esa-sceva/satcom-chunk-collection huggingface dataset:
# A High Resolution Vernier Digital-to-Time Converter Implemented with 65 nm FPGA
## 3 Experimental Results
### Implementation with Cyclone IV E FPGA
It can be seen that the measured results are a little worse but approximately the same as the post-synthesis simulated results. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cyclone IV E** & **Stratix III** \\ \hline Fabrication Process & 90 nm & 65 nm \\ Input frequency & 5–474 MHz & 5–717 MHz \\ Output frequency & 600–1300 MHz & 600–1600 MHz \\ Input Divisor (D) & 1–512 & 1–512 \\ Feedback Divisor (N) & 1–512 & 1–512 \\ Post scale counter (C) & 1–512 & 1–512 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PLL parameters of adopted FPGAs [16; 17]. Since the proposed DTC is implemented with PLLs, it is insensitive to device mismatch and shows excellent linearity. The measured DNL and INL are a mere \(-0.06\)LSB\(-0.07\)LSB and \(-0.43\)LSB\(-0.58\)LSB, as depicted in Figures 8 and 9, respectively. The proposed DTC utilizes 344 slice look-up-tables (LUTs), 207 registers and 3 PLLs when realized on a Cyclone IV E FPGA with 32 input bits.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Calculation Formulas of Main Circuit Parameters
#### 3.2.8 Calculation of the Converter Transformer Tap  
The range of the converter transformer tap is mainly determined by the steady-state fluctuation range of the AC bus voltage, the operation mode of the HVDC transmission system, the reduced DC voltage operation level, the power limit transmitted under the reduced DC voltage operation mode and the maximum allowable control angle of the thyristor valve.  
In the rated operation mode, the rated tap of the converter transformer corresponds to the '0' gear. The rated transformation ratio can be calculated as follows:  
\[\eta_{\text{nom}}=\frac{U_{\text{IN}}}{U_{\text{vN}}}=\frac{U_{\text{IN}}}{ \frac{U_{\text{diob}}}{\sqrt{2}}\frac{\pi}{3}} \tag{42}\]  
When calculating the on-load tap range of the converter transformer, the maximum and minimum transformation ratio can be calculated as follows:  
\[\eta_{\text{max}}=\frac{U_{\text{1 max}}U_{\text{diob}}}{U_{\text{IN}}U_{ \text{diominOLTC}}} \tag{43}\]  
\[\eta_{\text{min}}=\frac{U_{\text{1 min}}U_{\text{diob}}}{U_{\text{IN}}U_{ \text{diomaxOLTC}}} \tag{44}\]  
where \(U_{\text{IN}}\), \(U_{\text{1 max}}\), \(U_{\text{1 min}}\) are the rated value, maximum value and minimum value of the grid side AC voltage of the converter transformer, respectively. \(U_{\text{vN}}\) is the rated value of the valve side AC voltage. \(U_{\text{diomaxOLTC}}\) and \(U_{\text{diominOLTC}}\) are the maximum and minimum no-load DC voltage for equipment selection, respectively.  
The on-load tap changer of the converter transformer can be calculated according to:  
\[TC=\frac{\eta-1}{\Delta\eta} \tag{45}\]  
where \(\Delta\eta\) is the one on-load tap changer step, which is taken as 1.25%.  
Therefore, the maximum and minimum on-load tap changers of the converter transformer are:  
\[+TC=\frac{\eta_{\text{max}}-1}{\Delta\eta} \tag{46}\]  
\[-TC=\frac{\eta_{\text{min}}-1}{\Delta\eta} \tag{47}\]  
It can be seen that the number of positive and negative taps is determined according to the steady-state variation range of the grid side AC voltage and the extreme value of the no-load DC voltage at the valve side. However, the result calculated by the above formula only meets the the minimum requirements. The calculation of the maximum on-load tap changer also needs to consider the 70% reduced DC voltage operation. In addition, the degree of the DC voltage drop is related to the control angle of the converter.
# Set-Point Control of a Spatially Distributed Buck Converter
## 5 Closed-Loop Control of the Distributed Buck Converter  
### Continuous Control Mode  
In the following it will be assumed that the voltage at the beginning of the transmission line can be continuously actuated and will hence serve as a control handle. In order to design an appropriate control law, the mean current deviation from the desired current distribution \(i_{d}(z,t)\) will be used as an error measure.  
\[e=\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z \tag{7}\]  
This results in the following discrepancy \(\rho\) with the according quadratic control-Lyapunov functional \(V\).  
\[V=\rho=\frac{1}{2}e^{2} \tag{8}\]  
To derive a control law stabilizing the buck converter in continuous voltage control mode the time derivative of the control-Lyapunov functional \(V\) along the system trajectory is taken:  
\[\dot{V} = \int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z\cdot\int_{0}^{l}\left[\frac{ \partial i_{d}}{\partial t}+\frac{1}{L^{\prime}}\bigg{(}\frac{\partial v}{ \partial z}+R^{\prime}i\bigg{)}\right]\mathrm{d}\,z \tag{9a}\] \[= \int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z\cdot\left[\int_{0}^{l}\frac{ \partial i_{d}}{\partial t}\,\mathrm{d}\,z+\frac{1}{L^{\prime}}v\bigg{|}_{0} ^{l}+\frac{R^{\prime}}{L^{\prime}}\int_{0}^{l}i\,\mathrm{d}\,z\right]\] (9b) \[= \int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z\cdot\left[\int_{0}^{l}\frac{ \partial i_{d}}{\partial t}\,\mathrm{d}\,z+\frac{R^{\prime}}{L^{\prime}}\int_{ 0}^{l}i\,\mathrm{d}\,z+\frac{1}{L^{\prime}}(v(l,t)-v(0,t))\right]. \tag{9c}\]  
Here, the input voltage \(v(0,t)\) on the left boundary has to be chosen such that the time derivative of the control-Lyapunov functional \(V\) is negative definite in order to achieve a stable closed-loop system. One possible choice is the following compensating control law  
\[v(0,t)=v(l,t)+L^{\prime}\int_{0}^{l}\frac{\partial i_{d}}{\partial t}\, \mathrm{d}\,z+R^{\prime}\int_{0}^{l}i\,\mathrm{d}\,z+K\cdot L^{\prime}\int_{ 0}^{l}(i_{d}-i)\,\mathrm{d}\,z. \tag{10}\]  
The first term on the right hand side of Equation (10) is the output voltage on the right boundary. The second and third term describe the voltage drop along the line resulting from the inductance and resistance distribution with regard to the current deviation. The fourth term is the correction term with the control gain \(K>0\). Applying the control law (10) then results in an exponential converging Lyapunov functional \(V\) and, thus, achieves exponential stability with respect to the discrepancy \(\rho\):  
\[\dot{V}=-K\bigg{(}\int_{0}^{l}(i_{d}-i)\,\mathrm{d}\,z\bigg{)}^{2}=-2 KV. \tag{11}\]  
Now, we assume that the desired current \(i_{d}\) is constant. In addition, we consider a lossless transmission line, i.e., \(R^{\prime}=0\). However, we can set this control signal in relation to the constant input voltage to obtain the duty ratio  
\[d(t)=\frac{1}{E}\bigg{[}v(l,t)+K\cdot L^{\prime}\int_{0}^{l}(i_{d}-i)\, \mathrm{d}\,z\bigg{]}, \tag{12}\]  
which imposes the digital PWM signal to the distributed buck converter via the boundary condition (4c).
# Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning
## 3 System Model
### Delay Model  
The delay that users can perceive can be composed of two parts: computing delay and transmission delay. The transmission delay \(tr_{n}^{u}\) is directly proportional to the size of the transmitted data and inversely proportional to the bandwidth, which can be calculated by the following formula:  
\[tr_{n}^{u}=\frac{\kappa_{n}^{u}}{B_{n}^{tr}} \tag{1}\]  
where \(\kappa_{n}^{u}\) represents the task size, and \(B_{n}^{tr}\) represents the bandwidth between the BS to which the user is connected and the destination MEC server. The computing delay \(com_{n}^{u}\) represents the time it takes for the task to obtain the result on the MEC server. The delay can be calculated by the CPU computing cycle required by the task and the computing power of the MEC server, as shown below:  
\[com_{n}^{u}=\frac{c_{n}^{u}}{cap_{n}}. \tag{2}\]  
In summary, the task \(T_{u}\) is executed on the MEC server \(n\), and the total delay \(D_{u}\) perceived by the user can be expressed as a formula:  
\[D_{u}=tr_{n}^{u}+com_{n}^{u}. \tag{3}\]  
Figure 1: VM migration scenario.
# Photocatalytic Inactivation as a Method of Elimination of _E. coli_ from Drinking Water
## 2 Mechanism of the Photocatalytic Inactivation of _E. coli_  
Several studies have already discussed the mechanism of photocatalysis. Hence, only the summary is given in this review [33; 34; 35; 36]. Photocatalysis is easy to implement as it essentially involves the use of a light source and a photocatalyst. An interaction between light and the semiconductor solid catalyst results in the absorption of light with energy equal to or greater than the band gap energy of the semiconductor photocatalyst. This leads to the excitation of electron(s) from the lower energy level, the valence band (VB), to the higher energy level, the conduction band (CB). Generally, semiconductors consist of an electron filled valence band and an empty conduction band [37]. The migration of electrons from the occupied VB leaves positively charged holes on the VB (electron vacancy). It is possible for both the electrons generated and the holes to recombine, which is not desirable because their combination will lead to the loss of energy informing of heat energy. The second possibility is the production of hydroxyl radicals as a result of the interaction of holes with water and superoxides are formed as a result of the interaction of the generated electron with oxygen [34; 38] as shown in Figure 2. These photogenerated highly reactive species (ROS) or free radicals (such as hydroxyl radicals (OH') and superoxide (O\({}^{2-}\))) are capable of eliminating biological and chemical water contaminants [8; 15; 16]. The free radicals produced are highly effective antibacterial agents within aqueous environments.  
Three common theories have been proposed for the mechanism of inactivation of bacteria. The first is the destruction of the DNA, the second is the inhibition of the respiratory activities as a result of the oxidation of coenzyme A by the photogenerated reactive species, which leads to their loss from the cells. The third mechanism is the destruction of the membranes by reactive species, thereby leading to cell content's leakage [35; 39; 40]. Irrespective of the mechanism, the inactivation is affected by the photogenerated holes, electrons, and the reactive oxygen species (such as hydroxyl radicals, peroxyl radicals and hydroperoxyl radicals) generated during the mechanism of photocatalysis.  
As the _E. coli_ inactivation proceeds, the shape of _E. coli_ cells begins to get disfigured and the shape becomes different from the original shape before the inactivation process. This is followed with the rupturing of the cell wall, which permits the reactive species to permeate the cell and the size of the remaining part of the cell decreases noticeably [41]. The inorganic species used to monitor the destruction is potassium ion, since it is responsible for regulating polysomes and proteins. Rupturing of cell membrane leads to the leaking of potassium ions. The concentration of potassium ion has been discovered to increase with an increase in the time of photocatalytic inactivation [9; 41].
## 3 Results
### Evaluation of the Electrospinning Conditions of SBA-15 Coating  
The effect of electrospinning slurry composition on the extraction efficiency of the SBA-15-SPME Arrow for the five target analytes was investigated (Figure S1a). The amount of SBA-15 in the SPME Arrow coating was varied to assess its impact on adsorption capacity. Increasing the SBA-15 content from 100 mg to 300 mg enhanced adsorption efficiency due to the increased surface area available for analyte interaction. However, beyond 200 mg, the improvement became marginal, likely because the additional SBA-15 did not significantly increase the effective surface area exposed to the sample. Optimal adsorption performance was achieved when 100 mg of PAN and 200 mg of SBA-15 were mixed with 3 mL of DMF (Figure S1a). This composition appears to strike the best balance between the mechanical properties of the coating and its adsorption capacity. PAN, as the polymeric support, provides flexibility and durability, ensuring good adhesion to the SPME Arrow while withstanding multiple extraction cycles [26; 27]. The higher proportion of SBA-15 increases surface area and adsorption capacity, enabling more efficient extraction of veterinary drugs. Preliminary tests confirmed that this combination provides the best performance in terms of coating stability and extraction efficiency.  
The thickness of the SBA-15 coating increased from 135 \(\upmu\)m to 300 \(\upmu\)m as the electrospinninging time was extended from 20 to 60 min (Figure S2). Correspondingly, the extraction capacity of the SBA-15 coating for the five veterinary drugs significantly improved (Figure S1b). However, extending the electrospinninging time beyond 40 min did not notably enhance extraction capacity, likely due to the excessive thickness of the coating, which could impede diffusion pathways and slow adsorption kinetics [25]. Therefore, a 40 min electrospinninging duration was selected for fabricating the SBA-15-SPME Arrow (Figure S1b). SEM images of the SBA-15-SPME Arrow (40 min electrospinningning), captured at different magnifications, confirmed the uniformity of the SBA-15 coating on the bare SPME Arrow (Figure 4).  
Figure 4: SEM images of SBA-15 coating (40 min electrospinningning) with different magnifications: (**a**) 30\(\times\), (**b**) 50\(\times\), (**c**) 10,000\(\times\), and (**d**) 30,000\(\times\).
## 2 Problem Statement
### Process of SCOS  
In industrial software platforms, each user-submitted requirement is treated as an independent task. Based on the user's specific business scenario, the platform further decomposes the task into multiple software functional subtasks and performs resource search and matching within the service cloud pool to support subsequent SCOS. As illustrated in Figure 1, the SCOS process in industrial software can be divided into the following five key steps:  
1. User Requirement Submission: The user, based on their specific business scenario, provides a preliminary functional requirement or objective.
2. Task Decomposition: The user requirements are modularly decomposed to extract core functional features, which are then further subdivided into multiple sub-tasks for detailed execution.
3. Service Search and Matching: For each subtask, the system matches all suitable candidate software resources from the service cloud pool according to the user's quality of service and functional requirements and generates the corresponding set of software resources.  
* SCOS: The industrial software platform selects appropriate software from the resource set corresponding to each subtask and generates a Pareto-optimal set of service compositions that satisfy the constraints, based on a multi-objective optimization model.
* Task Feedback and Execution: All optimal composition solutions are returned to the user, who selects and executes the preferred one.  
This study focuses on the fourth stage of the above process--service composition optimization. The optimization results at this stage have a direct impact on both the efficiency and effectiveness of the platform's service delivery to users and therefore hold significant research value.
## 9 Conclusions  
It is evident that the RPL routing protocol is still vulnerable to a wide range of security attacks. RPL design has no sufficient security support for network resilience against the different internal routing attacks. The experimental study presented in this paper helps in establishing a firm understanding of the performance of RPL networks under a set of diverse routing attack scenarios. The outcome of this work can serve as a practical reference for deeply comprehending RPL-based IoT networks under routing attacks. This is an important step towards the development of effective security solutions for enriching the security of the protocol and reviving its potential for a wider scope of IoT applications.  
The results indicate the adverse impacts of routing attacks on the overall performance of RPL networks. Even in simple attack scenarios, the networks experienced noticeable degradation of QoS performance and network stability in addition to considerable increases in control traffic overhead and energy consumption. This was more evident in large-scale experimental setups and also under composite and hybrid attacks.  
Considering these implications of the routing attacks in RPL networks, the development of more secure routing mechanisms becomes vital. Without efficient security support, compromising the integrity of the RPL control messages becomes easy. However, security solutions should be developed based on a lightweight and simple approach. This is crucial given the limited capabilities of typical IoT devices which operate in low-power and lossy networks. Utilizing the outcomes of this study for addressing such considerations in an effective routing security solution is the main target for our future work. The focus will be on the development of an integrated RPL security architecture that provides effective protection from potential RPL routing attacks.  
Conceptualization, I.S.A. and M.A.; methodology, I.S.A. and M.A.; software, I.S.A. and M.A.; validation, I.S.A. and M.A.; formal analysis, I.S.A. and M.A.; investigation, I.S.A. and M.A.; resources, I.S.A.; data curation, I.S.A. and M.A.; writing--original draft preparation, I.S.A.; writing--review and editing, I.S.A. and M.A.; visualization, M.A.; supervision, I.S.A.; project administration, I.S.A.; funding acquisition, I.S.A. All authors have read and agreed to the published version of the manuscript.  
The researchers would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project.  
Not applicable.  
Data Availability Statement: Data are available upon request.  
The authors declare no conflict of interest.
## 3 Co-Channel Coexistence Mitigation Methods
### Superframe and Slot  
All of the mitigation methods mentioned in this paper are based on the concepts of superframeand slot. The superframe SF is a time interval consisting of two slots, one reserved for NR-V2X (hereafter denoted as \(\mathrm{S_{NR}}\)), and the other for IEEE 802.11p (hereafter denoted as \(\mathrm{S_{11p/bd}}\)). These concepts are exemplified in Figure 1, where the \(T_{\mathrm{SF}}\), \(T_{\mathrm{NR}}\), and \(T_{\mathrm{11p/bd}}\) stand for the durations of the corresponding intervals.  
The superframe duration is in all the cases fixed. The slot duration can instead be fixed, hereafter called static superframe settings, or variable, hereafter called dynamic superframe settings. The dynamic solution is preferable since it can better cope with variable distributions of the two technologies among the VUEs, but it introduces more complexity. In addition, the slots can be indicated by an external entity, called hereafter controlled superframe settings, or estimated independently by each node, called hereafter autonomous superframe settings. The use of an external entity can simplify the methods since all nodes then have the same settings, but also means that road side units (RSUs) are required and additional vehicle-to-infrastructure (V2I) communication protocols need to be defined.  
It should be noted that superframes and slots are relatively easy to implement in NR-V2X, since it is already a time-slotted system and already foresees the possibility of having pools of available resources as a subset of all resources. In contrast, IEEE 802.11p is based on completely asynchronous access to the channel.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 2 Previous Work on Perceptual Quality Assessment
[33] tried to reproduce a real-time communication scenario and performed a study that included variations of compression and network parameters, including video frame rates, quantization parameters, packet-loss rates (audio and video), noise filters, and compression bitrates (audio and video). Results showed that packet-loss errors (for audio and video) had a greater impact on the perceived audio-visual quality than other types of parameters. Certainly, there is a need to fully understand the audio-visual quality perception process. The complex interactions between audio and video pose a challenge and demand new methods to conduct perceptual experiments. The Immersive Methodology has shown promising results and offers a reliable method to deal with non-traditional experimental scenarios (e.g., long-duration stimuli, numerous test conditions, and diverse content). In the following Section, the motivation behind this study and the experimental settings are presented.
## 3 Results and Discussion
### Measurement Results
displacement (H2) presented a minimal displacement in the range of 0-1 mm from the flood level (S1) to the overtopping water level (S3). This small displacement indicates that it contributes to the suppression of damage or overtopping of the dam crest owing to the structural movement caused by the high weight of the gabion structure. The upstream vertical displacement (V1) showed a slight increase and repetition from the flood level (S1) to the overtopping water level (S3). However, its effect on the settlement was minimal. By contrast, the downstream vertical displacement (V2) continuously increased at each water level. When the upstream water level decreased, a large settlement was expected owing to the weight of the overtopping prevention structure. However, no significant settlement was observed. These results indicate that the pressure effect of the overtopping prevention structure contributes to suppressing the expansion or deformation of the dam body owing to changes in the water level. The overtopping prevention structure exhibited downstream movement, attributed to the saturation of the upstream fill zone. These findings suggest that a differential settlement might emerge. However, the horizontal displacement was approximately 0.5%, and the vertical displacement was approximately 0.2%, indicating that the effect of the overtopping prevention structure on the dam stability was negligible. Implementing a reservoir with an overtopping prevention structure based on the design presented in this experiment or implementing a more robust design can serve as a basis for reducing the risk of reservoir failure owing to overtopping.