esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 3 Proposed FRL-SR Algorithm  
In this section, we first discuss the design of the reinforcement learning model, including states, reward functions, and actions in Section 3.1. Then, we briefly introduce the neighborhood discovery and explain the propagation range of 'hello' packets in reinforcement learning in Section 3.2. The training approach and algorithm are proposed in Sections 3.3 and 3.4. In Section 3.5, we analyze the time complexity and space complexity of the FRL-SR algorithm and the Dijkstra algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**State** & \multicolumn{3}{c}{**Neighbor**} \\ \cline{2-5}  & \(\mathbf{N_{1}}\) & \(\mathbf{N_{2}}\) & \(\mathbf{N_{3}}\) &... \\ \hline \((i,D_{1})\) & \(Q_{i}((i,D_{1}),N_{1})\) & \(Q_{i}((i,D_{1}),N_{2})\) & \(Q_{i}((i,D_{1}),N_{3})\) &... \\ \hline \((i,D_{2})\) & \(Q_{i}((i,D_{2}),N_{1})\) & \(Q_{i}((i,D_{2}),N_{2})\) & \(Q_{i}((i,D_{2}),N_{3})\) &... \\ \hline \((i,D_{3})\) & \(Q_{i}((i,D_{3}),N_{1})\) & \(Q_{i}((i,D_{3}),N_{2})\) & \(Q_{i}((i,D_{3}),N_{3})\) &... \\ \hline \((i,\ldots)\) &... &... &... &... \\ \hline \hline \end{tabular}
\end{table}
Table 1: Q-table of node I.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definition** \\ \hline G & graph of the LEO satellite network \\ V & set of satellite nodes \\ E & set of satellite links \\ M & number of orbits \\ N & number of satellites per orbit \\ \(D_{ij}\) & transmission delay between node \(i\) and node \(j\) \\ \(N_{gi}\) & the set of neighbors of node \(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations of variables.
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
## 3 Impedance-Based Stability Analysis of SSO
### Impedance Sensitivity Analysis
#### 3.1.4 Impedance Sensitivity of Current Controller Parameters  
Similarly, the impedance curve dynamics while increasing the current controller parameters \(K_{pi}\) and \(K_{ii}\) are shown in Figure 13. It can be concluded that adjustments of \(K_{pi}\) and \(K_{ii}\) hardly affect the stability because the zero crossing frequency of resistance is nearly not changed.  
Figure 11: Impedance sensitivity of current (Drops). (**a**) Reactance \(X_{PMSG}\); (**b**) Resistance \(R_{PMSG}\).  
Figure 12: Impedance sensitivity of PLL parameters (Increase). (**a**) Sensitivity of PLL proportional constant \(K_{p}\); (**b**) Sensitivity of PLL integral constant \(K_{i}\).
## 3 Experimental Setup
### Experimental Procedure  
Different environmental conditions, such as wind speed and ambient temperature, were recorded. In addition, the thermal power that the photovoltaic cells did not convert into electricity was also recorded. Several longitudinal tilt angles of the secondary system were used in the study. The experimental procedure was carried out according to the following steps:  
1. The water was circulated through the cooling system by the pump to simulate thermosynphon operation. The flow meter registered the water flow rate, and a valve was used to keep it constant.
2. The electrical resistance simulated the energy that the photovoltaic cells do not convert into electricity. An autotransformer was used to ensure that the thermal power remained constant throughout the test.
3. The test started with a wind speed of 0 (m/s), and every hour the wind speed was increased up to 10.03 (m/s).
4. The following parameters were measured during the test: the power absorbed by the electrical resistance, the ambient temperature, the water flow rate, the temperature at the water inlet/outlet, and the wind speed.
# Attosecond Pulse Shaping by Multilayer Mirrors
## 4 Achieved Results and Examples of Multilayer Mirrors for Attosecond Pulses
### Attosecond Photoelectron Spectroscopy Utilizing Pulse Trains  
Whereas many pump-probe experiments require isolated pulses, as is particularly the case for attosecond streaking, it is also possible to do time-resolved attosecond photoelectron spectroscopy utilizing pulse trains instead of isolated pulses. In this case, the combination of the fundamental laser and its high harmonics gives rise to sidebands in the spectrum of photoelectrons emitted from a target. Photoemission delays can be extracted from the delay-dependent intensity of these sidebands via RABBITT [78]. Although less intuitive than attosecond streaking, this technique is also perfectly suited to carrying out measurements with attosecond precision [79]. Moreover, it features several advantages, such as simultaneous measurement at different discrete excitation energies (i.e., with different high harmonics) and high-energy resolution due to the well-separated sharp harmonics of bandwidths of a few hundred meV. This allows for discerning different initial (or final) states within a RABBITT-trace  
Figure 10: Merit function and GDD dependence on layer thickness errors. (**a**) Merit function for the simulated reflectivity and the soft X-ray measurement for different layer thickness errors. (**b**) Mean GDD for different layer errors.  
and referencing their photoemission delays to each other [36; 80; 81] or resolving the band dispersion in solids and angle-dependent effects in gas and perform attosecond-ARPES [36; 82; 83].  
EUV multilayer mirrors for RABBITT-experiments can be more broadband than for attosecond streaking, since it is not necessary to spectrally filter the cut-off region. A large mirror bandwidth can be advantageous for measuring simultaneously at multiple excitation energies [36; 84]. However, if the mirror bandwidth is larger than the energy separation between two initial states that are being compared, it will complicate the data analysis because photoelectrons from different initial states and different harmonics start to overlap [80; 81].  
An EUV multilayer mirror design and an EUV spectrum that have been used for RABBITT measurements on tungsten [85] are depicted in Figure 11a. The mirror reflectivity is centered around 65 eV with a peak reflectivity of 40% and a bandwidth of 5 eV (FWHM) supporting about three high harmonics at a fundamental wavelength of 1030 nm. The EUV spectrum calculated from this mirror design fits well to the measured photoelectron spectrum from a tungsten (110) single crystal (Figure 11b), which features broader peaks due to the electronic band structure and a tail of slower secondary electrons.
## 4 Numerical Simulation Analysis  
To compare and analyze the stability of the tunnel surrounding rock under two excavation methods, the displacement and stress evolution characteristics of the tunnel surrounding rock were analyzed separately. Based on the site geological conditions, the model dimensions were set as length \(\times\) width \(\times\) height = 50 m \(\times\) 60 m \(\times\) 50 m, with the tunnel dimensions of width \(\times\) height = 4.12 m \(\times\) 4.86 m. The 3D numerical model is shown in Figure 3, where the bottom and sides are fixed with boundary constraints, and the top serves as the stress boundary.  
The physical and mechanical parameters of the rock layers obtained from the laboratory tests are shown in Table 1. The model uses the Mohr-Coulomb failure criterion to describe the strength characteristics of the rock mass. The expression of the Mohr-Coulomb failure criterion is:  
\[\sigma_{1}=\frac{1+sin\varphi}{1-sin\varphi}\sigma_{3}+\frac{2C\ cos\varphi}{1 -sin\varphi}, \tag{4}\]  
where \(\sigma_{1}\) is the maximum principal stress, \(\sigma_{3}\) is the minimum principal stress, C is the cohesion, and \(\varphi\) is the internal friction angle.
###### Abstract  
The recent increase in earthquake activities has highlighted the importance of predicting the seismic response of structures. Damage to civil infrastructure, particularly bridges, can cause considerable human and property losses. The seismic performance of a structure should be evaluated based on the characteristics of structures and earthquakes. For this, this study defined the two main factors of ground motion and structural system that affect the seismic response of a structure. Ground motions, which are mainly dependent on the distance from the epicenter, were defined as near-fault and far-fault ground motions. Near-fault ground motion includes the characteristics of forward directivity and fling step. In addition to ground motion, the aspect ratio of the pier, as a representative factor of a structural system, influences the seismic behavior of bridges. Thus, this study assessed the seismic response of bridges with various aspect ratios under the near-fault and far-fault ground motion conditions. Nonlinear static analysis was first performed to evaluate the seismic capacity of the pier. Then modal and dynamic analyses were carried out to examine the effects of the aspect ratio and ground motion on the displacement and force response and the change in the natural frequency of the bridge.  
bridges; seismic response; ground motion; far fault; near fault; forward directivity; fling step; aspect ratio; nonlinear dynamic analysis +
Footnote †: journal: Applied Sciences
# Photodynamic Therapy--An Up-to-Date Review
## 4 Nanomaterials for Photodynamic Therapy  
By exploring new strategies to improve PDT, integrating PSs with nanotechnology came as a highly promising solution to increase therapy effectiveness [15]. Nanomaterials have recently become an important component of PDT for achieving enhanced results in terms of specific targeting, high drug loading, multifunctional integration, improved solubility of hydrophobic PSs, maintenance of a constant PS delivery rate, and reduced toxic effects over healthy cells [15; 36]. One of the attractive features of nanoparticles is their large surface-to-volume ratio. This property promotes loading capacity, improving concentration delivery and uptake in the target cells. The small size of nanoparticles also helps them mimic biological molecules, allowing these nanocarriers to easily pass through the immune system barriers [29]. Moreover, their surface can be functionalized with specific ligands that can be recognized only by certain receptors, this unique match being the drug-release trigger [7; 26]. In this respect, PSs may be encapsulated in or immobilized to nanoplatforms to be selectively delivered into the tumor [7; 16] (Figure 4). A variety of nanostructures can be employed for this purpose, including diverse metallic, organic, inorganic, and polymeric materials in the form of dendrimers, micelles, liposomes, quantum dots, nanoparticles, antibody-drug conjugates, and more [15; 16; 43]. A promising approach is represented by metal-based nanoparticles that can carry and deliver hydrophobic PSs to tumoral tissues through the enhanced permeability and retention (EPR) effect [8; 65; 66; 29]. Compared to conventional photosensitizers, metal-based nanoparticles have a long cycle time, slow degradation, targeted and controllable release, being advantageous for PDT [8].
## 3 Results and Discussion
### Damage Pattern Recognition  
#### 3.1.1 Clustering Results of Common Clustering Methods  
The five common clustering methods described above have one aspect in common: they all need to be classified first and then the optimal number of clusters determined. Therefore, it is necessary to find the cluster number before using the five common clustering methods to cluster the crumb rubber concrete acoustic emission signals. To solve this problem, four evaluation indexes, Davies-Bouldin, Tou, Silhouette [54], and Dunn, were used in this paper.  
Figure 4: Artificial neural network structure.  
When using the five common clustering methods and four clustering evaluation indexes in the clustering analysis, a total of six acoustic emission parameters, including rise time, count, energy, duration, amplitude, and peak frequency, were used for analysis [55,56]. The clustering evaluation index results of these five commonly used clustering methods can be seen in Figure 5. According to the description of the four clustering evaluation indicators in this section, the smaller the Davies-Bouldin (DB) index, the better the classification effect. The Dunn (D) index, the Silhouette index, and the Tou index are numerical values indicating classification; the higher their value, the better the classification effect. According to Figure 5, the optimal clustering number corresponding to these five clustering methods is three.  
Figure 5: Clustering evaluation index results of five common clustering methods: (**a**) k-means clustering; (**b**) FCM clustering; (**c**) GMM clustering; (**d**) SOM clustering; (**e**) hierarchical clustering.  
In addition, according to the clustering results of the five commonly used clustering methods shown in Figure 6, it is found that some data points cannot be clearly distinguished, and the division of the three clusters is not particularly obvious. Therefore, it is crucial to find a better clustering method to analyze the acoustic emission data of crumb rubber concrete.
## 6 Energy Management
### Smart Energy Management
#### 6.2.1 Energy Management by Considering Pricing Scheme  
Different pricing schemes have been used by the distribution companies in order to create an efficient energy management scheme. The pricing schemes proposed so far are real-time pricing, time of use pricing, critical peak pricing, day ahead pricing. In real-time pricing, the consumer is notified about the charges on an hourly basis. In time of use pricing, the consumer is charged more during peak periods, less during mid-peak, and least during off-peak. An energy management that links fee structure and scheduling of appliances operation can be used to minimize cost of energy. One example is the in-home energy management (iHEM) [96]. iHEM utilizes smart appliances, a central energy management unit (EMU), and wireless sensor networks for communication between the appliances, EMU, and smart smart meters. The EMU communicates with a smart meter to inquire about the updated charges. Occupants' requirements are processed in near real-time, then iHEM suggest a convenient start time to the consumer. The consumer at this phase may be willing to negotiate with EMU. The decision support tool (DsT) proposed in [97] has the main intention to assist users make smart decisions during their appliances operation. In the absence of energy management programs, 30% of the load occurs in the course of peak hours. By employing iHEM, the peak load can be decreased to 5% [98]. iHEM also attenuates carbon emission and energy consumption expenses. In order to take advantage from the time of use pricing and reduce energy expense, appliance coordination have been proposed in [99] with the objective of shifting the consumer load to off-peak periods. The work indicates that the consumer's cooperation in the energy management program improves the efficiency of the method.  
Benefits of an energy management program may be increased if distributed energy resources (DER) coordination is adopted in parallel with the appliance coordination [100]. In [97], the concept of the DER coordination has been assessed. The net benefit of the consumer is maximized by scheduling the controllable DER conforming the scheduling algorithm. The consumer's energy bill is decreased by 16%-25% [97].
## 6 Experimental Results and Discussion
### Differences between Original and Temporal Recovery Data in Multifractal Spectrum Estimation
For example, in Figure 11, the width feature values of all spectra for HoneyBee spectra are quite different from the widths of all spectra found in Figure 11a,c. The right side is not pointed in Figure 11b due to the video content itself, where little motion is present. Thus, cumulative sums of trace sequences are presented in multifractal domain in order to have a novel consideration of content from the standpoint of the multifractal concept. Spectra curves obtain similar concave shapes due to a cumulative trend, as presented in Figure 12. The multifractal spectra of a cumulative sum of traces are presented for the sequences of ReadySetGo and HoneyBee in Figure 12. The structures seem more suitable to observe the behavior of data. Feature extraction is performed on the corresponding spectra in order to mark the differentiation between temporal recovery and the original case. Still, the width is not a proper selection as a feature for TRR detection. Thus, features are selected in a manner not to deal with the width but with the relative width, as in (8). Moreover, the right side of the spectra is zoomed for ReadySetGo and HoneyBee sequence in Figure 12 in order to observe additional features. Besides the slope, features (8)-(10) are applied in TRR detection. Figure 11: Legendre multifractal spectra presented for different HFR signals with different crf values: (**a**) ReadySetGo, (**b**) HoneyBee and (**c**) YachtRide. Figure 12: Legendre spectra structure and its right side for different crf values and different UHD 120 fps video content: (**a**) ReadySetGo and (**b**) HoneyBee. Differences between original and temporal recovery data in multifractal spectrum estimation, as in the case of the Hurst index differences, show the possibility to recognize TRR content. Common features such as spectrum width can be applied in most cases when original data of the same compression quality are available. Nevertheless, if different compression quality and content are taken into account, this is not an easy task, due to spectra overlapping. Thus, in practice, common features may not be useful, and TRR detection should be based on specific features.
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 2 Connectivity in Tactical MANETs
### Transmission Range  
It is important to determine the transmission ranges into order to obtain a connected network with given transmission powers. Connectivity and coverage of MANETs with different power control strategies have been well studied in the literature. In these studies, transmission range is either determined for given transmission power, or the minimum SINR threshold. However, the existing studies do not focus on specific context of managing tactical MANETs, and rarely consider typical networking problems such as hidden and exposed terminal problem, and congestion issues [11]. In order to more precisely determine transmission ranges for specific conditions encountered in tactical MANETs, average network connectivity can be evaluated assuming ratio of the expected number of paths between pairs of nodes over the total number of possible paths [12]:  
\[C_{N}(k)=\frac{2\ \sum_{i=1}^{N}\sum_{j=i+1}^{N}\ \varphi^{(ij)}(k)}{N\times(N-1)} \tag{1}\]  
where N is total number of network nodes, \(\varphi^{(ij)}(k)\) is the largest probability of route between nodes \(i\) and \(j\) computed over all possible paths, and \(k\) denotes the time instant. We note that this expression assumes LOS connectivity. Since tactical MANETs are often deployed in harsh terrains with where LOS connectivity is not available, the network connectivity must be sustained via intermediary relay or re-transmission nodes, and also by using aerial platforms such as balloons and drones.
# A Highly Integrated Millimeter-Wave Circularly Polarized Wide-Angle Scanning Antenna Unit
## 3 Two-Dimensional Wide-Angle Scanning Antenna Array  
### Antenna Array Design and Simulation Analysis  
The previously described antenna unit serves as a fundamental component for the phased array antenna. A triangular grid configuration is implemented to expand the scanning capability of the phased array. The array consists of 16 (4 \(\times\) 4) elements with inter-element spacings set to dx = 7 mm and dy = 6 mm. Figure 9 illustrates the layout of the array configuration. This is a top view of the antenna array, with a red box representing an antenna element.  
Extensive modeling and full-wave simulations are conducted using HFSS to evaluate the beam scanning capabilities of the phased array formed by the antenna units. Figure 10 displays the gain characteristics at different scanning angles, centered around frequency \(f_{0}\) with a bandwidth of 1 GHz. The simulations indicate that the circularly polarized wide-angle scanning antenna achieves extensive phased scanning in both azimuth and elevation, spanning \(\pm 60^{\circ}\) from the center frequency. At \(0^{\circ}\) scanning angle, the phased array antenna exhibits a gain of 15.9 dB. Within the antenna array's scanning range, the reduction in gain is consistently below 3.9 dB.
# Finite Differences on Sparse Grids for Continuous-Time Heterogeneous Agent Models
## 2 Heterogeneous Agent Models as Optimal Control Problems
### Optimal Control Problems  
Most deterministic infinite time optimal control problems in continuous time can be written as  
\[\max_{\{\alpha(t)\}_{t\geq 0}}J(x,\alpha),\text{ with }J(x,\alpha):=\int_{0}^{\infty}e^{-pt}h(x(t), \alpha(t))dt \tag{1}\]such that the law of motion for given state \(x\) and control \(\alpha\)  
\[\dot{x}(t)=f(x(t),\alpha(t))\text{ and }\alpha(t)\in A\]  
holds for \(t\geq 0\) and \(x(0)=x_{0}\) using the notation \(\dot{x}(t)=\frac{d}{dt}x(t)\).  
Here, \(x\in X\subset\mathbb{R}^{m}\) denotes the _state vector_, \(\alpha(t)\in A\subset\mathbb{R}^{n}\) the _control vector_, and \(h:X\times A\to\mathbb{R}\) the _instantaneous return function_. Further, \(\rho\geq 0\) denotes the _discount rate_ which discounts future returns. The state changes depending on the current state and action (control), following \(f:X\times A\to\mathbb{R}^{m}\). Note that there are _finite time_ and _infinite time_ models. In economics, infinite time models are often just used to simplify theoretical aspects.  
The _value function_ associated to the problem (1) is defined as  
\[v(x)=\max_{\{\alpha(t)\}_{t\geq 0}}J(x,\alpha).\]  
We define the _optimal control_ as \(\dot{\alpha}(t)\in A,\ t\geq 0\) such that  
\[v(x)=J(x,\dot{\alpha}).\]  
To solve optimal control problems, one uses the _dynamic programming principle_ (DPP) introduced by [PERSON]; see [14]. It is based on the recursive structure of the problem. By using this principle, one can show that the value function satisfies the HJB equation [15],  
\[\rho v(x)=\max_{\alpha\in A}h(x,\alpha)+f(x,\alpha)^{T}\
abla_{x}v(x)\cdot, \quad\forall x\in X. \tag{2}\]  
To compute the optimal controls, one typically uses the _first order conditions_ (FOCs) on the HJB equation. For that, one computes the derivatives with respect to the different controls and sets them to zero. The optimal controls for all states are collected in the so-called _policy function_.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 4 Practical Engineering Application
### Comparative Analysis of Monitoring Data  
To validate the accuracy of the calculation method for the assembled corrugated steel initial support structure, numerical results are compared with actual monitoring data. Despite challenges such as construction-related interference, delayed support timing, and damage to measuring points, several representative values of final CSP strain and tunnel convergence after secondary lining are selected. These values are subjected to comparative analysis with the corresponding outputs from the numerical simulations.
# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 4 Datasets and Performance Evaluation Indices  
In this section, we discuss most of the spacecraft telemetry datasets to which anomaly detection approaches in the literature have been applied. The commonly used performance metrics are also highlighted.