esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Concept for an Adaptive Clamping Device with Sensors and Actuators  
Figure 1a shows a schematic figure for the clamping strategy and the positions of the sensors for acquiring the sheet displacement as an index for gap size. Sheet 1 was fixed within the clamping device by clamping jaw 2 while sheet 2 was freely moveable in y-direction. This behavior was ensured by a gap between clamping jaw 6 and sheet 2 of 0.1 mm. The distance between both clamping jaws was set to 30 mm. The laser beam was moved between both clamping jaws carrying out the weld. Three inductive probes (Millimar P2004M) positioned along the specimen were used to determine the sheet displacement in y-direction representing the gap size at different positions. A resolution of 3 mm was reached within a range of 4 mm measuring length in total. The signals were amplified utilizing carrier frequency amplifiers (Dewetron DAQP-CFB2). The probes were set to zero before welding started. It should be noted that the probes acted on the sheet with a measuring force of 0.75 N \(\pm\) 0.15 N [29].  
The resulting forces were determined by temperature-compensated load cells type K-12 from Lorenz Messtechnik (LC1-2) for measuring tensile and compression forces with a maximum value of \(\pm\)1000 N at an accuracy class of 0.25% (Figure 1b). The signal was amplified via LCV measuring amplifier from Lorenz Messtechnik. Both load cells were mounted to linear actuators L59-A with trapezoidal spindle and linear guide from Nanotec Electronic (LA1-2). The linear motor provided a force of up to \(\pm\)1000 N at a maximum speed of 0.08 m/s and a resolution of 10 mm/step. The linear unit could extend to a stroke length of 25 mm. The linear motor had the advantage over conventional drives that a specified angle of the rotor could be approached and held in a targeted manner. The minimum step width of the rotor is 7.2\({}^{\circ}\). The self-retaining spindle prevented the system to be moved in case of an external force, i.e., only the linear motor was able to change the position of the linear actuator. C5E-EtherCAT from Nanotec was used as motor controller  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value and Dimension** \\ \hline Focal diameter & \(d_{f}\) & 280 μm \\ Wavelength & \(\lambda\) & 1030 nm \\ Angle of incidence & - & 0\({}^{\circ}\) (perpendicular to surface) \\ Welding speed & \(v_{w}\) & 1 m/min & 5 m/min \\ Resulting welding time for 300 mm weld seam length & - & 18 s & 3.6 s \\ Laser beam power & \(P_{L}\) & 400 W & 1000 W \\ Energy per unit length & \(E\) & 24 kJ/m & 12 kJ/m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Laser welding parameters.  
The actuators LA1-2 and therefore both load cells LC1-2 were positioned at begin and end of the sheet outside the inductive probes.
# Seismic Multi-Parameter Full-Waveform Inversion Based on Rock Physical Constraints
## 2 Materials and Methods
### Construction of Rock Physical Prior Information
#### 2.1.1 Wave Velocity and Lithology  
Seismic exploration serves as a pivotal geophysical method for characterizing subsurface geological architectures through the analysis of seismic wave propagation dynamics in underground strata. The development of underground media is inherently regulated by sedimentary environments, including the natural geographical conditions, climatic conditions, structural conditions, physical conditions of sedimentary media, and geochemical conditions of media [18]. As a result, in a given sedimentary setting, the strata have certain similarities, which provides a theoretical basis for the petrophysical constraint based on the prior information of lithology [19]. Prior lithological information can be acquired through multiple phases in seismic exploration, such as drilling, coring, and laboratory determination.
# Business Impact Analysis of AMM Data: A Case Study
## 4 Business Impact Analysis of AMM Datasets
### CIA Triad Security Classification  
Before the BIA itself is carried out, it is necessary to carry out a classification and establish safety criteria. These criteria are determined according to the degree of severity of the impact on the operation of the organization. The main purpose is to make the subsequent design and associated measures clearer and simpler. The readings from the measuring equipment, which must be protected from misuse, will be entered as a data set. The security aspects are mapped to the CIA triad, and we use the A, B, C, and D grades to determine the importance of the datasets. The specific parameters are given in Tables 3-5.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class** & **Security** & **Description** \\
**Availability** & **Classification** & **Description** \\ \hline A & [*; A; *] & The information assets are very confidential and if they were to be disclosed, this could have fatal consequences for the DSO and possibly the functioning of the distribution system. \\ B & [*; B; *] & Information assets are confidential, and their protection is mandated by law, the Civil Code, or GDPR. \\ C & [*; C; *] & Information assets are not public, and their disclosure would violate unbundling. \\ D & [*; D; *] & Information assets may only be disclosed under certain conditions set by law or the DSO’s internal regulations. \\ \hline \end{tabular}
\end{table}
Table 4: Confidentiality criteria.  
\begin{table}
\begin{tabular}{c c c} \hline
**Class** & **Security** & **Description** \\
**Availability** & **Classification** & **Description** \\ \hline A & [A; *; *] & Disruption of the availability of the information asset system is not acceptable, and even short-term unavailability must be addressed as soon as possible. Recovery should be within minutes; otherwise, the interests of the distribution system operator (DSO) may be critically compromised. \\ B & [B; *; *] & The unavailability of the information asset system must be restored within hours; otherwise, the interests of the DSO may be seriously compromised. \\ C & [C; *; *] & The unavailability of a system information asset should not exceed a period of one day. A longer outage may result in a partial compromise of the DSO’s interests. \\ D & [D; *; *] & Data unavailability affects the interests of the DSO to an insignificant extent. \\ \hline \end{tabular}
\end{table}
Table 3: Availability criteria.
## 3 Results  
### Spectrum Analysis  
The Lomb-Scargle periodogram method was used to perform spectral analysis of the time series of the Sichuan reference stations and baselines separately. Figure 3 shows the results of the time series spectral analysis of some stations (SCMN and SCXC) and baselines (SCML-SCMN and SCJL-SCXC).  
Figure 3: Results of the Lomb–Scargle periodogram method for analyzing the time-frequency transformation of time series. (**a**,**b**) represent the spectral analysis results of the reference stations SCMN and SCXC, respectively. (**c**,**d**) represent the spectral analysis results of baseline SCML-SCMN and SCJL-SCXC, respectively.  
Figure 2: Flow chart of the analysis of the spatiotemporal characteristics of reference frame.  
Both stations show a periodicity with a frequency of one year and half a year (weaker). However, station SCMN does not show a clear peak in the E direction with a period of one year. The effect of longer periodicity (about 2.7 or 5 years) is present in the figure, but the length of the time series of the reference station in the Sichuan area of this study is 7 years, so the error is probably due to the sampling interval of the LSP method. In addition, the two stations also show an insignificant periodicity of about 250 and 270 days in the N direction due to the different environments in which the stations are located. Affected by plate motion, ground subsidence and changes in the surrounding environment, the stations may show insignificant periodic movements with the stacking of multiple factors.  
The baseline SCML-SCMN and SCJL-SCXC time series in all four components of N, E, U and L show obvious annual periodicity; the periodicity of the E and L components with half-year frequency is not obvious, which may be due to the baseline weakening the spatial correlation among the reference stations and makes the spatially correlated CME change.
## 1 Background
In the ideal picture, the cluster of human summarizers at the top of each plot would be further to the right and directly along the prediction line that ROUGE fits with the machine summarizers. Figure 1: TAC 2008/2011 task results. To illustrate the strengths and weaknesses of ROUGE as a measure of a summarization system's performance, we give two scatter plots in Figure 1. In these plots, ordered pairs of each system's average ROUGE scores and average \"overall responsiveness\" scores are given for each automatic summarization system as well as for 8 human summarizers. The plots illustrate two of the best ROUGE scoring approaches, ROUGE-2 which measures the bigram similarity of a summary against human-generated summaries and ROUGE-SU4 which measures bigram similarity but allows for a skip distance of up to 4 words. Both plots show 2 major groups of data which, not surprisingly, correspond to the machine-generated and human-generated summaries. In 2008, there was a wide gap in the performance in average overall responsiveness (the human judgment) while the best systems scored in the lower range of human performance for both ROUGE metrics. Three years later, we see that the best machine-generated summaries have made improvement in both ROUGE and responsiveness, so much so that some systems now exceed the performance of humans in the ROUGE metric. However, their performance in responsiveness pales in comparison with that of humans. This inability of ROUGE to adequately predict human judgments of summaries gave rise to a \"meta-evaluation\", an evaluation of evaluation methods. The task is called AESOP or Automatically Evaluating Summaries of Peers and has been part of TAC for the last two years. In this paper, we propose using both content-oriented features, similar in spirit to ROUGE, in conjunction with low-level linguistic features to produce a metric that correlates well between human-generated summaries and those produced by machine-generated summarization systems. The features were combined based on training on previous years of TAC data using linear algebraic and statistical methods. They were then used to predict scores for the 2011 data prior to their release. The resulting metrics more accurately predicted the current performance gap between human and machine generated summaries than ROUGE. In addition, we have applied our methods to the 2008 TAC data to further validate their ability to predict the human-machine performance gap. The rest of this paper is organized as follows: Section 2 covers related work in text summarization evaluation. We discuss the origin of our data in Section 3. In Section 4, we define the linguistic and content features that go into our supervised learning algorithms and in Section 5, we discuss how those algorithms select subsets of the features. Section 6 describes our results and Section 7 contains our conclusions and ideas for future work.
# A New Life Prediction Scheme for Mechanical System with Considering the Mission Profile Switching
## 4 Results and Discussion
### The Life Prediction Under Mission Profile Switching  
In order to show the calculation process of the proposed method more clearly, lifecycle data in set #2 of the turbofan engine simulation data from NASA repository are selected to be displayed as a the sample to be predicted. The selected life-cycle data are shown in Figure 6a and the life of the data is 340 cycle. The selected life-cycle data are divided into two parts: The first 2/3 is used as the sample to be predicted, as shown in Figure 6b, and the latter 1/3 is used to validate the prediction result under the mission profile experienced by the latter 1/3 data, as shown in Figure 6c. The remaining samples in set #2 are used as the historical sample. Then, the results of the two main steps of the proposed method are shown below.
# Could We Realize the Fully Flexible System by Real-Time Computing with Thin-Film Transistors? +
## 2 Materials and Methods
### Fault-Tolerant Image Processing Algorithm
#### 2.2.1 Pre-Processing Algorithms  
Pre-processing algorithms are very important step in image processing including edge enhancing, features extracting, etc. Among them, the Difference-of-Gaussian (DoG) [11] is a widely used algorithm to extract and enhance edges. Actually, the human retina can be considered as a retina kernel filter whose model based on the DoG, which, thus, is an appropriate method that is applied to novel flexible computing.  
The DoG is calculated by decreasing the two Gaussian functions with different scales. Assume that the input image is \(l(x,y)\), the Gaussian kernel function is \(G(x,y,\sigma)\) and output image processed by Gaussian convolution noted as M\((x,y,\sigma)\), where x and y are horizontal and vertical coordinates in pixels space, and \(\sigma\) is the coordinate in scale space. Then, we have:  
\[M(x,y,\sigma)\ =\ I(x,y)*G(x,y,\sigma) \tag{1}\]  
\[G(x,y,\sigma)\ =\ \frac{1}{2\pi\sigma^{2}}\mathrm{e}^{-\frac{x^{2}+y^{2}}{2 \sigma^{2}}} \tag{2}\]  
The difference of one image filtered by \(\sigma 1\) and \(\sigma 2\) is:  
\[D(x,y,\sigma 1,\sigma 2)\ =\ M(x,y,\sigma 1)-M(x,y,\sigma 2) \tag{3}\]  
The feature descriptors here are generated from local statistical information, in which the exact original data does not make much sense. Thus, noise or fault in the appropriate range can be ignored. Above all, Gaussian convolution is a most common and critical operation in the DoG. It has been demonstrated by [PERSON] that the only possible scale-space kernel is the Gaussian function among a variety of reasonable assumptions [11; 21]. [PERSON] et al. [22] proposed a Gilbert Gaussian circuit, whose I-V characteristic is very close to Gaussian function. Based on this circuit, we construct an architecture to implement Gaussian convolution in the analog domain, and it is chosen to demonstrate the computation potential of Oxide TFT technology.
###### Abstract  
The rapid development of information communication and artificial intelligence (AI) technology is driving innovation in various new application fields such as autonomous driving, augmented reality, and the metaverse. In particular, the advancement of wireless localization technology plays a great role in these cutting-edge technologies. However, traditional wireless localization systems rely on the global navigation satellite system (GNSS), which is ineffective in indoor or underground environments. To overcome this issue, indoor positioning systems (IPS) have gained attention, and various localization techniques utilizing wireless communication were studied. Subsequently, AI technologies are improving the performance of wireless localization and addressing problems that were previously difficult to solve. In this paper, we summarize wireless localization techniques and define the factors that impede their performance. Furthermore, we categorize AI algorithms and present examples of how they can be used to address these hindering factors. Finally, we propose open research directions and prospects for AI-assisted wireless localization.  
wireless communication; wireless localization; artificial intelligence +
Footnote †: journal: Applied sciences
## 3 Results and Discussions
The effect of variation in oxygen partial pressure to the conductivity values is presented in Figure 3b. As we can see, the electrical conductivity is mixed hole ionic at dry oxidizing conditions. The share of oxygen ionic transport does not change at the temperature variation, and it is around 25%, which is comparable with the value (20%) for undoped BaLaInO4 composition [56]. We can suggest that the dopant concentration of 0.1 mol is not enough to have a meaningful impact on the conductivity nature. At the same time, the nature of dopant is also a possible reason for the absence of significant changes in the nature of conductivity. However, a significant increase in the conductivity values (\(\sim\)1.5 orders of magnitude) during doping is observed (Figure 3c,d). The most probable reason for the increase in the mobility of oxygen ions is due to the increase in the size of the crystal lattice and space for ionic transport. It should be noted that the conductivity values from the electrolytic area (oxygen ionic conductivity, \(\mathrm{pO_{2}<10^{-5}}\)) do correlate well with the conductivity values obtained at the Ar atmosphere. This allows us to consider the values obtained in argon as ionic conductivity values. Figure 2: The TG, DSC and MS results for the composition BaLa\({}_{0.9}\)Pr\({}_{0.1}\)InO\({}_{4}\). The humidity of the atmosphere affects the conductivity values below 600 \({}^{\circ}\)C. The proton concentration increases with temperature decreases. This is because of the increase of the conductivity under wet conditions in comparison with dry conditions (Figure 3b,c). The proton conductivity was calculated as the difference between conductivity values obtained under _wet Ar_ and _dry Ar_:  
\[\sigma_{H^{+}}=\sigma_{wet\ Ar}-\sigma_{dry\ Ar}=\sigma_{wet}^{ion}-\sigma_{ dry}^{ion} \tag{2}\]  
Figure 3: The Nyquist plots for the composition BaLa\({}_{0.9}\)Pr\({}_{0.1}\)InO\({}_{4}\) obtained under dry and wet air at 400 \({}^{\circ}\)C (**a**), the dependencies \(\sigma-\)pO\({}_{2}\) for the composition BaLa\({}_{0.9}\)Pr\({}_{0.1}\)InO\({}_{4}\) (violet symbols) at dry (closed symbols) and wet (open symbols) atmospheres (**b**), the dependencies \(\sigma-\)1000/T for the compositions BaLa\({}_{0.9}\)Pr\({}_{0.1}\)InO\({}_{4}\) (**c**) and BaLaInO\({}_{4}\) [56] (**d**). and its temperature dependencies are shown in Figure 4a. As we can see, the protonic conductivity for the Pr-doped composition is higher than the undoped sample by \(\sim\)1.5 orders of magnitude. This increase is due to the increase in both the proton concentration and proton mobility (Figure 4b). The proton conductivity value for doped BaLa\({}_{0.9}\)Pr\({}_{0.1}\)InO\({}_{4}\) composition is 5.0 \(\times\) 10\({}^{-6}\) S/cm (T = 400 \({}^{\circ}\)C) in comparison with 2.7 \(\times\)10\({}^{-7}\) S/cm for BaLaInO\({}_{4}\) composition that is 18 times greater. It can be suggested that the change in the dopant concentration and dopant nature can lead to significant changes in the nature of electrical conductivity.
## 3 Numerical Analysis of the Thermal Performance
### Measure Point Temperature
#### 3.6.2 Temperature Field on the Interface of Insert and Tool-Holder  
Figure 11 shows the temperature distribution of the interface of insert and tool-holder, where different colors represented the temperature distribution in the CFX simulation process.  
Figure 10: Temperature solving algorithm.  
The temperature distribution on the interface between the insert and the tool-holder can be expressed as:  
\[\left\{\begin{array}{l}y=P_{1}\left(x^{2}+y^{2}\right)^{\frac{3}{2}}+P_{2}\left( x^{2}+y^{2}\right)+P_{3}\left(x^{2}+y^{2}\right)^{\frac{1}{2}}+t_{\max}\\ P_{1}=0.0001t_{\max}-0.0004767e^{-11.62a_{P}}-0.002207e^{0.1603a_{p}}\\ P_{2}=\left(-0.0005202e^{-5.553a_{p}}-0.00212e^{0.3082a_{p}}\right)t_{\max}+0.03 066e^{-19.73a_{p}}+0.0449e^{0.244a_{p}}\\ P_{3}=\left(-0.02449e^{-0.2466a_{p}}+0.02115e^{-23.68a_{p}}\right)t_{\max}+0.4889 e^{-0.2458a_{p}}-0.7004e^{-28.88a_{p}}\end{array}\right. \tag{17}\]  
where \(t_{\max}\) is the maximal temperature of the interface. Substituted the coordinate of measure point in Equation (17) the measure point temperature can be easy to acquire.  
Based on the calculated temperature field on the tool-chip interface and the mathematical deductions illustrated above, temperatures of the three measure points can be acquired.
# Random Access Performance of Distributed Sensors Attacked by Unknown Jammers
## 3 RA Performance Analysis  
### Success Probability of Preamble Transmission  
#### 3.1.1 Success Probability of \(r\)-th Preamble Retransmission  
Given that \(n_{p}\), \(n_{m}\), \(k\) and \(n_{J}\), let us denote the conditional success probability at the \(r\)-th preamble retransmission (i.e., at the \((r+1)\)-th PT) by \(u_{\bullet}(r|n_{p},k,n_{m},n_{J})\), where \(\bullet\in\{P,S\}\) and furthermore \(P\) for power jamming and \(S\) for code jamming. The probability can be obtained analogously by the method used in [22] according to the criteria (4) and (5), respectively.  
\[u_{\bullet}(r|n_{p},k,n_{m},n_{J})=\begin{cases}\frac{\exp\left(-\frac{\beta p _{c}}{\mu}\right)}{\left(1+\beta_{p}\right)^{p}\left(b-1\right)!}\sum_{i=0}^{ q-1}\sum_{j=0}^{i}\frac{(b+j-1)!}{j!(i-j)!}\frac{\beta_{p}^{i}}{(1+\beta_{p})^{ j}}\left(\frac{c}{\mu}\right)^{i-j},1\leq k\leq n_{p}-1,\\ \exp\left(-\frac{\beta p_{c}}{\mu}\right)\sum_{i=0}^{a-1}\frac{1}{\bar{\eta}} \left(\frac{\beta p_{c}}{\mu}\right)^{i},\,k=n_{p},\end{cases} \tag{10}\]where the three interim variables \(a\), \(b\) and \(c\) for the power jamming (i.e., \(\bullet=P\)) are defined as:  
\[\begin{cases}a=\lfloor(k-1)\,\overline{m}L+m_{r}L\rfloor,\\ b=\lfloor\left(n_{p}-k\right)\,\overline{m}L+n_{m}\alpha\overline{m}L+n_{J}m_{J }L\rfloor,\\ c=\lfloor\eta\rfloor,\end{cases} \tag{11}\]  
and for the code jamming (i.e., \(\bullet=C\)):  
\[\begin{cases}a=\lfloor(k-1)\,\overline{m}L+m_{r}L+m_{J}L\rfloor,\\ b=\lfloor\left(n_{p}-k\right)\,\overline{m}L+n_{m}\alpha\overline{m}L+(n_{J}-1 )m_{J}L\rfloor,\\ c=\lfloor\eta\rfloor.\end{cases} \tag{12}\]  
Aggregating \(u_{\bullet}(r|n_{p},k,n_{m},n_{J})\) for all possible values on \(n_{p}\) and \(k\) gives:  
\[u_{\bullet}(r|n_{m},n_{J})=\sum_{n_{p}=1}^{\infty}\sum_{k=1}^{n_{p}}\Pr\{K=k| N=n_{p}\}\Pr\{N=n_{p}\}\bar{u}_{\bullet}(r|n_{p},k,n_{m},n_{J}), \tag{13}\]  
where an interim function \(\bar{u}_{\bullet}(r|n_{p},k,n_{m},n_{J})\) represents either \(\bar{u}_{P}(r|n_{p},k,n_{m},n_{J})=u_{P}(r|n_{p},k,n_{m},n_{J})\) for power jamming or:  
\[\bar{u}_{C}(r|n_{p},k,n_{m},n_{J})=\underbrace{\frac{n_{J}}{s}u_{C}(r|n_{p},k, n_{m},n_{J})}_{u_{C,1}}+\underbrace{\frac{s-n_{J}}{s}u_{P}(r|n_{p},k,n_{m},n_{J })}_{u_{C,2}}, \tag{14}\]  
for code jamming. Furthermore, \(u_{C,1}\) and \(u_{C,2}\) in (14) represent whether SN-A selects the code being jammed by a code jammer or not, respectively. Hereafter, we use \(u_{\bullet}(r)\stackrel{{\rm(def)}}{{=}}u_{\bullet}(r|n_{m},n_{J})\) for notational brevity.
## 2 Data and Method
### Data
#### 2.2.2 Landsat Imagery  
We used Landsat TM and ETM\(+\) data to make a comparison with GIMMS NDVI due to the relatively higher spatial and spectral resolution of the sensors. The spatial resolution of 30 m was adequate to respond within stand changes, and the scene extent was large enough to cover the whole burned on two images. Moreover, since Landsat TM data are available for such an old fire event occurred on 1987. The Landsat TM and ETM\(+\) data used in this study are list in Table1.  
Figure 1: Location of the study area.