esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Formulation of the Effectiveness Criterion
### Definition of Equivalent Substructure Acceleration \(a_{Es}\)  
Deck isolation is implemented by replacing the bridge bearings with seismic isolators and creating an isolation layer between the deck and the substructures. According to this layout, the isolation is effective in reducing the acceleration of the deck but has no effect on the acceleration of the masses beneath the isolation layer. Therefore, it is necessary to evaluate whether the capacity of the piers is sufficient to resist the seismic force associated with their own masses.  
In accordance with IBC [20], SS7.9.4.1, the mass of the not-isolated substructure is defined as the mass of the pier cap and one-third of the mass of the pier:  
\[m_{sub}=m_{piercap}+m_{pier}/3 \tag{8}\]  
Assuming that the deck is completely isolated, the substructure can be studied on its own. The effective period of the substructure is  
\[T_{sub}=2\pi\sqrt{m_{sub}/K} \tag{9}\]  
where \(K\) is the stiffness of the pier calculated according to Equation (5).  
Given the period, the substructure acceleration \(a_{sub}\) is obtained from the 5%-damped response spectrum.  
Referring to the three accelerations introduced so far, namely, \(a_{EL}\), \(a_{As-Built}\), and \(a_{sub}\), the associated seismic forces can be written as  
\[V_{EL} =a_{EL}\cdot m_{eff} \tag{10a}\] \[V_{As-Built} =a_{As-Built}\cdot m_{eff}\] (10b) \[V_{sub} =a_{sub}\cdot m_{sub} \tag{10c}\]  
To compare the accelerations in the same space, it is necessary to refer them all to the same mass. Therefore the \"Equivalent Substructure acceleration\" is introduced:  
\[a_{ES}=\frac{a_{sub}\cdot m_{sub}}{m_{eff}} \tag{11}\]
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
###### Abstract  
This study proposes a lightweight solution to estimate affine parameters in affine motion compensation. Most of the current approaches start with an initial approximation based on the standard motion estimation, which only estimates the translation parameters. From there, iterative methods are used to find the best parameters, but they require a significant amount of time. The proposed method aims to speed up the process in two ways, first, skip evaluating affine prediction when it is likely to bring no encoding efficiency benefit, and second, by estimating better initial values for the iteration process. We use the optical flow between the reference picture and the current picture to estimate quickly the best encoding mode and get a better initial estimation. We achieve a reduction in encoding time over the reference of half when compared to the state of the art, with a loss in efficiency below 1%.  
block-based coding; video coding; H.265/HEVC; affine motion compensation +
Footnote †: journal: Applied Sciences  
_Article_  
Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 3 Methods for Real-Time Food Monitoring
### Time-Temperature Sensors  
Sensors based on temperature detection consist of devices in direct contact with the food products [135, 136, 137, 138]. As these variations typically result in significant mechanical, chemical, electrochemical, enzymatic, or microbiological changes in the food samples, temperature sensors are extremely useful in spoilage monitoring [139, 140, 141, 142, 143, 144]. Time-temperature indicator (TTIs) systems can be divided into three main groups: critical temperature indicators, critical time-temperature indicators, and full history indicators [12]. In detail, critical temperature indicators can only provide information in regard to deterioration and changes in food organoleptic properties due to the occurrence of temperature dissimilarities with respect to a reference value for a certain time. Usually, this kind of sensor exploits irreversible but visible changes in the color of the indicator. For instance, Figure 4a shows a critical temperature sensing system based on an enzymatic reaction with a sensitive spot that changes color under thermal exposure (i.e., orange color indicates that the product has been compromised) [145]. Critical time-temperature indicators supply indications  
Figure 3: (**a**) Photo of a generic humidity-based sensor radio frequency identification (RFID) tag and (**b**) transmission coefficient S21 of the sensor unit at 25 \({}^{\circ}\)C. Reproduced with permission from [126]. Copyright © 2018 Elsevier B.V. All rights reserved. (**c**) Spectra shift of an opal hydrogel composite by increasing RH (%). Reproduced with permission from [134]. Copyright © 2020 Published by Elsevier Ltd.  
associated with the cumulative effect of time and temperature on product quality or safety when these have been exposed to a temperature above the reference one for a certain period. By way of example, Figure 4b reports a commercially available critical TTIs system for the monitoring of frozen food products. A blue color appears in the viewing window if a breach of the threshold temperature has occurred. The duration of one or more breaches is shown by how far the blue color has progressed along with the time markers [146]. Finally, full history indicators continuously monitor the temperature changes to which a sample is subjected, with the data being recorded in terms of irreversible changes that explain food safety and quality to consumers. Generally speaking, temperature-based sensors are, nowadays, the only ones exploited in the food industry, owing to their relatively small size, low cost, and simplicity compared to other food monitoring devices [147; 148; 149; 150; 151].
## 3 System Model and Problem Formulation
### System Model
The uplink and downlink rates for transmitting the output data of task \(i\) from execution node \(k\) to execution node \(v\) as the input data of task \(j\) are given by \(R_{ij}^{kv}=B\cdot\log_{2}\left(1+\frac{c_{ij}^{kv}}{\sigma^{2}}\right)\) and \(\tilde{R}_{ij}^{kv}=\tilde{B}\cdot\log_{2}\left(1+\frac{c_{ij}^{kv}}{\sigma^{ 2}}\right)\), respectively, where \(B\) and \(\tilde{B}\) denote uplink and downlink the communication bandwidth, respectively; \(c=0.935\) watt denotes the communication cost per unit time, which is an approximation [39; 40]; and \(\sigma^{2}\) and \(\sigma^{2}\) denote uplink and downlink the noise power, respectively. For simplicity, we assume equal noise power without loss of generality. Therefore, the communication time and cost for transmitting the output data of task \(i\) from execution node \(k\) to execution node \(v\) as the input data of task \(j\) can be expressed as follows:  
\[T_{ij}^{kv}=\frac{O_{i}}{R_{ij}^{kv}}+\frac{I_{j}}{\tilde{R}_{ij}^{kv}},\quad C _{ij}^{kv}=c\cdot T_{ij}^{kv} \tag{2}\]  
where \(O_{i}\) is the output data of task \(i\) and \(I_{j}\) is the input data of task \(j\),  
Figure 2 illustrates an example of an offloading scheme that considers both service caching and task dependency, which consists of three execution nodes. Execution node \(m_{2}\) caches the functionality of task 1, and execution node \(m_{3}\) caches the functionalities of tasks 2 and 3. The application task set is initiated by execution node \(m_{1}\). Specifically, virtual nodes are offloaded to \(m_{1}\). However, since neither \(m_{1}\) nor \(m_{3}\) caches the functionality of task 1, task 1 is offloaded to \(m_{2}\). Similarly, tasks 2 and 3 are offloaded to \(m_{3}\). In this example, \(m_{1}\) and \(m_{2}\) represent mobile devices, while \(m_{3}\) represents an edge node. The cloud node is not considered in this case.
# Efficiency of Algorithms for Computing Influence and Information Spreading on Social Networks
## 1 Introduction
### Influence and Information-Spreading Mechanisms
Influence-spreading models proposed in this study are continuous models as the opinion state variables of our model take continuous probability values. In this study, differing from the definition of [11], we categorise the case of processes using self-avoiding paths as simple contagion (SC) processes while all other alternatives are considered complex contagion (CC) processes. If loops are allowed, one node can be visited several times during the spreading process. If loops are not allowed, we say that only self-avoiding paths are possible. In summary, we can say that different versions of complex contagion are more common in describing real-world phenomena. An SC model using self-avoiding paths does not allow breakthrough influence [7] and is used for describing information transfer in a specific case of spreading normalised data items by agents with memory. Additionally, SC models can be used to describe specific virus epidemics with immunisation. The methodology for modelling and analysing influence spreading, human behaviour and interaction with others in a social network has been published in our earlier studies [6; 7]. Here, we focus on presenting the explicit pseudo-algorithms in a form that enables researchers to implement and further develop the models. In addition, we discuss the efficiency and application areas of the methods. We provide examples of the modelling results for two networks of different sizes: a social media network and a social network.
## 3 Experiment and Analyses
### Dynamic Experiment
#### 3.2.2 Fault Exclusion  
In combination with Figures 12 and 13, in order to ensure the accuracy and reliability of positioning, the best way to exclude the fault is to eliminate PL8. Figure 15 shows the test statistics based on pseudorange and carrier phase after PL8 is eliminated.  
According to Figure 15, it can be found that the test statistics based on pseudorange and carrier phase return to normal after PL8 areexcluded and the carrier phase-based statistics are much smaller  
Figure 14: Test Statistics when simulated cycle slips are added.  
Figure 15: (**a**) The Test Statistics of CRAIM after PL8 is excluded, **(b**) The Test Statistics of PRAIM after PL8 is excluded.  
than the threshold. Since the normal DD observations can be received after fault exclusion (PL8 is eliminated). The observations are only affected by general factors such as noise and the test statistics based on posterior residuals are less than the threshold. At the same time, the correctness of AR is verified. Although the pseudorange-based test statistics are below the threshold, they are relatively large, which further confirms the unavailability of pseudorange observations in high-precision positioning for indoor pseudolites.  
In theory, the best way is to continue to receive the signals of PL8 when it returns to normal. However, it is revealed that PL8 has been in a faulty state during the dynamic process according to Figure 13. Therefore, the signals of PL8 are not received during the whole experiment. In addition, PL8 is normal in the first 18 epochs, but the rover station is in a static state, so whether the PL8 is used at this stage does not affect the positioning results. The result without PL8 is shown in Figure 16. The red line represents the reference trajectory.  
According to Figure 16 a,b, the motion trajectory conforms to the actual situation basically that the carrier is controlled to move back and forth along a straight rail. The positioning precision is at centimeter-level in ambiguity fixed solution and the fixed-rate reaches 99.2%. Therefore, it can be judged that PL8 is faulty and the normal observations cannot be obtained during the whole experiment according to the test statistics and threshold. The signals of PL8 should not be received in the whole process. After fault exclusion, all DD observations with good quality can be obtained and indoor positioning results with high-precision can be achieved, which ensures the reliability and availability of indoor pseudolite positioning.  
Figure 16: (**a**) The trajectory of Experiment2 after FaultExclusion, (**b**) Results in the X, Y Direction of Experiment 2 after Fault Exclusion.
# Safety of Nuclear Power Plants with Respect to the Fault Displacement Hazard
## 5 Sensitivity/Vulnerability of the Fundamental Safety Functions to the PGD
### Emergency Heat Removal from the Fuel and Containment  
For the cooling down and continuous heat removal from the fuel, redundant emergency core cooling systems are in place at nuclear power plants (practically independent of the type and design) that transfer the residual heat to the ultimate heat sink (natural or artificial water body or air).  
In case of typical PWR design, the safety systems inside the containment and safety buildings are composed by vessel- and piping-type components fixed to the walls and floors.  
The pipelines and cables passing through building interfaces are designed for allowing differential movements due to seismic ground motions. The relative displacement between the building interfaces can cause damages in distribution systems. However, rather simple design solutions exist for avoiding these damages.  
The heat from the reactor should be transported to the ultimate heat sink that is either a natural or artificial water body or the air. The systems for heat removal to water body consist of piping heat exchangers and pumps. The pipelines and cables are crossing the interfaces between the buildings and run usually underground. Critical could be the interfaces between structures. Diesel generators are sources of emergency power supply. These aggregates are not sensitive to the surface movements. In case of cooling towers used for heat sink, cooling ponds are usually designed as a redundant and ultimate heat sink that can be damaged by the PGD.  
In the new designs of reactors, passive emergency core cooling systems and passive containment heat removal systems are designed that do not need external power supply. These are composed from heat exchangers and piping that are not sensitive to the tilting of the building.
# Teletraffic Analysis of DoS and Malware Cyber Attacks on P2P Networks under Exponential Assumptions
## 4 Malware and DoS Attacks in the P2P Network
### Denial of Service and Distributed Denial of Service  
A denial of service (DoS) attack attempts to make a node or network and its resources unavailable to its intended users by overloading [6]. Figure 3 shows the flow chart for this kind of attack. A distributed denial of service (DDoS) attack usually means that a group of network nodes launches DoS attacks against the same victim. They both cause the  
Figure 2: Flow chart for the operation of the malware attack in the P2P network.  
service to stop working by using reasonable service requests to exhaust the resources of the target host. The host sends riddles to its clients before continuing with the requested computation, thus ensuring that the client performs an equally costly computation. DoS and DDoS attacks are very likely to happen in P2P systems. Indeed, in a P2P network, there are numerous participants, and the traffic generated by them is considerably large. Therefore, it is very difficult to predict the traffic between nodes. Hence, attackers can make use of this kind of behavior to overload the network and then disrupt or disable the P2P network. The impact of such an attack is more important when there are millions of concurrently active peers because there is the risk that it could serve as a DDoS engine for attacks against a targeted host. Since any node can act as a router in P2P systems, DDoS attacks are difficult to detect [53; 54; 55; 56; 57].  
The query flooding a P2P network can be easily attacked by sending a massive number of queries to peers. Furthermore, this attack can be performed by malicious nodes transmitting a jamming signal that causes enough interference in the surrounding nodes, effectively impairing their communication capabilities. Attacks can also use the P2P network as an agent to attack other targets, such as websites. Peers in the network request files from a target and overwhelm the victim with enormous bandwidth usage. Such attacks intend to exhaust resources, paralyzing the capacity of the target. The exhausted resources include the target's CPU processing, downstream bandwidth, upstream bandwidth, etc.
# Space-Efficient, Fast and Exact Routing in Time-Dependent Road Networks +
## 2 Materials and Methods
### Shortcut Unpacking Data
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**CCH arcs**} & \multicolumn{2}{c}{**Expansions per arc**} & \multicolumn{2}{c}{**Index**} & \multicolumn{2}{c}{**Running Time [s]**} \\ \cline{4-9}  & & **[-10\({}^{3}\)]** & **Avg.** & **Max.** & \(=\)1 [\%] & **[GB]** & **Phase 1** & **Phase 2** \\ \hline Ber & Monday & 1977 & 1.040 & 25 & 98.6 & 0.09 & 1.5 & 6.2 \\  & Tuesday & 1977 & 1.039 & 31 & 98.6 & 0.09 & 1.5 & 6.2 \\  & Wednesday & 1976 & 1.038 & 19 & 98.6 & 0.09 & 1.5 & 6.2 \\  & Thursday & 1977 & 1.039 & 23 & 98.6 & 0.09 & 1.6 & 6.2 \\  & Friday & 1975 & 1.037 & 28 & 98.7 & 0.09 & 1.5 & 5.8 \\  & Saturday & 1961 & 1.023 & 21 & 99.1 & 0.09 & 1.5 & 3.8 \\  & Sunday & 1957 & 1.021 & 27 & 99.2 & 0.09 & 1.6 & 3.3 \\ \hline Ger06 & Monday & 22,499 & 1.073 & 42 & 98.4 & 1.06 & 30.0 & 20.9 \\  & midweek & 22,519 & 1.075 & 44 & 98.4 & 1.06 & 30.1 & 21.6 \\  & Friday & 22,445 & 1.064 & 43 & 98.6 & 1.05 & 30.2 & 17.2 \\  & Saturday & 22,229 & 1.031 & 37 & 99.2 & 1.03 & 30.2 & 6.0 \\  & Sunday & 22,128 & 1.019 & 39 & 99.5 & 1.02 & 29.8 & 3.6 \\ \hline SynEur & Low & 88,884 & 1.036 & 23 & 99.2 & 4.14 & 238.3 & 82.7 \\  & Medium & 90,514 & 1.109 & 24 & 97.6 & 4.31 & 231.5 & 224.8 \\  & High & 94,302 & 1.264 & 31 & 94.6 & 4.71 & 233.3 & 523.0 \\ \hline Ger17 & Tuesday & 31,488 & 1.090 & 107 & 98.5 & 1.50 & 35.0 & 107.4 \\ \hline Eur17 & Tuesday & 114,857 & 1.099 & 115 & 98.4 & 5.47 & 189.6 & 557.0 \\ \hline Eur20 & Tuesday & 128,921 & 1.191 & 109 & 96.9 & 6.32 & 209.6 & 1039.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Preprocessing statistics. Running times are for parallel execution on 16 cores.
## 3 Results and Discussion
### Material Removal Rate Results and Analysis  
In accordance with review of the obtained data, Figure 7 presents a graphical analysis of the behaviour of material removal rate towards different traverse speed and material thickness in abrasive waterjet profile cutting of AISI 304L.  
In this study, the lowest value of KTA of 0.825\({}^{\circ}\) for arcs profile and 0.916\({}^{\circ}\) for straight profile were achieved at the lowest level of traverse speed at 90 mm/min rate. The maximum value of MRR of 769.50 mm\({}^{3}\)/min was obtained from machining of curvature profile and 751.50 mm\({}^{3}\)/min achieved when cutting straight line profiles at a higher value of traverse speed at 150 mm/min rate. A similar trend linking increased levels of input parameters with increasing values for output parameters has been observed for both curvature (i.e., arcs and straight line profiles) and different thicknesses of materials. The process of material removal for AWJM in ductile material, such as steel, takes place through erosion caused by impinging abrasive particles from the waterjet stream. Hence, higher kinetic energy generates higher erosion rates and leads to higher material removal rate. With a higher level of traverse speed, the machining rate increases, resulting in more material being removed from the workpiece. In turn, the material removal rate is noted to be mainly influenced by traverse speed, where these findings accord with previous studies [16]. In this work, the amount of material removed increased by approximately 60-80% as the value of material thickness increased from 4 mm to 12 mm. The study showed that a higher material thickness obtained a higher value of MRR 346.50 mm\({}^{3}\)/min for 4 mm, 612.00 mm\({}^{3}\)/min for 8 mm, and 769.50 mm\({}^{3}\)/min for 12 mm material thickness of AISI 304L material. The results also show that traverse speed is an essential factor in obtaining a higher material removal rate, demonstrating a direct proportional trend to MRR.  
Figure 6: Impacts of material thickness and traverse speed on Kerf upper angle in AWI profile cutting of AISODAL.  
Figure 7: Impacts of material thickness and traverse speed towards material removal rate in AWJ profile cutting of AISIO4L.
## 4 Discussion
### Limitations and Future Directions  
Obviously, there is much to be discovered before such an endeavor is, in practice, feasible. For example, it remains to be seen if hearing aids will have the computing power of modern PC machines to store features of a familiar voice. Even if such storage is possible, it remains likely a few years hence before real-time processing and voice conversion can be realized. Furthermore, there is the nagging unknown of whether people will be able to tolerate or accept a voice from a stranger that sounds similar and familiar to your partner or spouse, even if it does lead to easier listening and better performance. It is conceivable that this might be very strange for the listener. Nevertheless, the work outlined here was a necessary first step along this path.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 4 Testing and Analysis
### Receiver Signal Source: Long-Wave Signal from Analogue Source
#### 4.1.3 Accuracy of 1 PPS Measurement  
The analogue source generates 8390M station timing signals, with the time differences between broadcasting and transmitting during the test being recorded as TD1 = 14,990.1 us and TD2 = 33,428.1 us. The test equipment received these timing signals and, once they stabilised, a counter measured the 1 PPS output from the receiving equipment. The mean and standard deviation were calculated to evaluate the timing accuracy and stability. This test was repeated multiple times, and the resulting data are presented in Tables 2 and 3.  
The experimental data indicate that under a pure signal analogue-source output, the timing stability of Receiver 2 is approximately 60 ns or less, whereas that of Receiver 1 is \(\sim\)30 ns. This demonstrates that Receiver 1 has a higher tracking accuracy. Consequently, the tracking accuracies of foreign receivers surpassed those of domestic receivers, suggesting room for improvement.
## 4 Experiment  
### Simulation Environment and Parameters  
We developed a modular simulation environment to evaluate our proposed approach. The environment allowed for flexible setup of the supply chain network structure, specifying the number of factories and warehouses and their connectivity. Cost and reward coefficients for production, storage, penalty, and transportation could also be configured.  
For our experiments, we used a set of parameters based on optimal supply chain network design under uncertain demand. The key parameters included production cost (\(p=200\)), production capacity (kpr = 60), storage costs (kst,1 = kst,0 = 8), penalty cost (kpe = 40), transportation cost (ktr,1 = 80), and maximum demand (dmax = 200).