and 70% were chosen for each contamination percentage (0%, 5%, 10%, and 15%). The amount of gas oil was calculated as a percentage of the weight of dry sand. To compensate for the evaporation during mixing, a slightly greater amount of gas oil was added to the mix. To achieve homogeneity and uniformity in the contamination distribution, gas oil was well mixed with the sand in a primary tank and then poured layer-by-layer in the main tank for preparation. Before the subgrade preparation, the required amount of sand to fill a specific volume was determined for each desired density. Inside the test tank, six layers of soil were marked with parallel horizontal lines. The determined soil mass for each layer was poured into the tank and compacted until it reached the specified height. Each layer consisted of 10 cm segments, with a fixed amount of sand poured, leveled, and compacted using a square tamper with a tamping area of 64 cm\({}^{2}\). Two distinct relative densities (30% and 70%) were achieved using the same technique. Less effort was applied to compact the deeper layers, considering that they tend to densify further during the compaction of the top layers. Figure 3: Schematic and actual picture of the bearing capacity physical model.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 3 Sensors and Actuators in the IoMT
### Role of Body Sensors in the loMT
#### 3.1.5 Pulse Comimeter  
These sensors [17] are used for monitoring oxygen saturation levels in the blood. They are particularly useful in assessing respiratory conditions such as chronic obstructive pulmonary disease (COPD), asthma, and sleep apnea. Moreover, pulse oximetry is commonly used during anesthesia and critical care to measure oxygen levels and ensure adequate oxygenation of tissues. By providing continuous readings of oxygen saturation, pulse oximeters aid healthcare professionals in promptly identifying and addressing respiratory abnormalities. These devices include the Masimo MightySat Fingertip Pulse Commeter, Nonin Onyx Vantage 9590 Finger Pulse Commeter, and ChoiceMMed Fingertip Pulse Commeter, among others.
# Research on the Reciprocating Friction and Installation Force of Seals in Deep-Sea Samplers
## 3 Results
### Results of Numerical Simulation
#### 3.1.2 The Reciprocation Friction  
Although the pressure-retaining samplers are capable of operating at full ocean depth, instances where the pressure difference between the sealing fluids on both sides of a moving O-ring exceed 30 MPa are rare. Consequently, the numerical simulation on _RF_ is limited to a pressure of 30 MPa. The simulation results for the _RF_ of O-rings with different material parameters are presented in Figure 9 (2 \({}^{\circ}\)C, \(\delta\) = 0.15 mm, \(\theta\) = 20\({}^{\circ}\), \(v\) = 0.5 m/s). The results indicate a linear correlation between RF and pressure, with an approximate increase of 20 N in _RF_ for every 1 MPa rise in pressure. The magnitude of _RF_, based on different material parameters, follows this order: FKM (2 \({}^{\circ}\)C) > FKM (25 \({}^{\circ}\)C) > NBR (2 \({}^{\circ}\)C) > NBR (25 \({}^{\circ}\)C). These findings suggest that selecting NBR as the sealing component in the design of deep-sea equipment can effectively mitigate the increase in motion resistance associated with low temperatures.  
Figure 7: The installation process of the O-ring.  
Figure 8: Simulation results of installation force. (**a**) Relationship between \(\delta\) and \(IF\). (**b**) Relationship between \(\theta\) and \(IF\). (**c**) Relationship between \(\
u\) and \(IF\); (**d**) Relationship between material and \(IF\).  
Figure 9: Simulation results of reciprocation friction: (**a**) NBR (25 \({}^{\circ}\)C); (**b**) NBR (2 \({}^{\circ}\)C); (**c**) FKM (25 \({}^{\circ}\)C); and (**d**) FKM (2 \({}^{\circ}\)C).
## 4 Case Study
### The Basic Test of the Proposed Algorithm
#### 4.1.2 The Optimization Simulation for an Air Conditioner  
For a single air conditioner and air conditioner group, we undertook an optimization simulation with half an hour as the optimal period from the known outdoor temperature curve (\(T_{out}=[26^{\mathrm{o}}\mathrm{C},35^{\mathrm{o}}\mathrm{C}]\)) on a certain summer day, and the result is shown in Figure 7. The energy-saving data in the above air conditioner's simulation is shown in Table 2. It can be seen that a single air-conditioner needs to operate continuously when the outside temperature is too high, and the energy-saving potential is limited. However, for the air conditioning unit, when the total running time is shortened by adjusting the number of opening appliances and setting the proper temperature, the energy-saving effect is significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Appliances**} & **Before** & **After Optimization** & **The amount of Energy** & **Energy Saving** \\  & **Optimization/kWh** & **/kWh** & **Saving/kWh** & **Rate** \\ \hline Single air conditioner & 15.48 & 14.75 & 0.73 & 4.7\% \\ Air conditioner group & 63.29 & 47.66 & 15.63 & 24.7\% \\ (four devices) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the energy saving simulation for an air-conditioner.  
Figure 6: Energy saving optimization for lights and water dispensers. (**a**) Lighting power. (**b**) Lighting time utility. (**c**) Water dispenser power. (**d**) Water dispenser time utility.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### Receiver Architecture  
Considering the engineering design requirements, this paper proposes the architecture of an NB-IoT terminal receiver for satellite communication using the BOS method in Figure 11. After down-conversion, analog-to-digital conversion (ADC), and forward filtering, the received signal undergoes frame synchronization using the NPSS detection method outlined in this paper. During this process, observation values \(L_{1},L_{2},L_{3}\) are recorded. After performing FFT to remove the CP of the OFDM symbols, channel equalization and estimation are carried out. Data is demodulated and decoded to extract satellite ephemerides, and the TDOA algorithm proposed in this paper is used for position estimation. Finally, the TA value is calculated to complete the uplink synchronization.
# Quantum Dilation and Erosion
## 2 Preliminaries
### Operators in Quantum Circuits  
The quantum circuit is composed of \"wires\" and \"gates\". Wires are represented by the qubits that store information, while gates are utilized to execute computational operations. In some sense, the quantum circuit presents some similarity to the classical circuit such as, the classical NOT gate (Figure 1a) and the quantum NOT gate (X gate, Figure 1b). They act in the same way as flipping the state of a bit/qubit.  
Classical binary gates are irreversible and the input logic state cannot be uniquely inferred from the output. For example if the output of the logic AND gate is logic 0, the input could be one of three states (00, 01 or 10). On the other hand all the gates in quantum circuits are reversible. NOT gate is considered as the central operation in both classical and quantum gate circuits. However, NOT gate is defined for only a one-qubit operation, it is not adapted for interactions between different qubits. By adding a control qubit to the NOT gate, we get the \"controlled-NOT\" gate, or CNOT gate. If the control qubit is \(|0\rangle\), the target qubit will not be changed. For the control qubit with value \(|1\rangle\), the target qubit is flipped. See Figure 2d for the representations and table truth of the CNOT-gate. The function of CNOT gate can be represented also as \(|q_{0},q_{1}\rangle\rightarrow|q_{0},q_{0}\oplus q_{1}\rangle\) where \(q_{1}\) represents the control qubit, and \(\oplus\) refers to the operation plus modulo 2. By adding another control qubit to the  
Figure 1: NOT gates in binary and quantum circuits. **(a)** NOT gate in a classical binary circuit, which has input \(c\) and output \(\overline{c}\) (the complement state of \(c\)). **(b)** NOT gate in a quantum circuit (X gate), which has input \(q\) and output \(\overline{q}\).  
CNOT gate, we realize the \"controlled-controlled-NOT\" gate, which is also known as \"Toffoli gate\". The target qubit is flipped only if both control qubits have the value \(|1\rangle\). This gate is also represented by \(|q_{0},q_{1},q_{2}\rangle\rightarrow|q_{0},q_{1},q_{2}\oplus q_{0}q_{1}\rangle\), where \(q_{0}\), \(q_{1}\) design the control qubits [10] (Figure 2e).  
Toffoli gate is considered as a universal reversible classical gate [15; 16]. A universal gate is defined as a building block from which all other gates can be constructed. Nevertheless, toffoli gate is not enough to built a universal quantum gate, we need to combine with the single qubit Hadamard gate in order to obtain the universal quantum gate. We realize quantum circuit by using multiple gates in order to simplify the implementation.
## 5 Disinfection  
The ability of atmospheric pressure plasma to kill bacteria, either by disruption of the cell envelope or through more subtle effects [16], is well known, and indeed is the first effect which has been invoked for utilization in medical practice [17].  
The first paper of the special issue related to CAP disinfection properties is a review, by [PERSON] and co-workers, concerning the effectiveness of this technique on biofilms [18]. While most studies in this field test the plasma action against bacteria cultures in planktonic form, in real life applications bacteria are often found in the form of biofilms, that is groups of microorganisms adhered to a substrate within a self-produced matrix of extracellular polymeric substance, mostly composed of water, polysaccharides, proteins, and extracellular DNA. Biofilms are much more resilient to antibiotics and antiseptics, thanks to the fact that the extracellular polymeric substance forms a physical barrier, responsible for limiting the transport of chemicals into and out of the biofilm, and are usually challenging to eradicate. The ability to significantly affect biofilms is thus a crucial property to be demonstrated if plasma-based disinfection is to be brought to the market. The paper of [PERSON] et al. reviews the existing literature on biofilm eradication through CAP, concluding that the technology appears to be promising, but further effort is required in developing (and certifying, I would add) plasma sources adequate for use in real world environments.  
The second paper, by [PERSON] and co-workers, is aimed at fully understanding the bio-decontamination process in a reduced-pressure oxygen plasma, using _Escherichia coli_ as the target microorganism [19]. This study does not completely fall into the plasma medicine realm, as defined in the introduction, in the sense that it makes use of low-pressure conditions. It is however very interesting, as it makesa thorough comparison of the role of different agents, that is UV radiation, charged species and free radicals, in the decontamination process, and thus offers valuable information also for processes operating at atmospheric pressure. In particular, the authors found that the essential effectiveness on _E. coli_ of the oxygen plasma can to be attributed to the intense etching action of charged species, that is electrons and ions, on the bacilli materials. Lipid peroxidation in the cell membrane by oxygen radicals plays a major role only during the initial phase (< 40 s), and is then restrained by the effect of charged particles. The function of UV radiation is to assist in the whole process, resulting in slight damage and rupture of DNA. This study, while confirming the marginal role of UV radiation, adds another bit to the ongoing debate about the importance of charged species in the deactivation process.
# Efficient Estimation of Synthetic Indicators for the Assessment of Nonlinear Systems Quality
## 3 Results
### Damaged Metallic Structure  
Simulation experiments of a damaged metallic structure were carried out to verify the effectiveness of the proposed method. The damage index HI, proposed in [15], is defined by (19). The model used for the structure is the one already proposed in [16] and used in the experiments in [15]: the system is a one degree of freedom spring-mass-damper (SMD) of the type shown in Figure 4. The damage that has been considered is represented by a bi-linear stiffness k[x(t)], which allows for the simulation of a breathing fracture [16]: a lower stiffness when the fracture is open than when it is closed.  
The bi-linear stiffness has been defined as follows:  
\[k[x(t)]=\begin{cases}\text{k}^{I}\\ (1-\alpha)\text{k}^{I}\end{cases}. \tag{22}\]  
where k\({}^{I}\) is the stiffness of the undamaged system and \(\alpha\) is the parameter representing the extent of damage if \(\alpha=0\) the system is intact. If \(\alpha=1\), the fracture is open and the system is completely damaged.  
We generated data from the system defined in Figure 4 and by relation (22) using the following parameters: M = 1 [kg], b = 2 [N s/m] and k\({}^{I}=20,000\) [N/m].  
Taking into account that the resonance frequency of the simulated system is 22.5 [Hz], an exponential chirp signal operating in the frequency range between 2.25 and 225 [Hz] was used as an input for the identification of the Hammerstein model. The excitation duration was T = 9.36 [s]. For an input amplitude E = 0.1 [N] the output signal maximum absolute value \(|x_{MAX}|\) is of the order of \(4*10^{-5}\) [m].  
To verify the effectiveness of the methodologies proposed in this paper, the damage index HI was estimated, in a range of values of parameter \(\alpha\) between 0.01 and 0.50, for two different signal levels, chosen in order to guarantee signal-to-noise ratios of 10 and 5 dB, respectively: the estimate for the signal-to-noise ratio of 5 dB was also obtained by extrapolating the Hammerstein model parameters obtained in the 10 dB case through expression (12). Figure 5 shows the results obtained for the HI index as the parameter \(\alpha\) varies: the left panel in the case where the amplitude of the excitation signal guarantees a signal-to-noise ratio of 10 dB. The right panel shows two curves, both related to a signal-to-noise ratio of 5 dB. Of the two curves, one is obtained by decreasing the amplitude of the excitation signal adopted; the other was obtained by extrapolating the parameters of the Hammerstein model from the 10 dB case, using (12). It is evident from the right panel how the trends of the two curves provide equivalent information and show a knee of the curve near the same \(\alpha\cong 0.20\) value. Thus, (12) allows to obtain with good approximation the trends of the HI index as \(\alpha\) varies at signal levels other than the one actually used.  
Figure 4: The one degree of freedom system spring-mass-damper.
# Cyber-Storms Cone from Clouds: Security of Cloud Computing in the IoT Era
## 5 Cloud-Specific Security Issues
### Availability
#### VM Isolation  
Availability can be compromised by virtual machines breaking out of isolation and being able to either use all host resources or performing a system halt [49].  
Scheduling issues might be exploited to impact on the performance (and also availability) of other VMs. Indeed, an attacker can manipulate hypervisor scheduling mechanisms in such a way to obtain more resources for his own VM at the expenses of other clients [104]. Such a situation, taken to the limit, can lead to starvation of other VMs or, more in general, can degrade services to such an extent of making services deployed within VMs unusable.  
Virtual Network Issues  
According to [52], poor scalability of virtual networks is another factor that can be exploited for a denial of service (DoS) attack.  
VM Migration Issues  
Malicious VMs can take advantage of live virtual machine migration to perform DoS attacks or achieve performance degradation. The migrant attack is an example of such type of DoS attack. In a migrant attack, a small set of compromised VMs is coordinated to generate useless resource consumption in order to mislead the Cloud monitoring mechanisms to trigger migration processes [66]. Since live migrations are expensive processes, this allows attackers to waste Cloud resources and degrade performances of other VMs. An equivalent class of DoS attack similar to the previous one is Cloud-Internal Denial of Service attacks (CIDoS) [105].  
Researchers in [106; 107], proposed to use live migration for reducing the time of co-residency among virtual machines and hence prevent side-channel attacks. However, it has been recently shown that it could be possible for an adversary to slow down migration processes and therefore still permit the attackers to perform side-channel information stealing [108]. In relation to availability, this attack (known as stalling attack) demonstrates the possibility for co-resident adversaries to prevent migrations and hence degrade performances by obstructing the performance gain that would follow from migrations.  
Cloud-Droplet-Freezing (CDF) is another type of DoS attack which is based on the observation that if migrations of VMs are carried on during a flooding attack for the purpose of load-balancing and trying to mitigate the attack, then it might also contribute to increase the overhead for the Cloud and weaken even more its resource availability [109].
# User Experience of 5G Video Services in Indonesia: Predictions Based on a Structural Equation Model
## 3 Methods
### Processing and Interpretation of the Results  
Through data processing with SEM, the construction of the concept model and the factors were checked first. Examination of the construction of this model is called the Goodness of Fit Index (GFI). The GFI value is guided by the range from 0 to 1, where the higher the value obtained, the better the suitability of the model construction. In addition to the GFI value, the results of the SEM processing analysis for the two variables with a total of 20 factors also require a minimum validity value of 0.32 and a reliability value above 0.6 [39].  
The results of the SEM processing obtained a GFI value of 0.75 from the range 0-1. The validity value of the factor was between 0.52 and 0.85, which means all factors were valid. Meanwhile, the reliability value obtained was 0.86, which means this value was above the required 0.6. From these results, the construction of this model is considered to meet the requirements for use in measurement and analysis [40; 41].  
The correlation values in this model are denoted by \(\alpha\), \(\beta\), and \(\gamma\), where \(\alpha_{1}\)-\(\alpha_{10}\) is the correlation between the measurement of video streaming user experience on the 4G network (UX 4G) with its 10 factors, \(\beta_{1}\)-\(\beta_{10}\) is the correlation between the predicted user experiences of streaming videos on the 5G network (UX 5G) with its 10 factors, and \(\gamma\) is the correlation between UX 4G variables and UX 5G variables related to user experience in streaming video.  
The \(\alpha\) and \(\beta\) values in SEM are mathematically obtained with the following basic equations [38]:  
\[\alpha=\lambda_{\text{nq}}\ \xi+\delta \tag{1}\]  
and  
\[\beta=\lambda_{\text{mp}}\ \mu+\varepsilon \tag{2}\]  
where \(\alpha\) is the correlation value of the exogenous 4G indicator to its factors, \(\lambda_{\text{nq}}\) is an exogenous manifest indicator, \(\xi\), is an exogenous variable, and \(\delta\) is an exogenous error factor. Meanwhile, \(\beta\) is the correlation value of the endogenous 5G indicator to its factors, \(\lambda_{\text{mp}}\) is an endogenous manifest indicator, \(\mu\) is an endogenous variable, and \(\varepsilon\) is an endogenous error factor.