esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Predicting the Seismic Response by Applying the Energy Balance Concept
### Estimation of the Plastic Strain Energy \(W_{p}\)  
In RC frames with a soft first story and infill walls in the upper stories, plastic deformations occur only at the first story. The inelastic demand in terms of curvature ductility occurs at the columns' ends (critical regions), and it is very often, due to high axial load, much higher than the current ductility. Past studies [22; 23] have shown that in critical regions of primary seismic columns, the minimum amounts of transverse reinforcement indicated by codes such as Eurocode 8 are not sufficient in many cases in satisfying the curvature ductility demand, even when the axial load is moderate. Further, most existing RC frames that need seismic retrofitting were designed according to old seismic codes (or designed without seismic provisions), and the plastic deformation capacity of the mem bers is very limited. When establishing the energy balance of the structure, it is therefore reasonable not to rely on the plastic deformation capacity of the existing RC frame and neglect its contribution to the energy dissipation capacity of the structure in the nonlinear range. Considering that only the dampers of the first story dissipate energy through plastic deformations, \(W_{p}\) can be expressed as:  
\[W_{p}=\eta_{s}Q_{y1s}\delta_{y1}=\ \eta_{s}\frac{Q_{y1}^{2}}{sk_{1}}=\eta_{s} \alpha_{1}^{2}M^{2}g^{2}\frac{1}{sk_{1}} \tag{9}\]  
where \(s\)\(a_{1}\) is the base shear force coefficient provided by the dampers and \(\eta\) is the cumulative plastic energy deformation ratio defined by:  
\[s\alpha_{1}=\frac{sQ_{y1}}{Mg} \tag{10}\]  
\[\eta=\frac{W_{p}}{sQ_{y1s}\delta_{y1}} \tag{11}\]  
The cumulative plastic energy dissipation ratio \(\eta\) is related to the normalised maximum plastic deformation \(\mu=(\delta_{max1}-s\delta_{y1})/s\delta_{y1}\) through a parameter termed in the literature equivalent number of plastic cycles, \(n_{eq}\), and defined by \(n_{eq}=\eta/\mu\). Here, \(\delta_{max1}\) denotes the maximum deformation of the first story. For general structures, [PERSON] [19; 20] proposed the following expression to estimate \(n_{eq}\):  
\[n_{eq}=\left\{\begin{array}{c}4+4r_{q1}\ \forall\ r_{q1}<1\\ 8\ \forall\ r_{q1}>1\end{array}\right. \tag{12}\]  
where \(r_{q1}\) (=\(Q_{max,1}/s\)\(Q_{y1}\)) is the ratio of the maximum shear force experienced by the frame, \(\int\)\(Q_{max,1}\), to the yielding force of the dampers, \(s\)\(Q_{y1}\). For convenience, the following new ratios are defined:  
\[\chi_{1}=\frac{fk_{1}}{k_{eq}} \tag{13}\]  
\[K_{1}=\frac{sk_{1}}{fk_{1}} \tag{14}\]  
where \(k_{eq}\) is the stiffness of an equivalent SDOF with mass \(M\) and period \(T_{1}\), obtained by:  
\[k_{eq}=\frac{4\pi^{2}M}{T_{1}^{2}} \tag{15}\]  
Using the above ratios, the plastic strain energy of Equation (9) can be rewritten as:  
\[W_{p}=\frac{Mg^{2}T_{1}^{2}}{4\pi^{2}}\frac{s\alpha_{1}^{2}\eta}{K_{1}\chi_{1}} \tag{16}\]
## 2 Experimental System and Principle
### Principles of the Dynamic Brazilian Test under SHPB Loading  
As illustrated in Figure 2, the striker bar is driven up to a velocity (\(V\)) by a gas gun with compressed nitrogen gas. The striker bar then impacts on the incident bar, generating a compressive wave (traveling to the right) in the incident bar and another compressive wave (traveling to the left), with the same stress magnitude, in the strike bar. The free surface (left end) of the striker bar reflects the compressive wave as a tensile wave, which leads to a release wave in the incident bar. When the incident wave reaches the interface between the rock specimen and the incident bar, a part of the wave is reflected back as the reflected wave. The incident and reflected waves are recorded by the strain gauge (Gauge a) on the incident bar, and the corresponding strain histories are denoted \(\varepsilon_{i}(t)\) and \(\varepsilon_{r}(t)\), respectively. The remaining wave passes through the specimen and transmits into the transmission bar. The strain gauge (Gauge b) records the strain history, \(\varepsilon_{i}(t)\), of the transmitted wave.  
Figure 1: Layout of experimental system.  
From the one-dimensional stress wave theory [34], the forces \(P_{1}(t)\), \(P_{2}(t)\) at the contact points of the specimen can be expressed as:  
\[P_{1}(t)=EA[\varepsilon_{t}(t)+\varepsilon_{r}(t)], \tag{1}\]  
\[P_{2}(t)=EA\varepsilon_{t}(t) \tag{2}\]  
where \(E\) and \(A\) are the Young's modulus and the cross-sectional area of the bars, respectively. When the stress equilibrium, \(\varepsilon_{i}(t)+\varepsilon_{r}(t)=\varepsilon_{t}(t)\), in specimens is achieved, the equation, \(P_{1}(t)=P_{2}(t)\), will be satisfied. The indirect tensile stress in the specimen is expressed as:  
\[\sigma_{t}(t)=\frac{2P_{t}(t)}{\pi DB} \tag{3}\]  
where \(D\) and \(B\) are the diameter and thickness of Brazilian specimen, respectively, and the force is calculated by the equation, \(P_{t}(t)=[P_{1}(t)+P_{2}(t)]/2\)[23].
# Borophene Is a Promising 2D Allotropic Material for Biomedical Devices
###### Abstract  
Allotropic 2D materials are the new frontier of materials science, due to their unique strategic properties and application within several sciences. Allotropic 2D materials have shown tunable physical, chemical, biochemical, and optical characteristics, and among the allotropic materials, graphene has been widely investigated for its interesting properties, which are highly required in biomedical applications. Recently, the synthesis of thin 2D boron sheets, developed on Ag(111) substrates, was able to create a 2D triangular structure called borophene (BO). Borophene has consistently shown anisotropic behavior similar to graphene. In this topical review, we will describe the main properties and latest applications of borophene. This review will critically describe the most interesting uses of borophene as part of electronic and optical circuits. Moreover, we will report how borophene can be an innovative component of sensors within biomedical devices, and we will discuss its use in nanotechnologies and theranostic applications. The conclusions will provide insight into the latest frontiers of translational medicine involving this novel and strategic 2D allotropic material.  
biomaterials; biosensors; biomedical applications +
Footnote †: journal: Applied Physics Letters
# Compositional Analysis of Chalcopyrite Using Calibration-Free Laser-Induced Breakdown Spectroscopy
## 3 Results
### Quantitative Analysis  
The CF-LIBS method was adopted for quantification of chalcopyrite sample [19]. Prerequisites for application of this technique include fulfillment of local thermodynamic equilibrium (LTE) condition, optically thin plasma and stoichiometric ablation [18; 19].
## 3 Geophysical Data and Methodology
### Multichannel Analysis of Surface Waves  
The multichannel analysis of surface waves (MASW) method estimates the S-wave velocity (Vs) of the subsoil from the recorded surface waves. Surface waves have a dispersive nature when propagating through layered media, that is, each frequency has a different penetration depth and thus travels at a different velocity. The dispersion curves (Figure 5) describe the relationship between the phase velocity and the corresponding frequency and can be inverted to predict the Vs distribution as a function of depth in both 1D and 2D [65]. The main process in MASW is the transformation of the recorded surface waves from time (s) and offset (meter) domain (as data is recorded) to the phase velocity (m/s) and frequency (Hz) domain, to form the surface wave dispersion curve [66].  
In this work, we retrieved the dispersion curves using the wavefield transformation in each shot gather [67]. Then, we inverted each dispersion curve following an Occam's inversion algorithm [66] to get a 1D Vs model at each shot gather position. Finally, we created the pseudo 2D Vs velocity profiles interpolating and merging the 1D Vs models. To average spatially balanced small-scale sharp anomalies, a spatial smoothing operator was applied to the 2D velocity model. This operation can be justified by considering that the inverted model is an approximation of the average at a wavelength of 25-35 m combining the frequency of the seismic signal and the offset contribution. Furthermore, each velocity  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Seismic Survey Parameter** & **Description** \\ \hline Seismic source & Accelerated weight-drop (200 kg) \\ Source interval & 6 m \\ Source impacts & 1 \\  & a. La Torrecilla = 1278 m \\  & b. La Salud North = 960 m \\ Transect Length & c. La Salud South = 1488 m \\  & d. Carrascoy = 2496 m \\ Receiver interval & 2 m \\ Geophone Natural Frequency & 5 Hz \\ Recording Time & 4 s \\ Sample rate & 1 ms \\ File format & SEGY \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the seismic parameters in the study area.  
Figure 5: (**a**) Example seismic shot record with labelled red arrows showing the extent of the surface waves. (**b**) Corresponding dispersion diagram. The high amplitudes are indicative of the different phases (frequency and phase-velocity). The theoretical and inverted curves are shown as white and black dots, respectively.  
depth function is the smoothest model that can reproduce observations within a standard deviation of 10-12%.
###### Abstract  
This paper seeks to address the limitations of conventional remote sensing data dissemination algorithms, particularly their inability to model fine-grained multi-modal heterogeneous feature correlations and adapt to dynamic network topologies under resource constraints. This paper proposes multi-modal-MAPPO, a novel multi-modal deep reinforcement learning (MDRL) framework designed for a proactive data push in large-scale integrated satellite-terrestrial networks (ISTNs). By integrating satellite cache states, user cache states, and multi-modal data attributes (including imagery, metadata, and temporal request patterns) into a unified Markov decision process (MDP), our approach pioneers the application of the multi-actor-attention-critic with parameter sharing (MAPPO) algorithm to ISTNs push tasks. Central to this framework is a dual-branch actor network architecture that dynamically fuses heterogeneous modalities: a lightweight MobileNet-v3-small backbone extracts semantic features from remote sensing imagery, while parallel branches--a multi-layer perceptron (MLP) for static attributes (e.g., payload specifications, geolocation tags) and a long short-term memory (LSTM) network for temporal user cache patterns--jointly model contextual and historical dependencies. A dynamically weighted attention mechanism further adapts modality-specific contributions to enhance cross-modal correlation modeling in complex, time-varying scenarios. To mitigate the curse of dimensionality in high-dimensional action spaces, we introduce a multi-dimensional discretization strategy that decomposes decisions into hierarchical sub-policies, balancing computational efficiency and decision granularity. Comprehensive experiments against state-of-the-art baselines (MAPPO, MAAC) demonstrate that multi-modal-MAPPO reduces the average content delivery latency by 53.55% and 29.55%, respectively, while improving push hit rates by 0.1718 and 0.4248. These results establish the framework as a scalable and adaptive solution for real-time intelligent data services in next-generation ISTNs, addressing critical challenges in resource-constrained, dynamic satellite-terrestrial environments.  
Keywords:integrated satellite-terrestrial networks; multi-modal remote sensing data; reinforcement learning; relay system; intelligent push +
Footnote †: journal: remote sensing
## 2 Related Work
### Existing Dynamic Route Planning Algorithms  
In [8] a review of the most relevant algorithms to calculate routes in the vehicle routing stage of VPR problems is performed. These are classified into 3 groups as shown in Figure 7[8].  
Optimal Algorithms focus on finding the global optimal solution in the full range of existing solutions. In this category [PERSON]'s and incremental graph algorithms are found [25]. Heuristic Algorithms are algorithms that explore a subset of the total solution and allow to find a solution that is very close to the global optimum. As examples of heuristics algorithms we find A* Algorithm, Genetic algorithms [26], Ant Colony Optimization [27] and Tabu search [28]. Hybrid algorithms try to combine algorithms in each category to generate a powerful combined solution to compensate for any shortcomings or limitations on each individual strategy. As an example is the genetic algorithm for dynamic route planning using [PERSON]'s heuristic in multi-objective optimization, proposed by [PERSON] _et al._[29].  
Based on the presented classification, some different contributions over the evaluation of the described algorithms are usually executed by authors in different scenarios. For example, in the work done by [PERSON] _et al._[30], four algorithms based on the shortest-path strategy are evaluated in a scenario with realistic data obtained from the TAPAS-Cologne project dataset, using SUMO simulator [31]. The algorithms evaluated were Dijkstra and A*, each one in a static and dynamic way. They select three areas (center, suburban and rural), and according to the evaluation performed, there is a suggestion for the most efficient routing algorithm in the different road scenarios.  
Figure 7: Classification of Dynamic Route Planning Algorithms. Reproduced with permission from [8] [PERSON] _et al._, First International Workshop on Vehicular Traffic Management for Smart Cities (VTM); published by IEEE, 2012.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 4 Results and Discussion
### Residual Analysis  
The residuals (prediction errors) represent the differences between the predicted and actual values at each time step. Ideally, a well-performing model should yield residuals that are symmetrically distributed around zero and exhibit no obvious pattern over time. Figure 13 illustrates the residual distributions of six models, reflecting their prediction errors relative to actual values.  
In each case, the error becomes smaller and smaller. Figure 13a shows LSTM's performance. The residuals are mostly concentrated around zero after the time step of 38,000. The early section likely includes transition periods or unusual patterns. Figure 13b gives ANFIS's residuals. The errors are symmetrically distributed with moderate scatter and fewer extreme outliers compared to LSTM. The residuals still show a visible spread early in the timeline. ANFIS is more robust to nonlinear but smooth transitions due to its fuzzy logic rule-base. However, it may struggle with highly dynamic or stochastic segments. Figure 13c gives the NAR model's residuals. There is a similar pattern to ANFIS with symmetric error spread and relatively fewer extreme values. The residuals stabilise quickly. NAR networks are recursive and perform well in autoregressive tasks. Figure 13d shows GRU's performance. It has a relatively small scatter and fewer extreme errors, especially after the 38,000 mark. The distribution is tighter than LSTM's. GRU converges faster and generalises better than LSTM when the training data are limited. It may offer a good balance between complexity and performance. Figure 13e shows CNN-RNN's performance. The distribution is similar, with minimal extreme values. Figure 13e shows MLP's performance. It exhibits a slightly broader error distribution. Lacking memory, MLP models rely solely on current and recent input values without temporal feedback, making them less suited to time-series prediction unless lagged inputs are manually engineered. Consequently, NAR and MLP have a superior performance. GRU offers a faster performance. LSTM shows potential but has a lower performance. ANFIS performs reasonably well but may not handle highly dynamic segments or abrupt transitions efficiently.
## 3 System Model
### Receiver  
In the receiver operation \(SNR\) received from the transmitter and relay are measured and combined at receiver. Performance of the system is measure by using Shannon capacity theorem that is given in Equation (5).  
\[C=B\times log_{2}(1+SNR) \tag{5}\]  
In Equation (5), \(C\) is the channel capacity, \(B\) is the bandwidth, and \(SNR\) is the signal to noise ratio.
## 4 Conclusions  
This study demonstrates the significant potential of utilizing ultrafine microsilica particles (~128 nm) as a minor reinforcement (~0.36 wt%) in aluminum matrix composites (AMCs) produced via ultrasonic-assisted stir casting, followed by T6 heat treatment. Key findings of this research include the following:  
* Enhanced mechanical properties: The incorporation of microsilica led to a notable improvement in both strength and hardness of the composites. T6 heat treatment further enhanced ductility, achieving a balance between strength and elongation. For the heat-treated composite (S2-HT), the tensile strength reached 73 MPa with an elongation of 60.3%, demonstrating its suitability for forming processes.
* Structural improvements: The ultrasonic-assisted stir casting process ensured uniform dispersion of microsilica particles, reducing porosity and agglomeration. T6 heat treatment refined the microstructure, improving the bonding at the matrix-particle interface.
* Cost-effectiveness and sustainability: By leveraging microsilica, an industrial by-product, this study contributes to the development of cost-efficient and environmentally sustainable materials, addressing waste management challenges while enhancing material performance.  
The results validate the feasibility of using ultrafine microsilica particles to produce high-performance AMCs suitable for advanced forming processes like caliber rolling. Future research may focus on optimizing processing parameters and exploring higher reinforcement concentrations or hybrid composites to further enhance mechanical properties.  
Conceptualization, M.A. and I.T.; methodology, M.A., validation, K.N.; formal analysis, N.L. and N.A.; investigation, I.T.; resources, D.K.; data curation, N.L.; writing--original draft preparation, K.N.; writing--review and editing, A.A.; visualization, D.K. and A.A.; supervision, M.A.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (grant no. AP14972831).  
Not applicable.  
All the relevant data are contained within the article itself. Additional data may be shared by the authors following a reasonable request.  
We extend our special gratitude to the staff of the Department of Technological Machines and Transportation at Karaganda Industrial University, Temirtau, Kazakhstan, including [PERSON], [PERSON], and [PERSON], for their continuous support and collaboration throughout this research.  
Author [PERSON] is employed at Qarmet Joint Stock Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Abbreviations  
The following abbreviations are used in this manuscript:  
OIIOcean Observation Initiative  
TCBSTimed Communication Buoy System  
ACFAutonomous Communication Float  
SCB Satellite Communication Buoy  
TDTemperature-Depth sensor  
CTD Conductivity-Temperature-Depth sensor  
CTD-IMConductivity-Temperature-Depth sensor with an Inductive Modem  
HYCOMHYbrid Coordinate Ocean Model  
WOAWorld Ocean Atlas
## 2 Design and Modeling of Soft Actuator with Embedded FBG Sensors
### The Fabrication and Prototype of the Soft Actuator  
In this section, we describe our procedure for molding the soft actuator The design of the FBG-based optical fiber is embedded into the soft actuator through a polyimide tape, which acted as the strain limited layer; the bending behavior and deformed shape measurement of the soft actuator was realized by the FBG sensor. The soft actuator mainly includes five parts: A hemi-circle elastomeric air chamber which including the caps at the distal and proximal ends; an optical fiber with two FBG sensors is glued on the bottom surface of the silicone layer; some fiber windings are wrapped around the chamber of the actuator; and the whole actuator is encapsulated by a soft silicone sheath [11]. The radial expansion was limited and the linear extension was promoted by the reinforcement of the circumferential fibers, the linear elongation of the bottom surface of the soft actuator was restricted by the strain limited layer, so the part of the soft actuator extends while the strain limited layer restricts expansion along one surface, and thus generating a bending motion as the pressure was inputted into the soft actuator.  
The fabrication of a soft actuator with embedded FBG sensors is performed through several steps. First, equal amount of Ecoflex-0050 A and B are mixed and poured into the mold #1 to fabricate the half round soft actuator with a thickness of 4 mm, and the silicone actuator is cured for 4 hours under indoor temperature. An Objet Connex 500 printer (Objet500 Connex3, Stratasys, Eden Prairie, MN, USA, 2016) is used to printing the two (#1, #2) models of the soft actuator, as depicts in Figure 3. Second, the optical fiber with two FBG sensors is put on the bottom surface of the silicone layer, then a polyimide tape is glued on the bottom surface, and thus the optical fiber is fixed to the bottom of the silicone layer. Third, a single Kevlar fiber is used as fiber reinforcement and wrapped around the actuator body in a double helix pattern, and the actuator body is then encapsulated by a 1.0 mm thick silicone layer putting its entire assembly into the mold #2. Finally, the mold (#2) and the half steel rod are extracted from the soft actuator, then some additional silicone is poured on the top and bottom of the actuator in order to enclose the chamber and connect the pipe of pneumatic actuation.  
The prototype of the soft actuator with different views are presents in Figure 4, as we can see from Figure 4, an optical fiber with two FBG sensors are embedded into the soft actuator through a polyimide tape, and the optical fiber with FBG sensors are well protected by the polyimide thin film layer. Thus, the FBG-based polyimide sensing layer is formed, and it is embedded into the soft actuator as the strain restricted layer, which served as a primary part to realize bending and shape measurement, and the FBG sensors are stretched with the bending motion of the soft actuator.  
Figure 3: 3D printed mold of the soft actuator.