esa-sceva/satcom-chunk-collection huggingface dataset:
# Attosecond Pulse Shaping by Multilayer Mirrors
## 5 Outlook
### Multilayer Mirrors for HHG Photon Energies up to the keV Spectral Range  
The previous section describes the way toward ever higher photon energies in HHG but neglects one important parameter: the high photon number needed for low-cross-section attosecond experiments. Consequently, the sources should offer a high photon number (\(>\)\(10^{10}\) photons) in the soft X-ray range, and multilayer mirrors being used for beam guiding, beam shaping, or beam focusing should ensure a high photon throughput. This will ensure high attosecond pulse intensities on the target to be investigated. The way toward higher photon energies combined with a high photon flux leaves only one direction for multilayer mirrors: toward grazing incidence optics. Grazing incidence optics are the key for photon energies above the water window (\(>\)\(543.1\,\mathrm{eV}=\mathrm{oxygen}\) 1s)--up to the keV range--for four distinct reasons:  
* High single interface reflectivity for grazing incidence.
* For grazing optics, in the case of keV: A photon energy of 1000 eV corresponds to a wavelength of \(\lambda=1.2398\) nm. This leads, in the case of normal incidence, to a \(\lambda/2\) period in a multilayer mirror. Assuming typical two-material systems, one ends up in a layer with a thickness of \(\sim\)0.3 nm, which is close to the limit of technical realization.
* One can minimize the influence of the roughness on the reflectivity performance: The lower the \(\sigma/d\)-ratio, the lower is the reflectivity loss. One thus can minimize the reflectivity loss by reducing the interface roughness \(\sigma\) or increase the period thickness \(d\). Keeping the central energy from a multilayer mirror constant leads, in the case of a higher period thickness, to a lower grazing incidence angle. This overlaps with the previous statement. Typical roughness values are in the range of 0.4-0.6 nm. Comparable thickness values are strongly discouraged.
* In the case of grazing incidence (compared with multilayer mirrors at normal incidence), only a few layers can contribute to the overall reflectivity, which results in a certain energy in a higher spectral bandwidth support. According to the relation between bandwidth and temporal pulse duration, \(\Delta E\left[\mathrm{eV}\right]\cdot\tau_{0}\left[\mathrm{as}\right]\approx 1 824\left[\mathrm{eV}\cdot\mathrm{as}\right]\), this presents the opportunity for shorter pulses.
## 3 Controller Design
### Stability Analysis  
Substituting (34) into \(\dot{V}_{3}\) yields  
\[\dot{V}_{3}=-c_{1}z_{1}^{2}-c_{2}z_{2}^{2}+z_{2}z_{3}+s\bigg{(}-\beta_{1} \Phi_{1}(\hat{s})-\beta_{2}\bigg{/}\Phi_{2}(\hat{s})\bigg{)}+\delta_{\text{max}} \tag{34}\]  
where \(\delta_{max}\) is the maximum observation error of the observer. Define  
\[\Omega=\begin{bmatrix}c_{1}+\beta_{1}\mu_{2}k_{1}^{2}&\beta_{1}\mu_{2}k_{1}k_ {2}&\beta_{1}\mu_{2}k_{1}\\ \beta_{1}\mu_{2}k_{1}k_{2}&c_{2}+\beta_{1}\mu_{2}k_{2}^{2}&\beta_{1}\mu_{2}k_ {2}-\frac{1}{2}\\ \beta_{1}\mu_{2}k_{1}&\beta_{1}\mu_{2}k_{2}-\frac{1}{2}&\beta_{1}\mu_{2}\\ \end{bmatrix} \tag{35}\]  
due to  
\[z^{T}\Omega z=c_{1}z_{1}^{2}+c_{2}z_{2}^{2}-z_{2}z_{3}+\beta_{1}\mu_{2}s^{2} \tag{36}\]  
where \(z^{T}=\begin{bmatrix}z_{1}&z_{2}&z_{3}\\ \end{bmatrix}\). Then, if \(\Omega\) is guaranteed to be a positive definite matrix, we have  
\[\dot{V}_{3}=-z^{T}\Omega z-\beta_{1}\mu_{1}|s|^{\frac{3}{2}}-\beta_{2}s\bigg{/} \Phi_{2}(s)+\delta_{\text{max}} \tag{37}\]  
The parameters in the selection controller satisfy the following inequalities:  
\[\left\{\begin{array}{l}c_{1}>0,c_{2}>0,\beta_{1}>0,\mu_{2}>0k_{1}>0,k_{2}>0 \\ c_{1}c_{2}+c_{1}\beta_{1}\mu_{2}k_{2}^{2}+c_{2}\beta_{1}\mu_{2}k_{1}^{2}>0\\ c_{1}c_{2}\beta_{1}\mu_{2}+c_{1}\beta_{1}\mu_{2}k_{2}^{2}-\big{(}c_{1}+\beta_{ 1}\mu_{2}k_{1}^{2}\big{)}/4>0\\ \end{array}\right. \tag{38}\]  
It can be guaranteed that \(\Omega\) is a positive definite matrix when  
\[M=-z^{T}\Omega z-\beta_{1}\mu_{1}|s|^{\frac{3}{2}}-\beta_{2}s\bigg{/}\Phi_{2} (s)\leq 0 \tag{39}\]  
\(\dot{V}_{3}\) can be regained as  
\[\dot{V}_{3}=M+\delta_{\text{max}} \tag{40}\]  
As the error of the observer gradually converges to zero, i.e., \(\delta_{max}\longrightarrow 0\), when \(\dot{V}_{3}\leq 0\) and thus \(z_{1}\longrightarrow 0\), \(z_{2}\longrightarrow 0\) and \(z_{3}\longrightarrow 0\), then \(x_{1}\longrightarrow x_{1d}\), and the asymptotic stability of the system is achieved.  
Figure 2: Overall block diagram of the controller.
## 4 Photovoltaic Systems Optimization Criteria
### Reliability Analysis  
In PV system design, especially in SAPV system, one of the most important aspects to ensure power system security is to analyze power supply availability. This is because solar energy production in one site is intermittent, and energy generated usually will not match with load demand. A reliable power system is a generation system that has sufficient power to feed load demand in a period. There are many methods to determine reliability of power system. The most popular methods to express system reliability are loss of load probability (LOLP) and loss of power supply probability (LPSP). In both methods, if the probability is 0, the load then will always be fulfilled, while if the probability is 1 then the load will never be fulfilled.  
LOLP is a probability for the case when a load demand exceeds the generated power by the system. Here, the reliable PV system is defined as the system which is able to generate sufficient power (\(E_{PV}\)) to fulfil the demanded load (\(E_{L}\)) within time span. There are several researchers who apply this method in their proposed system's reliability analysis such as [69; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82]. LOLP can be described as,  
\[\mathrm{LOLP}=\frac{\sum_{i=1}^{8760}\;Energy\;Deficit_{i}}{\sum_{i=1}^{8760} \;Energy\;Demand_{j}} \tag{27}\]  
where,  
\[Energy\;Deficit_{i}=\sum_{i=1}^{8760}(E_{L}(i)-E_{PV}(i)) \tag{28}\]  
On the other hand, LPSP is defined as probability of the case when the system generates insufficient power to satisfy the load demand [83]. LPSP has been used in [84; 85; 86; 87] to measure PV system reliability as illustrated below.  
\[\mathrm{LPSP}=\frac{\sum_{i=1}^{8760}\;LPS(t)}{\sum_{i=1}^{8760}\;E_{L}(t)} \tag{29}\]  
where,  
\[LPS(t)=E_{L}(t)-[(E_{PV}(t)\;\eta_{bat})+E_{B}(t)-E_{B\;min}]\cdot[\eta_{inv} \;\eta_{wire}] \tag{30}\]  
There are two approaches for LPSP application in standalone PV design, based on chronological simulation and probabilistic technique. Chronological simulation can present dynamic changing in system performances, for example, energy accumulation effect on battery. Nevertheless, this technique requires time series data in a certain period, and it needs more computational effort, compared to probabilistic technique [83; 86; 88; 89]. On the other hand, probabilistic technique eliminates need for time series data and can assess long-term system performances. However, this technique not used in recent studies anymore, as it has a flaw, where researchers cannot observe dynamic changing in system performances.  
Some of the researchers will analyze hourly, daily, or monthly energy generated and battery state of charge, by using PV modelling and battery storage modelling as in the previous section. Then, by using the results of energy generated and battery state of charge, reliability analysis within analysis's period, either LOLP or LPSP can be done.
# Data-Driven Predictive Modeling of Neuronal Dynamics Using Long Short-Term Memory
## 2 Neural Network Architecture, Algorithm and Approach
### Synthetic Data  
Hippocampal CA1 pyramidal neurons exhibit various multi-timescale firing patterns (from simple spiking to bursting) and play an essential role in shaping spatial and episodic memory [31]. In the last two decades, several biophysiological models of the CA1 pyramidal (CA1 Py) neurons ranging from single compartmental biophysiological and phenomenological models [32; 33; 34] to detailed morphology-based multi-compartmental models [35; 36; 37; 38; 39; 40; 41] have been developed to understand the contributions of various ion-channels in diverse firing patterns (e.g., simple spiking to bursting) exhibited by the CA1 Py neurons.  
In this paper, we use an experimentally validated 9-dimensional nonlinear model of CA1 pyramidal neuron in the Hodgkin-Huxley formalism given in Reference [32] to generate the synthetic data for the network training and validation. The model exhibits several different bifurcations to the external stimulating current and has shown its capability in generating diverse firing patterns observed in electrophysiological recordings from CA1 pyramidal cells under various stimulating currents. Figure 5 shows three different firing patterns generated from this model based on the three different regimes of the applied input currents.  
To construct the synthetic training and validation dataset for the deep LSTM neural networks we designed in this paper with different predictive horizons, we simulated the Hodgkin-Huxley model of CA1 pyramidal neuron given in [32] (see Appendix A for the details of the model) for 1000 ms duration for 2000 constant stimulating currents, sampled uniformly between \(I=0.0\) nA and \(I=3.0\) nA. From these 2000 examples, we randomly and uniformly drew 50 samples (i.e., \(10^{4}\) data points) of the desired predictive horizon as the input/output sequence data for training and validation. As described in Section 2.4, we used 1/32 of these data points for validation, that is, 96,875 data points for the training and 3125 data points for the validation. Since our deep LSTM neural network takes an initial sequence of outputs of appropriate predictive horizon length (i.e., \(N_{p}=1,50,100,200\)) as an input sequence to make the next time-horizon prediction of equivalent length of sequence, we assume that this initial output sequence data is available to the deep LSTM neural network throughout our simulations.
# Map Matching Based on Seq2 Seq with Topology Information
## 2 Related Work
### Dataset Preparation
#### 2.2.2 Noise Trajectory Generation  
In real life, GPS systems often have some degree of deviation from the actual location, which is typically within a certain range known as the Fresnel distance as shown in Equation (1), where \(A\) and \(B\) are two continuous curves, \(\alpha\) and \(\beta\) are two reparameterization functions of the unit interval, and d is a metric function. This formula represents the minimum of the maximum distances between \(A(\alpha(t))\) and \(B(\beta(t))\) under all possible \(\alpha\) and \(\beta\) values. This is the optimal case of the worst-case distance under all possible path pairings. This definition captures the similarity between two paths, taking into account both the shape and length of the paths, or there may be an overall error in a series of GPS points. Therefore, following this rule, Gaussian spatial noise is added to all standard simulated paths to generate a series of noisy trajectories as a corpus for the model's input sequence, as described in Equation (2).  
\[F(A,B)=\inf_{\alpha,\beta}\max_{\mu\in[0,1]}\{d(A(\alpha(t)),B(\beta(t)))\}\;. \tag{1}\]  
\[f\Big{(}x\Big{|}\mu,\sigma^{2}\Big{)}=\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{\frac{ -(x-\mu)^{2}}{2\sigma^{2}}}. \tag{2}\]  
The expected value of the \(\mu\) distribution and \(\sigma\) is the standard deviation, both of which can be set from the actual accuracy requirements. Examples of noise tracks generated based on nodes 0 to 103 in Table 2 are shown in Table 3.  
The generated noise trajectory points of the entire road network are shown in Figure 4. The yellow dots are noise points generated by the algorithm, representing inaccurate latitude and longitude points, which are used as materials for model training. The red lines are the road network of Yibin City.  
Figure 4: Real trajectory and noise trajectory points.  
\begin{table}
\begin{tabular}{c c} \hline
**Starting Point and Endpoint** & **Cell Path** \\ \hline
0=\textgreater{}103 & [757497, 759494, 764486, 768484, 776483, 811426] \\
1=\textgreater{}3923 & [758500, 759499, 763494, 768488, 773487, 813422] \\ \hline \end{tabular}
\end{table}
Table 3: Noise Pathway Example.
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
## 2 Related Work
### Informer Model
(2019) proposed an improved stacked integration algorithm-based LSTM-Informer model (ISt-LSTM-Informer) to accurately predict photovoltaic power generation at different time scales. [PERSON] et al. (2019) improved the Informer model for photovoltaic power prediction by using the locality-sensitive hashing (LSH) attention mechanism. [PERSON] et al. (2019) proposed a medium-term photovoltaic power prediction model combining a GCN and the Informer model. This fusion model utilizes the multi-output capability of the Informer model and provides more reliable feature information for medium-term photovoltaic power prediction by using the feature extraction capability of the GCN model on the nodes, which ensures the accuracy of the long-sequence prediction. [PERSON] et al. (2019) proposed an anomaly detection scheme based on a graph attention network (GAT) and the Informer model. [PERSON] et al. (2019) used the Informer as the main predictor and based on the encoder-decoder architecture and the self-attention mechanism, the accurate prediction results are efficiently output. Therefore, based on existing research, this paper proposes a logistics transportation supply prediction method based on the improved Informer model. Aiming at the spatiotemporal evolution law of logistics and transportation supply volume data, we establish multidimensional feature engineering to extract the correlation factor feature information, design a spatiotemporal convolutional network inside the Informer model to extract the spatiotemporal features of the data, add an LSTM model to further extract the long- and short-term dependencies of the time series data, and finally predict the future supply volume through a multilayer perceptual machine.
###### Abstract  
Mobile phone data provides information, such as the home (origin) and current locations of people. The data can be used to analyze the impact of new high-speed railway (HSR) openings. This study examined the population observed in stations and cities of the Honam HSR line in Korea, based on mobile phone data recorded one year before and after its opening. We analyzed the volume of the population observed at each railway station, density of the distance between home and station, and activity hotspots in a city. The results show that the number of people and travel distance increased after the opening of the HSR. The distance to access railway stations increased, as the HSR saves travel time. Moreover, the activity hotspots in a city increased after the opening of the HSR, as more people gathered near the station area. The findings show that the mobility measures enhanced after the opening of the HSR for regional travel and local activities. These measures can help transit agencies and planners in providing better intercity travel.  
high-speed railway (HSR); mobile phone data; travel behavior; hotspots; compactness +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Evaluation Metrics  
To prove the robustness and accuracy of our algorithm with respect to other well-established software [20], we compare the surface meshes generated. The metric used for this comparison is the Intersection over Union (IoU), defined as:  
\[IoU=\left(A\cap B\right)\,/\,\left(A\cup B\right)\]  
where _p_=_a_ and \(B\) are two different volumes enclosed by the generated surface meshes. The _IoU_ is 1 when both objects are equal and overlap totally. It should be noted that, unlike in the computer vision field, the registration step can be skipped since our volumes are aligned in the 3 spatial axes, which simplifies the computation of the IoU considerably.  
We also perform an analysis of the quality of the volumetric mesh obtained from _Gmsh_. For this purpose, we have chosen two arbitrary metrics: the Aspect Ratio (AR) and the Aspect Frobenius (AF) [31], which compute how far the analysed mesh is from an ideal  
Figure 2: A whole-process demonstration example with an input of three binary layers representing the X, Y and Z letters: (**A**) signed Euclidean distance map of the first two masks (X and Y shapes). Pixels inside the perimeter (red line) present positive distances, whereas exterior pixels are assigned negative values; (**B**) distance maps are interpolated in any number of intermediate layers (left vertical cut) and thresholded to return a binarized volume (1 if distance > 0; 0 otherwise); (**C**) interpolation of 10 intermediate positions (equally distributed) between input and output layers; (**D**) surface mesh reconstruction (.stl format) (left panel), and 3D tetrahedral mesh generated with a transversal cut (middle panel). Insets 1 and 2 show the smoothness and good quality of the elements (right panel).  
mesh. The \(AR\) is defined as the ratio of the maximum edge with respect to the radius of the element's inscribed sphere:  
\[AR=max(x_{1},x_{2},\dots,x_{6})/2*\sqrt{6}*r\]  
where \(x_{1}\) to \(x_{6}\) are the length of the edges in a tetrahedron, and \(r\) is the radius of the sphere inscribed in the tetrahedral element.  
The AF of an element is the normalised Frobenius condition number of matrix \(A_{0}\). The mathematical expression for the Frobenius condition number of an element is:  
\[|A_{0}|_{F}=\sqrt{tr(A_{0}{}^{T}A_{0})} \tag{1}\]  
where \(A_{0}=T_{0}W^{-1}\). \(T_{0}\) is the edge matrix of the tetrahedral element, and \(W\) is the edge matrix of the reference regular tetrahedron.  
Both metrics are normalised, i.e., they equal one when the element analysed is the ideal regular element. According to other authors [31], acceptable ranges are [1, 3] and [1, 1.3] for the AR and AF, respectively.
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Metal or Metal Oxide Nanoparticles  
In the previous section dealing with graphene-based devices, metal nanoparticles (e.g., AuNPs) were already implemented in addition to graphene. In the following section are more generally  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Target** & **Probe** & **LoD** & **Method** & **Ref.** \\ \hline Cortisol & Ab & 0.05 ng mL\({}^{-1}\) & AuNPs/GO/o-PD/HRP/DPV & [98] \\ MMP-2 & Ab & 0.11 pg mL\({}^{-1}\) & AuNPs/GR/GO/Thi/DPV & [99] \\ HIV-p24 protein & Ab & 0.15 pg mL\({}^{-1}\) & GO/Thi/HRP & [100] \\ Adiponectin & Ab & 60 ng mL\({}^{-1}\) & RGO/Thi/HRP/ & [101] \\ CA19-9 carbohydrate & Ab & 6 mU mL\({}^{-1}\) & Au@PdNPs/GR/Thi/HRP & [102] \\ miRNA & Ab & 10 fmol L\({}^{-1}\) & RGO/MWCNT/HQ/HRP & [103] \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 17: **(A)** Preparation and detection procedures for the MMP-2 immunosensor. Reprinted from [99], Copyright 2013, with permission from Elsevier; **(B)** Typical DPVs corresponding to MMP-2 concentrations from a to j (0, 0.0005, 0.005, 0.02, 0.05, 0.25, 1.0, 5.0, 10.0 and 50.0 ng mL\({}^{-1}\)) in PBS + 4 mM H\({}_{2}\)O\({}_{2}\), and corresponding calibration curve; **(C)** The different steps involved in the construction of the adiponectin (APN) immunosensor using a metallocomplex polymer (CMC) and RGO on a screen-printed carbon electrode; **(D)** Corresponding calibration plot. Reprinted from [101], Copyright 2016, with permission from Elsevier.  
Figure 18: Electrochemical ELISA-like immunosensor for micro-RNA detection. Reprinted from [103], Copyright 2014, with permission from Elsevier.  
reviewed electrochemical immunosensors featuring metal or metal-oxide NPs, used to increase the surface area of the sensing layer but also and mainly to provide a catalytic activity in order to replace an enzymatically-catalyzed reaction (in enzyme-less immunosensors) or even to catalyze enzymatic products (in enzyme-based immunosensors).
## 4 Application Design and Implementation
### System Overview and Challenges
All transmissions in the system are broadcast, in order to reach the largest number of nodes at a time and to avoid any overhead related to routing tables' update issues. The IF protocol controls message rebroadcasting, guaranteeing an efficient network-wide data dissemination.
## 5 Results and Discussions
Figure 10 depicts the PI results, showing an improved PI range of \(-0.9\%\) to \(13.3\%\) in using the model compared to a \(-1.6\%\) to \(12.6\%\) range without the model. As can be seen in Table 8, the NPV, IRR, and PI are all superior when the proposed model is used. According to the results of the probabilistic economic evaluation using @Risk, the probability of the NPV \(>0\) and IRR \(>0\) increases when a fluctuating price ratio model is used. Alternatively, the distribution range of the PI is also higher. The probabilistic model's findings confirm that the proposed price index model equates to a reduction in the smelter's o \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Variable** & **Ni Content** & **2.0** & **1.9** & **1.8** & **1.7** & **1.6** & **1.5** \\ \hline Ore Price (\$M USD / ton) & 72.0 & 67.5 & 61.5 & 55.5 & 49.5 & 43.5 \\ Ore Purchasing Cost (\$M USD *) & 112.1 & 105.1 & 95.7 & 86.4 & 77.1 & 67.8 \\ Profit Decrease (\$ M USD *) & 15.1 & 27.9 & 40.7 & 53.5 & 66.3 \\ \hline \hline \end{tabular}  
* Currency conversion from KRW (Korean Won) to USD was made based on the average exchange rate of 1 USD = 1000 KRW in 2018.  
\end{table} 
Table 6: Cost and profit with Ni content variation (fluctuating price ratio). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Variable** & **2.0** & **1.9** & **1.8** & **1.7** & **1.6** & **1.5** \\ \hline Ore Price (\$M USD / ton) & 72.0 & 70.8 & 69.7 & 68.5 & 67.4 & 66.2 \\ Ore Purchasing Cost (\$ M USD *) & 112.1 & 110.3 & 108.5 & 106.7 & 104.9 & 103.1 \\ Profit Decrease (\$ M USD *) & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}  
* Currency conversion from KRW (Korean Won) to USD was made based on the average exchange rate of 1 USD = 1000 KRW in 2018.  
\end{table} 
Table 7: Cost and profit with Fe content variation (fluctuating price ratio). Figure 8: NPV probabilistic analysis (**a**) without model and (**b**) with model. Figure 9: IRR probabilistic analysis (**a**) without model and (**b**) with model. This model successfully mitigates the cited weaknesses of the existing long-term iron ore pricing model [40, 41] used in contractual feedstock agreements between the material supplier and smelter. It also negates the need for complex iron ore pricing modeling [10, 11, 12, 13, 14]. Although this model is presented for the Ni smelting process, the findings could theoretically be applied to any long-term procurement activities with variable quality and market conditions.