esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Reactive Protection Concept for Industrial Automation Systems  
This section presents a concept of reactive protection for IAS from which a Reactive Protection System is derived. This concept addresses the requirements discussed in Section 3. Special attention is given to its suitability for generic IAS architectures (i.e., Figure 1) and its consideration of concepts presented in the ISA/IEC 62443 series of Standards.  
First, the scope of protection coverage provided by the Reactive Protection System is presented. This includes pre-requirements and expected protection capabilities. Afterwards, the components that constitute the Reactive Protection System are presented. Finally, a deeper discussion is given regarding the security and operational policies, as well as reactive actions that can be considered by the presented concept taking into account well-known architectural and operational trends of real automation systems.  
\begin{table}
\begin{tabular}{l c c c c l} \hline \hline
**Approaches** & **R1** & **R2** & **R3** & **R4** & **Active Response** \\ \hline [PERSON] et al. [13] & + & + & o & + & Packet filtering for access control \\ \hline [PERSON] et al. [34] & + & + & o & + & Block incoming connections or drop network traffic \\ \hline Security Matters [35] & + & ++ & o & + & Dynamically add Firewall rules \\ \hline [PERSON] et al. [36] & o & - & o & o & Packet Filtering and Redirection of Attacks towards a Honeynet \\ \hline [PERSON] et al. [37] & + & o & o & ++ & Drop network traffic \\ \hline [PERSON] et al. [39] & + & - & ++ & o & Drop malicious commands, terminate physical processes, replace or isolate compromised devices and one time authentication \\ \hline [PERSON] et al. [40] & o & - & o & o & Cut energy to sensor, discard data from malicious node, update routing tables \\ \hline [PERSON] et al. [41] & + & - & ++ & - & Dynamic reconfiguration of system components \\ \hline CockpitCI [42] & o & ++ & ++ & o & Restart of a component \\ \hline \hline \end{tabular} ++: fulfilled, **+**: partially fulfilled, **-**: not fulfilled, **o**: unclear, **R1**: Configurability and automatic or semi-automatic reactions to intrusions, **R2**: Compliance with the ISA/IEC 62443 series of Standards, **R3**: Ensure correct operation of the underlying automation system, **R4**: Multi-platform support and interoperability with preexisting solutions.  
\end{table}
Table 1: Overview of related approaches for reactive responses in Industrial Automation Systems (IAS) and ratings using Requirements for reactive protection in IAS.
## 2 Materials and Methods
### Mechanical Testing
#### 2.2.1 Ring Tensile Testing  
A materials testing device (ZwickRoell, Ulm, Germany) with a 200 N load cell was fitted with horizontally oriented pins and used to conduct tensile tests on rings of porcine aorta. Prior to testing, rings were imaged against graph paper to allow measurement of wall thickness and circumference optically using ImageJ (version 1.53r) analysis software [47]. The thickness of each ring was measured perpendicular to tangent lines constructed at the inner mural border at four equidistant locations on each sample. The circumferential length was measured as the circumference of an ellipse fit to the outer mural border for each sample. The pins used were 1 mm in diameter and all tests were performed within a PBS bath kept at a temperature of 37 \({}^{\circ}\)C. To begin testing, rings were loaded to hang freely from the top pin without contacting the lower pin and the force was set to zero. The test protocol involved preloading rings to 0.05 N at 10 mm/min and five preconditioning cycles to 10% strain at a rate of 60% the initial gauge length (L\({}_{0}\)) per minute. Data was collected from the end of the fifth preconditioning cycle, when rings were stretched from 0% strain to failure at a fixed strain rate of 40% L\({}_{0}\)/min. After failure, force-displacement and gauge length data were exported and the rings were fixed for histological analysis.  
Force-displacement data was converted to Cauchy and Green-Lagrange definitions of stress and strain, respectively, using equations published by [PERSON] et al. [48]. First, the circumferential stretch was calculated as [48]:  
\[\lambda_{1}=\frac{l+\pi r_{w}}{l_{0}+\pi r_{w}} \tag{1}\]  
where \(\lambda\) is the calculated stretch factor, \(r_{w}\) is the pin radius, and \(l\) and \(l_{0}\) represent the distance between the center of each pin in the deformed and undeformed configurations, respectively; subscript 1 denotes the circumferential axis of loading. Green strain \(E\) was calculated as [48]:  
\[E_{11}=\frac{1}{2}\Big{(}\lambda_{1}^{2}-1\Big{)} \tag{2}\]  
Figure 1: Developed ex vivo aortic treatment apparatus. (**a**) 3D printed apparatus, (**b**) measurement of localised 1 cm treatment region, (**c**) ‘gripped’ aorta for treatment, and (**d**) placement of aorta in 3D printed treatment apparatus. All scale bars are 5 cm.  
Cauchy stress was calculated on the assumptions of plane stress and incompressibility [48]:  
\[\sigma_{1}=\frac{F_{1}\lambda_{1}}{2 tw} \tag{3}\]  
where \(F\) is the force, \(t\) the thickness and \(w\) the width. Cauchy stress and Green strain data were plotted as stress-strain curves, from which the elastic moduli were calculated over initial and final regions as described by [PERSON] et al. [49]. Initial and final moduli were calculated as the slope of linear regressions plotted from 0-0.05 Green strain and 0.25-0.3 MPa Cauchy stress, respectively.
## 3 RS Applications in Ocean
### Ocean Surface Wind (OSW)
#### 3.1.4 Scatterometer  
Scatterometers have proven to be efficient tools for both OSW speed and direction measurements [101; 102]. A scatterometer transmits a radar pulse and then measures the returned pulse ratio from the ocean surface [103]. The differential response and the intensity of the reflected pulses determine the OSW direction and speed, respectively [82]. The main operating radar frequencies in scatterometers are Ku-band (frequency = 13.4 GHz and wavelength = 5 cm) and C-band (frequency = 5.2 GHz and wavelength = 2 cm). Scatterometers provide global OSW vector information with spatial resolutions between 25 and 50 km, which is almost similar to that of microwave radiometers [79]. Unlike SAR systems, the low spatial resolution of these two instruments cannot effectively satisfy several local applications, such as tropical cyclone analysis and coastal OSW mapping. For low OSW speeds, scatterometers provide more reliable information compared to microwave radiometers. However, scatterometers lose sensitivity, and the signal begins to saturate, at high wind speeds (above 35 m/s) [104]. Like SAR systems, the rain contamination in scatterometers' OSW measurements is more serious in the higher frequencies.  
Many studies have so far investigated the potential of different scatterometers for OSW estimation. For example, [PERSON] et al. [103] examined the spatial variability of global OSW resources at heights of 10 m and 100 m above Sea Level (SL) using multiple scatterometers, including Quick SCATerometer (QuikSCAT), ASCAT, and WindSat (see Figure 2). The authors also compared the results with mean OSW speeds and wind power densities collected by buoys during 1995-2015. Based on the results, although OSW information retrieved from each of these three scatterometers was fairly comparable, a combination of these instruments provided better results. Furthermore, other types of sensors, including terrestrial laser scanners, SONAR, and Autonomous Underwater Vehicles (AUV), have been utilized to collect data about the position, geometry, and morphology of icebergs [26; 27]. Although the abovementioned approaches provide accurate information, they are resource-intensive and logistically arduous in oceans, especially in remote locations of polar regions [28]. Consequently, it is efficient to employ other RS systems, such as satellites, which can provide broad observations about the icebergs through space and time. Various RS systems have been so far applied to identify and track icebergs [10; 29; 31]. Optical, SAR, scatterometer, altimeter, and HF radar systems have been widely used for iceberg studies.
# Centrifuge Modelling of the Impact of Excavation with Partition Piles on Adjacent Existing Tunnel
###### Abstract  
Recently, due to more excavation projects near existing tunnels, the research on tunnel deformation control has become very important and urgent. However, the systematic study of the control effectiveness of partition piles, one of the commonly used methods for controlling tunnel deformation, was not mature yet. In response to this situation, a series of centrifuge modelling studies were performed to explore how nearby existing tunnels respond to excavations with partition piles in a dry sand foundation. The tests provided insights into the variations of the horizontal displacement of retaining walls, surface settlement, the horizontal and vertical displacements of the tunnel, and the circular induced strain of the tunnel. The test results showed that the horizontal displacement of retaining walls exhibited a cantilever-type displacement pattern. The most significant surface settlement occurred near the retaining wall and decreased as the distance from the retaining wall increased. The overall deformation of the tunnel roughly showed an ellipsoidal deformation pattern in the horizontal direction. The maximum horizontal displacement was observed at the right shoulder of the tunnel, while the maximum vertical displacement occurred at the right arch of the tunnel. Increasing the length of the partition piles led to a deterioration in their deformation control effectiveness. The top burial depth of the pile could further improve the control effectiveness of the partition piles. However, when the top burial depth of the pile was excessive and exceeded its critical value, the improvement effect of the burial depth on the partition piles diminished.  
Keywords: excavation; centrifuge modelling; partition piles; tunnel deformation control +
Footnote †: journal: Applied
## 2 The Mathematical Modeling of OPF Objective Function
### Constraints
#### 2.2.1 Equality Constraints  
In OPF, equality constraints are expressed as non-linear load flow equations that state that total generated power equals total load power and power losses, as indicated in Equations (4) and (5).  
\[PG_{i}-PD_{i}-V_{i}\sum_{j=1}^{NB}V_{j}\big{(}G_{ij}\text{cos}\,\theta_{ij}+B _{ij}\text{sin}\,\theta_{ij}\big{)}=0 \tag{6}\]  
\[QG_{i}-QD_{i}-V_{i}\sum_{j=1}^{NB}V_{j}\big{(}G_{ij}\text{sin}\,\theta_{ij}-B _{ij}\text{cos}\,\theta_{ij}\big{)}=0 \tag{7}\]  
where \(PG_{i}\) is the active power generation, \(QG_{i}\) is the reactive power generation, \(PD_{i}\) is the active load demand, \(QD_{i}\) is the reactive load demand, \(NB\) is the number of busses, \(\theta_{ij}\) is the voltage angle between busses \(i\) and \(j\), and \(G_{ij}\) and \(B_{ij}\) are the real and imaginary terms of the bus admittance matrix, illustrated in Equation (8), corresponding to the \(i\)th row and \(j\)th column, respectively.  
\[\begin{bmatrix}I_{1}\\ I_{2}\\ \vdots\\ I_{i}\end{bmatrix}=\begin{bmatrix}Y_{11}&Y_{12}&\cdots&Y_{1j}\\ Y_{21}&Y_{22}&\cdots&Y_{2j}\\ \vdots&\vdots&\ddots&\vdots\\ Y_{11}&Y_{11}&\cdots&Y_{ij}\end{bmatrix}\begin{bmatrix}V_{1}\\ V_{2}\\ \vdots\\ V_{i}\end{bmatrix} \tag{8}\]  
where \(I_{i}\), \(V_{i}\) are the current and the voltage of bus \(i\), respectively, while \(Y_{ij}\) is the admittance element of the ith row and jth column.
## 4 Discussion
### First Stage of Amplification  
Special zero drift amplifiers were used to amplify the output signal from the shunt. This block is a differential-input to differential-output stage. Because the current flow of 1 A on the shunt is 125 \(\upmu\)O, it generates a voltage of 125 mV. Therefore, the amplifier should have a much lower offset voltage \(V_{os}\). The settling time of the amplifier should be small, t\({}_{\text{s}}\) < 2 \(\upmu\)s, which influences the correct operation of the AGC loop gain.  
The common mode rejection ratio (CMRR) of this structure depends on the gain factor (10 V/V) and the isolation amplifier improves signal attenuation. Common voltages appear in circuits with power inverters loaded with electric motors. Therefore, the measuring circuit should be considered accordingly. This means that a precise reference voltage should be used.
## 4 Single-Subchannel Resource Optimization  
This section investigates the resource allocation problem on resource block \(s\in S\) with the upper limit of power budget \(p^{s}\). As a fundamental module for multi-resource-block joint resource allocation solutions, our improved method contributes to designing more efficient optimization algorithms. The following equation can describe the single-resource-block optimization issue:  
\[W_{RB}^{s}:W_{s}(p^{s})=max\sum\limits_{n=1}^{T}\varphi_{n}^{s}(\widetilde{p}_ {n}^{s})+C^{s}. \tag{20}\]  
\[w1^{*},w4^{*},w5^{*}:0\leq\widetilde{p}_{T}^{s}\leq\widetilde{p}_{T-1}^{s}\leq \ldots\leq\widetilde{p}_{1}^{s}\leq P_{max}^{s}; \tag{21}\]  
\[w3^{*}:|A_{s}*|\leq A. \tag{22}\]  
On a single resource block, the constant term of the objective function is  
\[C^{s}=log_{2}(\frac{1}{\widetilde{\varphi}_{\beta_{s}(T)}^{s}})^{\alpha_{\beta _{s}(T)}}. \tag{23}\]
# Atomistic Investigation of Plastic Deformation and Dislocation Motion in Uranium Mononitride
###### Abstract  
Uranium mononitride (UN) is a promising advanced nuclear fuel due to its high thermal conductivity and high fissile density. However, many aspects of its mechanical behavior, particularly at reactor-relevant conditions, remain unclear. In this study, molecular dynamics (MD) simulations were employed to investigate the deformation behavior and dislocation motion in UN. We found that the Kocevski potential predicts the principal slip system as \(\frac{1}{2}\langle 110\rangle\langle 110\rangle\), aligning with experimental data. On the other hand, the Tseplyaev potential predicts slip to primarily occur on \(\frac{1}{2}\langle 110\rangle\langle 111\rangle\). MD simulations of stress-strain behavior were used to estimate the nanoindentation hardness, revealing that the Kocevski potential accurately predicts hardness even though it fails to model dynamic plasticity. Complete dislocation mobility functions have been fitted for the edge and screw dislocations in both the thermally activated and phonon-drag regimes. The 300 K linear mobility of the edge dislocation using the Tseplyaev potential was found to be 817 Pa\({}^{-1}\cdot\)s\({}^{-1}\), whereas that of the screw dislocation using the Kocevski potential was found to be 4546 Pa\({}^{-1}\cdot\)s\({}^{-1}\). At intermediate stresses, we observed that the subsonic steady-state motion of the edge dislocation in UN is intermittently interrupted by velocity jumps, reaching the average sound velocity. Finally, the threshold Schmid stress is calculated as 179-197 MPa, which gives an upper-limit estimate of the uniaxial yield stress of polycrystalline UN of 548-603 MPa. These findings, including the fitted dislocation mobility function, provide essential input for future plasticity and dislocation dynamics models of nuclear fuels.  
u +
Footnote †: journal: Applied Sciences
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 4 Method to Identify Activity Executions
### Visualize All Activities and Find Repeated Sequences  
After labeling the given IoT data set with the start and end of identified activities based on the similarity search of activity signatures, it may also be possible to discover entire processes based on the appearance of repeated activity sequences.  
Figure 17 shows the visualization of all detected activities in the given IoT data set. From a visual analysis of similarities and repetitions across activities, we are able to identify two different types of three repeated sequences of activity executions. These repeated sequences may be candidates for executed process instances, but can also be part of one or more loops executed within one or more process instances. In addition, the detected activities may belong to one or more process instances being executed in parallel. Moreover, in the example, we assume that the given IoT data set starts with the first activity of a new process instance, which may not always be the case and there is first a need for orientation, especially when moving into online data analysis settings [68]. Here we need to again rely on the process analyst and domain knowledge to label the processes.  
In Figure 17, we can see that the first three executions of process instances belong to the Storage Process (cf. Section 2.2.2) and the second three executions belong to the Production Process. This can be derived from the involved CPS components (cf. Table 3) and knowledge about the processes in the smart factory (cf. Section 2.2.2). Once executed processes were identified in the IoT data set, we can extend the definition of _activity signature  
Figure 16: Similar activities found for HBW_1 component.  
Figure 15: Similar activities found for VGR_1 component.  
(cf. Section 4.4.2) to the level of _process signature_ considering the time series of sensor and actuator values over the entire time of the process execution.  
To automate this step of finding potential processes in the labeled data set, the start and end of an activity can also be written as _events_ into a process _event log_, which can be used for process discovery (e.g., in the form of a process map) using traditional process mining techniques [3].
## 4 Analysis of Influence Factors
### Alignment Adjustment  
As analyzed above, in the field test and simulation calculation, the abnormal track alignment in the tangent between the reverse curve and the turnout causes severe wheel-rail impact before entering the switch. The adjustment amount of the alignment irregularities ahead of the turnout is eliminated, and the simulation results of the optimized alignment are compared with the results under basic conditions, presented in Figure 13.  
Figure 13: Dynamic characteristics of the train passing the tangent and switch under different track alignments. (**a**) Wheel–rail lateral force; (**b**) wheel–rail vertical force; (**c**) derailment coefficient; (**d**) wheel load reduction.  
In Figure 13, it can be seen that eliminating the alignment irregularity before the turnout significantly promotes the dynamic performance of the motor and the trailer passing the tangent. The lateral forces of the outer wheels of these two vehicles are reduced by about 80%, the derailment coefficients are reduced by about 70%, the vertical force reduced by about 10-20% and the wheel load is reduced by approximately 30%. After entering the switch zone, the dynamic performances of the motor have equal promotion as in the tangent. However, the dynamic performance of the trailer is rather limited, which is mainly due to the failure for the independently rotating wheelset to keep in the center position, thus even though the alignment irregularity has been eliminated, the outer leading wheel of the independently rotating wheel is still in flange contact with the outer rail before entering the switch, and with the evolution of the blade rail profile, the wheel-rail impact is still severe. It can be concluded that eliminating alignment irregularities before the switch can effectively improve the dynamic performance of the integrated wheelset in the switch. Due to keeping in flange contact with the outer rail entering the switch, the independent wheelset has a rather limited promotion in dynamic performance when passing the switch.
## 3 Experimental Analysis
### Decay Time  
From the time-frequency spectrum of reflection power, we can estimate the decay time of each frequency to predict the strong room modes in a real room environment. Figures 9 and 10 show the 60 dB decay time of each frequency estimated from the \(P_{T}(t,k|\mathbf{y}_{o})\) values for different source positions in Room-1 and Room-2, respectively. Even though the temporal response at each frequency in Figures 7 and 8 seems relatively independent of the source positions, the decay times of the frequencies is slightly different for each source position according to Figures 9 and 10. The average decay time, maximum decay time, and the corresponding frequency for each source position in both rooms are summarized under Table 1. We can say that the strongest modes in Room-1 are \(\approx\)140 Hz, \(\approx\)164 Hz, and \(\approx\)258 Hz, which are closer to the peak power frequencies observed in Figure 5. However, in Room-2, the frequencies with maximum decay time are different  
Figure 8: Reflection power with time for different frequencies and source positions in Room-2.  
Figure 7: Reflection power with time for different frequencies and source positions in Room-1.  
from the frequencies with maximum reflection power. Hence, we need a deeper insight into the directional variations of power and decay time which we will analyze in the next section.
## 2 The Proposed RDHEI Method
### Room Reservation and Image Encryption
#### 2.1.1 Pixel Prediction
This advantage primarily stems from the label map's requirement for far fewer bits to represent the same information compared to the sign bit plane and multiple bit planes. Figure 7: (**a**) Bit-planes of the mapped prediction error Lena image, (**b**) and their corresponding quadtree decompositions. (**c**) Bit-planes of the mapped prediction error Jetplane image, (**d**) and their corresponding quadtree decompositions. The bit planes start with the LSB to the MSB. The mapped prediction error images were obtained by using the GAP predictor. ```
function mappedPredictionError(\(x\)) Input: An Image \(x\) of size \(n\times m\). Output: A mapped prediction error image \(e\), and a Label map \(labelMap\). \(labelMap\): an array of size \(n\times m\) initialized with zeros. for\(u\gets 1\) to \(n\)do for\(v\gets 1\) to \(m\)do \(L\leftarrow listOfNeighbors(u,v)\) \(\hat{x}(u,v)\gets prediction(L)\) \(e(u,v)\gets x(u,v)-\hat{x}(u,v)\) if\(|e(u,v)|>127\)then \(e(u,v)\gets x(u,v)\) \(labelMap(u,v)\gets 1\) else \(SIGN\gets 0\) if\(e(u,v)<0\)then \(SIGN\gets 1\)  end if \(e(u,v)\leftarrow(|e(u,v)|<<1)|SIGN\) endif endfor endfor return\(labelMap,e\) endfunction
```  
**Algorithm 1** mappedPredictionError algorithm. It is worth mentioning that the article is supplemented with an Abbreviations part containing a table enumerating the symbols and functions utilized in the algorithms, providing clear explanations of their meanings to enhance reader comprehension.
## 3 Composite Control Method
At the same time, it reduces adjustment cycles, not only shortening the system's response time but also enhancing the closed-loop bandwidth. Additionally, it notably eliminates hysteresis effects, helping to minimize the steady-state error of the closed-loop system. These improvements collectively contribute to a substantial enhancement in the overall performance of the composite control method.
# Evaluation of Land Subsidence during Groundwater Recovery
## 2 Geological Conditions in Bangkok  
### Bangkok Soft Soil Profile  
The area of Bangkok soft soil is located along the Chao Phraya River basin, covering many provinces, including Bangkok and its urban area. In the past, this area was part of the sea. The soil was deposited in alternating clay and sand layers. The subsoil layers consist of very soft clay to soft clay, very stiff to stiff clay, first sand, hard clay, and second sand. The Department of Groundwater Resources (DRG), the Department of Public Works and Town & Country Planning, and many researchers have studied the characteristics of the subsoil layers in the Chao Phraya River basin area and found that the subsoil layers are variable. However, the overall soil layers still consist of alternating clay and sand layers.