esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunities for Two-Color Experiments in the Soft X-ray Regime at the European XFEL
## 1 Introduction  
Two-color pump-probe spectroscopy is a very powerful tool to study the temporal evolution of an excited system or, more generally, to investigate the dynamics of a photoinduced process [1; 2]. Typically, a first photon is used to initiate the process and a second interrogates the system under study in a time-resolved way. The technique is widely used combining an intense optical laser as a pump and a short wavelength source, such as a synchrotron [3] or a high harmonic generation source [4] as a probe. The advent of Free Electron Lasers (FELs) operating in the soft and hard X-ray regimes has enabled scientists to explore processes on the femtosecond time scale by taking advantage of theshort pulse duration and the high intensity of FEL pulses [5; 6]. Recent examples of this type of studies at FELs are related to the dissociation dynamics of molecules, magnetic ordering of material, as well as charge transfer processes [7]. One major challenge in these experiments is related to the inherent temporal jitter of the two independent laser sources and to its precise characterization in order to actually take advantage of the short pulse duration. Until now, only experiments using a seeded FEL in combination with the optical seed laser could demonstrate high temporal resolution reaching the sub-femtosecond time scale [8].  
New developments at FEL sources have shown that it is also possible to generate two FEL pulses at different photon energies, which enable pump-probe experiments [9; 10; 11]. This method has two major advantages; first, the temporal jitter is drastically reduced, since both pulses are produced by the same electron bunch and, second, pump and probe pulses are both of high photon energies. This allows, for example, to selectively address different sites in a molecule or different subshells in an atom for inducing a process (pump) as well as for monitoring its evolution (probe). Very soon after the start of operation of the European XFEL discussions were initiated about the possibilities to realize such a two-color pump-probe scheme at the SASE3 Soft X-ray branch of the facility.  
Already the simplest way of generating two-color pulses at the SASE3 beamline of the European XFEL, in combination with the high-repetition rate capabilities of the facility is expected to enable novel exciting science at the two soft X-ray instruments Small Quantum Systems (SQS) [12; 13] and Spectroscopy & Coherent Scattering (SCS) [12; 14].  
In the following we will summarize the scope of the project (Section 2) and the scientific background for the two-color pump-probe (2 CPP) set-up at the European XFEL with the help of concrete proof-of-principle experimental proposals (Section 3) followed by the results of simulations for pulse generation and transport (Section 4), while in Section 5 we show the results of recent experiments demonstrating the first two-color lasing capabilities. Finally, Section 6 comprises a short conclusion and outlook.
# V2G Strategies to Flatten the Daily Load Curve in Seoul, South Korea
###### Abstract  
In order to meet the increasing demand for electricity to maintain electric vehicles (EVs) worldwide, this paper aims to improve our understanding of the impact of the load on the power grid generated by the charging and discharging of electric vehicles. The rapid development of the electric vehicle (EV) industry offers new economic and environmental benefits, such as mitigating global warming by reducing carbon dioxide. On the other hand, however, we will face the reality that the emergence of such large-scale EVs will undoubtedly put additional strain on the power grid. In this context, solving the problem of excessive power usage associated with charging large electric vehicles and reducing the impact on the grid is paramount. Accordingly, in order to meet the increasing demand for electricity to maintain electric vehicles (EVs) worldwide, this paper aims to improve our understanding of the impact of the load on the power grid generated by the charging and discharging of electric vehicles. A V2G strategy is presented with the goal of flattening the daily load curve by considering the charge and the discharge positions of EVs. First, in this paper, based on the estimated share of electric vehicles, we set the assumption that EVs travel to work in the morning and leave work in the afternoon. Second, we develop an efficient V2G strategy to equalize the daily load curve due to charging and discharging of electric vehicles in Seoul by applying a system marginal price (SMP) and time-of-use (TOU) rate system. The EV charging/discharging load and existing load using V2G modeling are added up, all daily load curves are calculated and analyzed based on the 2030 and 2040 EV share scenarios for Seoul, and the grid load is leveled. The analysis suggests measures to minimize the impact of EV loads on the power grid according to the V2G strategy based on charging and discharging plans. Overall, this paper aims to smooth the grid's daily load curve and avoid grid overload by applying appropriate SMP and TOU plans; we also present an efficient V2G strategy, established through charge and discharge modeling and EV charge and discharge management techniques, in order to minimize grid expansion.  
V2G strategies; daily load curve; charging location; discharging location; time of use (TOU); system marginal price (SMP) +
Footnote †: journal: Applied Sciences
# Solution for Active and Passive Earth Pressure on Rigid Retaining Walls with Narrow Backfill
## 2 Theoretical Analysis
### Earth Pressure Distribution  
According to the study of [PERSON] et al. [41], the change in soil stress behind the retaining wall is closely related to the wall displacement and soil depth. The expression for the earth pressure resultant force behind the wall is shown in Equation (20).  
\[E_{a(p)}=F_{\Sigma x}=\int_{0}^{h}\sigma_{x}dz=\frac{1}{2}K_{0}\gamma h^{2}+ \frac{1}{1-\mu^{2}}\int_{0}^{h}E_{t}\varepsilon_{x}dz \tag{20}\]  
Figure 3: Force analysis of cohesionless soils in the passive limit state.  
\[E_{t}=\left[1-\frac{R_{f}(1-\sin\vartheta)(\sigma_{1}-\sigma_{3})}{2c\cos\varphi+2 \sigma_{3}\sin\varphi}\right]^{2}KP_{a}(\frac{\sigma_{3}}{p_{a}})^{n} \tag{21}\]  
In this context, \(E_{t}\) represents the tangent modulus in the Duncan model; \(R_{f}\) represents the failure ratio; \(K\) denotes the modulus parameter; \(P_{a}\) signifies the atmospheric pressure; \(n\) is the dimensionless index; \(\mu\) is the Poisson's ratio and \(K_{0}\) is the coefficient of earth pressure at rest. The limit earth pressure resultant force is derived from Equations (3) and (13) and then inserted into Equation (20) to ascertain the limit strain \(\varepsilon_{xmax}\).  
The displacement mode significantly impacts the displacement distribution of the retaining wall. In the translational mode, the soil deformation behind the wall is independent of the burial depth (\(z\)), represented by \(\varepsilon_{x}=\varepsilon_{x\max}\). In the rotation mode around the wall bottom, the maximum soil deformation occurs at the wall top. The deformation gradually decreases with increasing depth (\(z\)) and approaches zero at the wall bottom. This distribution function is described as \(\varepsilon_{x}=\varepsilon_{x\max}/H\). In the rotation mode around the wall top, the maximum soil deformation occurs at the wall bottom, while no deformation is observed at the top. The distribution function is expressed as \(\varepsilon_{x}=(1-H/z)\varepsilon_{x\max}\). By substituting these distribution functions of \(\varepsilon_{x}\) into Equation (22), the distribution function of earth pressure on the retaining wall in the limit state can be obtained.  
\[\sigma_{x}=\frac{\mu}{1-\mu}\sigma_{z}+\frac{E_{t}\varepsilon_{x}}{1-\mu^{2}} \tag{22}\]
## 3 Methods of Slope Stability
### Input Parameters
#### 3.2.3 Soil Parameters  
The soil parameters for Units I and II (Table 1) were derived based on the CPT data, laboratory tests, and recommendations by the Norwegian Road Authorities handbook V220. The selected parameters were best estimates.  
The governing differential equation used in the formulation of coupled convective analysis in TEMP/W and SEEP/W was:  
\[\frac{\partial}{\partial x}\bigg{(}K_{x}\frac{\partial T}{\partial x}\bigg{)}+ \frac{\partial}{\partial y}\bigg{(}K_{y}\frac{\partial T}{\partial y}\bigg{)} +c_{w}\frac{\partial(q_{w}T)}{\partial y}+Q=\bigg{(}c+L_{w}\frac{\partial W_{u} }{\partial T}\bigg{)}\frac{\partial T}{\partial t}\]  
where \(T=\) temperature; \(K_{x}\), \(K_{y}=\) thermal conductivity in the x- and y-directions respectively; \(c_{w}=\) volumetric heat capacity of water; \(q_{w}=\) the specific discharge of water; \(Q=\) applied boundary flux; \(c=\) volumetric heat capacity of soil; \(L=\) latent heat of water; \(w=\) the volumetric water content; \(W_{u}=\) the total unfrozen volumetric water content; \(t=\) time.  
The full thermal model (TEMP/W) was applied to simulate freezing-thawing of soils. The water content in the model was changed during the analysis. The main parameters for the thermal model were thermal conductivity vs. temperature and unfrozen water content vs. temperature (see Figure 8). The saturated/unsaturated model (SEEP/W) was applied to simulate water flow into the soils. The model allows seepage analysis in fully saturated and unsaturated soils. The main parameters of the model are water conductivity vs. matrix suction and water content vs. matrix suction (see Figure 9). Those parameters were verified and revised based on the continuous field monitoring and the data from instruments on the slope, as discussed below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & **Depth** & **Soil Unit Weight** & **Friction Angle, \(\psi^{\prime}\)** & **Cohesion,** & \multicolumn{4}{c}{**Grain Size Distribution**} & \multirow{2}{*}{**Classification**} \\  & **(m)** & **(kN/m\({}^{3}\))** & **(deg)** & **c\({}^{\prime}\) (kPa)** & **Gravel, \%** & **Sand, \%** & **Silt, \%** & **Clay, \%** & \\ \hline Unit I & 0–6 & 19.0 & - & 100.0 & - & 17.8 & 60.5 & 31.0 & Slity clay \\ \hline Unit II & 6–10 & 19.0 & 30.0 & - & 61.8 & 26.4 & 0.3 & - & Sand, gravel \\ \hline \hline \end{tabular}
\end{table}
Table 1: Best estimated soil parameters at the Øysand slope site.
## 4 Materials and Methods
### Heuristics  
There are several heuristics for finding solutions to the DPFSP problem, which are adaptations of heuristics for the PFSP that consider the existence of multiple factories in DPFSP. Some PFSP heuristics are shortest processing time (SPT); largest processing time (LPT); [PERSON]'s rule; CDS heuristic of [PERSON], [PERSON], and [PERSON]; minimum idle time (MINIT); minimum completion time (MICOT); and NEH heuristic of [PERSON], [PERSON], and [PERSON]. Details about these and many more heuristics can be consulted in [31]. An interesting remark is that NEH [32] is regarded as the best-performing heuristic for PFSP when the optimization criterion is the makespan. NEH iteratively constructs a schedule by inserting jobs into an initially empty sequence. It starts by sorting the jobs in descending order based on the sum of their processing times on all machines. Then, it iteratively inserts each job into the current sequence at all possible positions and evaluates the makespan after each insertion. The job permutation that results in the shortest makespan for the partial schedule is chosen and this process is repeated until all jobs are scheduled. The worst case complexity of a naive implementation of NEH is \(\mathcal{O}(n^{3}m)\), where \(n\) is the number of jobs and \(m\) is the number of machines, but by applying the accelerations proposed in [33], the complexity becomes \(\mathcal{O}(n^{2}m)\).  
Since DPFSP has to decide about two things: the allocation of jobs to factories and the sequence of jobs that will be processed at each factory, NEH might be combined with a rule for the allocation. Such a rule might be \"assign job \(j\) to the factory which completes it at the earliest time\" as proposed in [1], which gives the heuristic named NEH2, which seems to perform better than other alternatives for DPFSP. An extension of NEH2 is NEH2_en [13], which, after inserting a job into the best position among all factories, selects at random either the previous or the following job and assesses its relocation at all possible positions of the partial solution in the same factory. The worst case complexity for both NEH2 and NEH2_en is \(\mathcal{O}(n^{2}mF)\), where \(F\) is the number of factories.  
In this approach, NEH2 is used to produce initial solutions for the CP solver. In particular, for each problem instance, NEH2 is executed multiple times (10 to 50, based on the size of the problem) and the best solution (i.e., the solution with the lowest makespan) is kept among all runs. Since NEH2 is deterministic, a minor modification is applied for the algorithm to return a different solution at each run. After the first run, the sequence of jobs created using the algorithm, based on the sum of each job's processing times on all machines, is \"lightly\" shuffled. This occurs by taking all triplets of consecutive jobs in the original sequence and shuffling each triplet. Then, the subsequent steps of NEH2 are executed as usual. This \"trick\" seems to work nicely since it consistently produces better than the initial NEH2 solution for most cases, while the time burden imposed is minimal.
## 3 Results and Discussion
### Characterization of Wines by HS-SPME/GC-MS, UHPLC and ICP-OES  
The qualitative identification of the volatile components of _Trebbiano d'Abruzzo_, _Pecorino_, and _Passerina_ aroma was performed by GC-MS analysis. Figure S1 (Supplementary Materials) shows a typical chromatogram. Table S2 (Supplementary Materials) displays the list of the detected compounds and their mean content in the aroma of the three wine varieties, expressed as the relative (%) area in the GC-MS chromatogram, together with the related standard deviation. Table S3 (Supplementary Materials) displays the list of the organic acids and polyphenols detected by UHPLC whereas a typical chromatogram is shown in Figure S2 (Supplementary Materials). The identification carried out by means of GC-MS and UHPLC is in strong agreement with the literature. In fact, many of the determined compounds have also been reported by other authors [30; 31] as present in white wines of different cultivars grown both in Italy (such as those under investigation) and in other countries.  
The mean concentration of the 14 elements detected by ICP-OES in the three wine varieties is reported in Table S4 (Supplementary Materials). Part of the novelty of the present work lies in the fact that the mineral composition of the investigated wines had never been discussed before. However, the estimated concentrations are in excellent agreement with those reported in the literature on white wines of different cultivars. A complete discussion over the quantification of mineral elements falls outside the scope of the present work; consequently, for more details, the reader is addressed to [PERSON] [32] for the concentrations of Co, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Sr, and Zn; to [PERSON] [33] for Ca and V; and to [PERSON] [34] for Ba.
# A Data Processing Framework for Polar Performance Diagrams
## 2 Problem Formulations and Theoretical Backgrounds
### Weather Routing -- An Application of Polar Diagrams
#### 2.5.2 Isochrone Methods  
The main idea of approaches subsumed under the label _isocrroue methods_ is to iteratively find samples of positions which can be reached in a predefined amount of time from already-explored positions (see [17]). To reduce complexity, positions which do not seem profitable are dismissed on the way. If the final destination is covered by the isochrones, an optimal path can be traced back. An illustration of the isochrone approach is given by Figure 5 (see also [18]).  
An important task when running such an algorithm is to find positions which can be reached in a predefined time in a given direction. To be precise, for a given starting position \(A\), a direction \(w\) and a predefined total time \(c\), we aim to find the distance \(s_{*}\) we need to  
Figure 4: An example of a rule declaring how to connect nodes by edges.  
Figure 5: An example of isochrone points with edges to trace back the optimal path.  
travel from \(A\) in direction \(w\), such that \(t(s_{*})=c\). Here, \(t(s_{*})\) is defined as before. The established way is to calculate isochrone points by assuming the weather conditions given at the starting point. We aim to include an algorithm in our framework which can compute isochrone points while considering the changing weather conditions along the way.
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 2 Free-Space Optical Communications
### Ground-Station Selection  
In Reference [18], the authors outline the development efforts to overcome the lack of current applications of FSO. They survey several ground locations that can support laser communications and detail the basic design of an optical ground station. Systems with FSO links are not limited by their capacity, but they suffer from cloud blockage. In the design of optical networks, it is crucial select weather-uncorrelated locations first; then, the corresponding layout is used for distance and orbit calculations [19].  
Having a larger ground-station diversity is a widely adopted strategy to mitigate the cloud blockage disadvantage [20; 21; 22]. More specifically, in Reference [21], the objective was to maximize the transfer of data from a LEO satellite as the criterion to select the ground-stations. In Reference [22], the authors studied how to minimize the number of ground-stations within certain availability constraints of the network. In the state-of-the-art study for this topic, Reference [19], an algorithm is proposed that achieves a better trade-off between network availability and computational complexity, when compared to conventional methods. The selection of ground-stations is formulated as a mathematical optimization problem to maximize the network availability and solved with a method which provides better performance complexity trade-off.
###### Abstract  
The existence of karst cavities has an important impact on the safety of foundation pit excavation projects. It is of engineering guiding value to study the influence of karst cavities on the blasting process of foundation pits and how to optimize the stiffness of cement mortar to improve the blasting effect. Based on the karst foundation pit bench blasting project of Shenzhen Dayun Foundation Pit Project, this paper adopts the SPH-FEM coupling calculation method to study the influence of karst cavities, cavity-filling water and cavity-filling silt clay on the rock-blasting process of bench blasting. We analyzed the development process of blasting damage of rock when the stiffness of karst cavity grouting filling changes under the conditions of slightly weathered, moderately weathered and strongly weathered limestone. The calculation results show that the karst cavity near the blasthole changes the direction of the minimum resistance line, which leads to the release of blasting energy; the rock breaking effect is improved when the karst cavity is filled with water medium and clay medium. Under the three limestone conditions, after the karst cavity is pretreated by cement grouting, the increase in the stiffness of the cement mortar makes the rock damage area first increase and then decrease after the karst cavity implosion, and There is a critical cement mortar stiffness that makes the best rock breaking effect. The critical cement stiffness of micro-, medium- and strongly weathered limestone is 2.2%, 6.1% and 27% of the blasted rock mass, respectively, which makes the karst cavity wall stress reach the peak value, and the rock-breaking effect is the best at this time.  
karst foundation pit; SPH-FEM; damage; cement grouting +
Footnote †: journal: Applied sciences
# Fake Base Station Detection and Link Routing Defense +
## 7 Implementation and Experimental Results
### Faulty Experiment Informs Threshold Control  
We use fake base station detection and blacklisting while ensuring that faulty base stations are not detected as fake base stations and blacklisted. Blacklisting a temporarily faulty base station, e.g., no connectivity at the time, can have a lasting impact on the base station and deprive the user equipment of options when trying to access cellular service in the future. Because of this importance, our analyses focus on the false-positive probability/rate, corresponding to when the base station is actually legitimate but faulty but our scheme detects it as a fake base station and blacklists it.  
To achieve a minimum false-positive rate, our scheme selects the optimal threshold by using reference values that are informed by the faulty base station experiment. The reference values for obtaining the optimum thresholds can be derived at the user equipment level under the worst radio channel condition with the faulty base station. The faulty base station establishes a radio connection with the user equipment and releases the connection afterward due to the worst channel conditions. Because our reference values come from the worst channel conditions, the optimal thresholds hence ensure fake base station detection with zero false positives under varying network conditions. The concrete analyses for our detection threshold control are described in the next section, Section 7.5.