esa-sceva/satcom-chunk-collection huggingface dataset:
# Partial Energy Transfer Model of Lamb Waves Scattering in Materially Isotropic Waveguides
## 2 Mode Matching Technique
### Scattering of Lamb Waves at Vertical Notch  
Consider a homogeneous thickness waveguide with the present vertical notch of finite width \(L\), as shown in Figure 2. Note that the section below the notch is additionally split into two sub-waveguides for further application of the PET approach. Each waveguide section is numbered (number in a circle), including its interface. The continuity condition in terms of displacement vector/stress tensor components for a given interface is introduced by the following relations [27]:  
Figure 2: Scattering of Lamb wave at the vertical notch of width L.  
\[{}_{1}\mathbf{M}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{1}m_{n}(x_{1})_{1}\mathbf{M}_{n}(z)= \left\{ \begin{array}{c}{}_{2}\mathbf{M}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{2}m_{n}(x _{1})_{2}\mathbf{M}_{n}(z),z\in\left\langle\frac{t_{\text{fl}}}{2}-t_{\text{fl}2}, \frac{t_{\text{fl}}}{2}\right\rangle\\ {}_{3}\mathbf{M}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{3}m_{n}(x_{1})_{3}\mathbf{M}_{n}(z), z\in\left\langle-\frac{t_{\text{fl}}}{2}+t_{\text{fl}4},-\frac{t_{\text{fl}}}{2}+t_{ \text{fl}4}+t_{\text{fl}3}\right\rangle\\ {}_{4}\mathbf{M}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{4}m_{n}(x_{1})_{4}\mathbf{M}_{n}(z),z\in\left\langle-\frac{t_{\text{fl}}}{2},-\frac{t_{\text{fl}}}{2}+t_{\text{fl }4}\right\rangle\end{array} \right. \tag{13}\]  
\[{}_{1}\mathbf{N}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{1}n_{n}(x_{1})_{1}\mathbf{N}_{n}(z)= \left\{ \begin{array}{c}{}_{2}\mathbf{N}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{2}n_{ n}(x_{1})_{2}\mathbf{N}_{n}(z),z\in\left\langle\frac{t_{\text{fl}}}{2}-t_{\text{fl}2}, \frac{t_{\text{fl}}}{2}\right\rangle\\ {}_{3}\mathbf{N}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{3}n_{n}(x_{1})_{3}\mathbf{N}_{n}(z),z\in\left\langle-\frac{t_{\text{fl}}}{2}+t_{\text{fl}4},-\frac{t_{\text{fl}}} {2}+t_{\text{fl}4}+t_{\text{fl}3}\right\rangle\\ {}_{4}\mathbf{N}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{4}n_{n}(x_{1})_{4}\mathbf{N}_{n}(z),z\in\left\langle-\frac{t_{\text{fl}}}{2},-\frac{t_{\text{fl}}}{2}+t_{\text{fl }4}\right\rangle\end{array} \right. \tag{14}\]  
Equations (13) and (14) can be further rewritten in the following form:  
\[{}_{1}\mathbf{M}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{1}m_{n}(x_{1})_{1}\mathbf{M}_{n}(z) =\sum\limits_{m=2}^{N_{m}=4}\sum\limits_{n=1}^{N}m_{n}(x_{1})_{m}\mathbf{M}_{n}(z), \tag{15}\]  
\[{}_{1}\mathbf{N}(x_{1},z)=\sum\limits_{n=1}^{N}{}_{1}n_{n}(x_{1})_{1}\mathbf{N}_{n}(z) =\sum\limits_{m=2}^{N_{m}=4}\sum\limits_{n=1}^{N}{}_{m}n_{n}(x_{1})_{m}\mathbf{N}_{ n}(z), \tag{16}\]  
where \(N\) refers to the number of considered Lamb wave modes for which \(N\in\mathbb{N}\), \(N_{w}\) represents the highest index number of sub-waveguide section present and associated with the main waveguide at the junction \(x=x_{1}\) at the same time (see Figure 2), and the left subscript denotes the waveguide affiliation.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
###### Abstract  
Resonators are passive devices that respond to an excitation signal by oscillating at their natural frequency with exponentially decreasing amplitudes. Physical, chemical and electrical variables can modify the natural frequencies of resonators. If resonators are connected to antennas or other transducers that couple into a communication channel, they enable purely passive sensors that can be read wirelessly. In this manuscript, we use maximum likelihood estimation to analyze the measurement accuracy that can be achieved by the wireless readout of passive resonant sensors as a function of the read signal, the stimulation power and noise figure of the reader, the distance and transducer gain of the transmission channel, and the natural frequency and quality factor of the resonant passive sensor. The Cramer-Rao lower bound characterizes the minimum variance of the natural frequency and decay constant of the resonator. We show the derivation of the Cramer-Rao lower bounds from the Fisher information matrix based on a maximum likelihood estimation of discrete-time samples of an exponentially decaying phasor. This theoretical lower limit of accuracy is almost achieved by an iterative algorithm that approximates the maximum of the measured resonator spectrum with a Lorentz curve.  
resonator; quality factor; wireless; passive; chipless; estimation theory; statistical error; measurement accuracy; Cramer-Rao lower bound; Fisher information matrix; maximum likelihood estimation; Aboutanios algorithm Article
# Anticipating Future Behavior of an Industrial Press Using LSTM Networks
## 5 Experiments and Results
### Experiments to Determine Historical Window Size and Number of LSTM Units Using One Sample per Day  
The first experiments performed aimed to determine the best window size to use. The smaller the window, the smaller and faster the model that can be used. However, if the window is too small, it may be insufficient to make accurate predictions.  
The original dataset had 1,445,760 data points, which is very large and would require a lot of memory and time to train and test. The experiments were performed after downsampling the data, so that there is only one sample per day. That sample is the average of 1004 original samples. The downsampled dataset is, therefore, less than the one thousand of the original dataset.  
The results are measured in the test set. The figure above shows the MAPE and MAE measured for each variable. It also shows the global RMSE measured globally for the train and test sets.  
As Figure 7 shows, models with windows of 40 and 50 samples allow better learning and produce smaller prediction errors.  
Figure 5: Model summary of one of the LSTM networks used. The model receives a window of \(n\) samples of each variable and predicts the value of those variables as predicted 30 days ahead.  
Figure 6: Example of learning curve, showing the loss measured during training of an LSTM model.  
Additional experiments were performed to determine the best size for the number of cells in the hidden layer. For those experiments, a window of 40 historical samples was used, relying on the results of the previous experiments.  
Figure 7 shows the results obtained for experiments with a window of 40 days and different numbers of hidden cells. As the results show, the model with the best performance is the one with 50 hidden cells.  
After the results of the first experiments with one sample per day, additional experiments were conducted to determine if there was any considerable loss in downsampling from one sample per minute to one sample per day. A first experiment was performed, which consisted of halving the downsampling period from 24 to just 12 h. Therefore, the dataset doubled in size, since it contained two samples per day instead of just one.
# Antarctic Surface Ice Velocity Retrieval from MODIS-Based Mosaic of Antarctica (MOA)
## Data  
### Moa  
MOA datasets, together with a grain size map, are compiled by the National Snow and Ice Data Center (NSIDC) and University of New Hampshire [53]. Their source images were collected from December of the prior year to February of the next year in the periods of 2003-2004 and 2008-2009, respectively, from the MODIS instruments on-board the NASA EOS Aqua and Terra satellites. The two digital maps achieve a better spatial resolution of about 150 m by leveraging pixel cumulating schema. Furthermore, the 125 m grid images have a spatial coverage identical to the Radarsat Antarctic Mapping Project Antarctic Mapping Mission 1 (RAMP AMM-1) 125 m mosaic. They provide a nearly-perfect cloud-free view of the ice sheet, ice shelves, and outcrop surfaces for land areas north of 60 degS, allowing us to investigate its potentiality in determining the displacement between two acquisition times.  
This and the following datasets are all released and mapped as Antarctic 71 degS Polar Stereographic Projection (EPSG:3031).
## 3 Cardiovascular Diseases
### Atherosclerosis
#### 3.1.1 Endothelium  
The vascular endothelium consists of a monolayer of specialised cells, known as ECs, connected by tight junctions. These ECs are covered by a dense layer of glycoproteins and/or glycolipids, known as the glycocalyx, forming a barrier that lines the lumen of blood vessels. This barrier serves as the first line of defence against circulating molecules, cells, and pathogens in the bloodstream. In the large vessels, the endothelium, together with collagen and elastic fibres, forms the tunica intima. The tunica media lies beneath, and it is predominantly composed of vascular smooth muscle cells (VSMCs), collagen, and elastic tissue, providing elasticity and contractility to the vessels. Surrounding this layer is the tunica adventitia, which is mainly composed of a dense connective tissue matrix, offering structural support and protection to the blood vessel [108; 111; 112].  
Given its location, the endothelium, specifically the ECs, continuously senses hemodynamic and biochemical changes in the bloodstream and transmits signals to the underlying layers of the vascular wall. Through their ability to produce effector molecules that regulate thrombosis, vascular tone, vascular remodelling, and inflammatory response, ECs act as key regulators of vascular homeostasis [112; 113]. A compromise of this regulatory capacity, triggered by several factors, including turbulent and oscillatory blood flow (low shear stress), oxidised LDL (ox-LDL), and oxidative stress, results in endothelial dysfunction, a crucial event in the pathogenesis of atherosclerosis [112; 114].
## 2 Materials and Methods  
The thermally-reduce graphene oxide (TRG) was purchased from the GIBusiness, Taiwan, and was synthesized from natural graphite using a modified Hummers method [18], followed by thermal treatment at elevated temperatures. Typically, 10 mg of TRG powder was dispersed in 30 mL of 0.1 M CTAB surfactant solution with the aid of ultrasonication and vigorous stirring for 12 h. After that, 3 mL of the mixture containing 1 mg of TRG was deposited on a 1 cm\({}^{2}\) Celgard 3500 separator using a vacuum filter to obtain the TRGC electrodes. Furthermore, the TRGC electrodes were rinsed with 1.5 mL of 0.2 M of EMI-TFSI ethanol solution during filtration to obtain the TRGCE electrodes. The filtrated electrode on the separator was flipped over on a 1 cm\({}^{2}\) of 0.1 mm thick 304 stainless steel current collector to prepare each side of the electrode as shown in Figure 2b. The TRGSE samples were obtained following the same procedure but using SDS as surfactant. The EDLC cell was in the form of a two-electrode package inside a sealed polypropylene testing bag, filled with EMI-TFSI as the electrolyte as shown in Figure 2. For comparison, the TRG electrodes were also produced by dispersing 10 mg of TRG in 30 mL of 20 wt % of ethanol solution using the same dispersion and filtration process. The electrical impedance spectroscopy (EIS, 0.1 to 1 MHz), cyclic voltammetry (CV, between 0 and 3.2 V), and galvanostatic charging and discharging (between 0 and 3.2 V) measurements were made using a potentiostat (potentiostat, VersaSTAT 4, Princeton Applied Research, Oak Ridge,  
Figure 1: The depiction of the process to enlarge the interlayer distance between TRG sheets through the Coulombic force interaction. TRG: thermally-reduced graphene oxides; SDS: Sodium dodecyl sulfate; CTAB: Cetyltrimethylammonium bromide; TRGS: Thermally reduced graphene oxide intercalated with SDS; TRGC: Thermally reduced graphene oxide intercalated with CTAB; TRGSE: Thermally reduced graphene oxide intercalated with SDS and EMI-TFSI; TRGCE: Thermally reduced graphene oxide intercalated with CTAB and EMI-TFSI.  
Figure 2: Schematic of (**a**) the structure of the S\(-\)C\(+\) EDLC (electrical double layer capacitor) cell and (**b**) the supercapacitor cell for electrical measurement.  
TN, USA) using the two-electrode method. The microstructures of the composites were characterized via a scanning electron microscope (SEM, JEOL 6700, 5 kV, Tokyo, Japan), X-ray diffraction (XRD; Bruker-AXS D8, CuK\(\alpha\) = 1.5406 A, Karlsruhe, Germany), and small angle X-ray (SAXS, Nanostar U system, Bruker AXS Gmbh, CuK\(\alpha\) = 1.5406 A, Karlsruhe, Germany). The weight composition was determined by thermal gravimetric analyses (TGA; TAQ50, TA Instruments, New Castle, DE, USA) at a heating rate of 10 \({}^{\circ}\)C /min under nitrogen flow). A Fourier Transform Infrared Spectrometer (FTIR, Bruker Vertex 70v, Ettlingen, Germany) was also used to investigate the bond vibration in a wave number range of 4000 to 400 cm\({}^{-1}\).
## 3 Performance Analysis of System  
In this section, we first derive the formulas of BER. Then, the expression of harvested energy is derived. It is supposed that the maximum multipath delay is much shorter than symbol duration, i.e., \(0<\tau_{max}\ll\beta\). Thus, the inter-symbol-interference (ISI) is negligible [6; 18]. In addition, a slowly fading channel is considered and channel coefficients are assumed constant during a symbol duration.
# Intelligent Communication in Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSN) are designed to collect information by means of a large number of energy-limited battery sensor nodes. Therefore, it is important to minimize the energy consumed by each sensor, in order to extend the network life. The goal of this work is to design an intelligent WSN that collects as much information as possible to process it intelligently. To achieve this goal, an agent is sent to each sensor in order to process the information and to cooperate with neighboring sensors while mobile agents (MA) can be used to reduce information shared between source nodes (SN) and send them to the base station (Sink). This work proposes to use communication architecture for wireless sensor networks based on the multi-agent system (MAS) to ensure optimal information collection. The collaboration of these agents generates a simple message that summarizes the important information in order to transmit it by a mobile agent. To reduce the size of the MA, the sensors of the network have been grouped into sectors. For each MA, we have established an optimal itinerary, consuming a minimum amount of energy with data aggregation efficiency in a minimum time. Successive simulations in large-scale wireless sensor networks through the SINALGO (published under a BSD license) simulator show the performance of the proposed method, in terms of energy consumption and package delivery rate.  
wireless sensor network; agent cooperation; mobile agent; itinerary; multi-agent system; source node; communication +
Footnote †: journal:  
_Future Internet_**2018**, _10_, 91; doi:10.3390/610090091
# Simulation-Guided Path Optimization for Resolving Interlocked Hook-Shaped Components
## 5 Results
### Real-World Experiments
The second issue could potentially be addressed by introducing fallback mechanisms that safely release the grasped objects and replan the motion. The timelapse of the separating motion is shown in Figure 8.  
As shown in Figure 9, after a few iterations of updating the covariance matrix, no further progress is made in updating the path, as the covariance matrix is not large enough to generate sufficiently distinct noise. This stagnation suggests that the exploration capability of the optimization process becomes constrained, preventing the discovery of potentially better solutions beyond the current path. However, since the separation motion was successfully achieved in most cases, this issue can be overlooked for the addressed application. In more complex tasks, however, this limitation could significantly impact performance and the quality of the solution. A restricted covariance matrix might lead to premature convergence to suboptimal paths, reducing the robustness of the optimization process. To address this, adaptive mechanisms for dynamically adjusting the covariance matrix size could be explored, allowing for greater variability and enhanced exploration during different stages of the optimization. Figure 7: Parallel simulations in Isaac Gym. Each robot executes a different interpolated sampled path. The different paths are represented by green lines.
# Atom Probe Tomography for Catalysis Applications: A Review
###### Abstract  
Atom probe tomography is a well-established analytical instrument for imaging the 3D structure and composition of materials with high mass resolution, sub-nanometer spatial resolution and ppm elemental sensitivity. Thanks to recent hardware developments in Atom Probe Tomography (APT), combined with progress on site-specific focused ion beam (FIB)-based sample preparation methods and improved data treatment software, complex materials can now be routinely investigated. From model samples to complex, usable porous structures, there is currently a growing interest in the analysis of catalytic materials. APT is able to probe the end state of atomic-scale processes, providing information needed to improve the synthesis of catalysts and to unravel structure/composition/reactivity relationships. This review focuses on the study of catalytic materials with increasing complexity (tip-sample, unsupported and supported nanoparticles, powders, self-supported catalysts and zeolites), as well as sample preparation methods developed to obtain suitable specimens for APT experiments.  
atom probe tomography; APT; FIB sample preparation; alloy engineering; core-shell structure; nanoporous catalyst; zeolite; correlative techniques +
Footnote †: journal: Applied Physics Letters
# A Brief Review of Multipath TCP for Vehicular Networks
## 3 Congestion Control and Scheduler
### Congestion Control
#### 3.1.2 Congestion Control with AI
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Main Metrics or Key Idea** & **Publication** & **Research Summary** & **Limitation/Advantages** \\ \hline Packet loss rate and RTT & [PERSON] et al. [45] & A mechanism that takes into account the packet loss and RTT in the congestion window adjustment. & TCP fairness/Vulnerable in heterogeneous network \\ \hline Packet queuing delay & [PERSON] et al. [49] & An approach that formulates the multipath congestion problem and considers packet queuing delay as signal for congestion control. & Sensitive to network change/Weakness in taking available network bandwidth \\ \hline RTT, retransmission timeout value, packet loss & [PERSON] et al. [52] & A TCP-friendly approach for multipath transport protocol that considers the bottleneck of network. & TCP fairness/Inefficient utilization of network bandwidth \\ \hline Loss and delay signal & [PERSON] et al. [53] & A proposal that detects shared bottlenecks by loss and delay signals, and makes decision of coupling/decoupling subflows. & Adaptive to network change/No real system evaluation \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Studies on MPTCP congestion control.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 3 An Open Service for Remote ECG
### Doctor's Side Web Interface  
On the doctor's side, we need a generic display: a laptop or a smartphone fit the purpose. To avoid portability issues, we prefer that such a task runs as a JavaScript application in a browser.  
As a consequence of the above option, the protocol for the transport of ECG data is the Hypertext Transfer Protocol (HTTP). Such a system design is very convenient, as HTTP implements an effective security policy while avoiding the limits created by firewalls and other security measures. However, HTTP features a rigid request-response scheme which is a problem in our design, since we need to establish a persistent one-way flow for the ECG data. To solve this kind of problem, in 2011, the Internet Engineering Task Force (IETF) introduced the _WebSocket_[20]: in essence after having established an HTTP session, the partners may upgrade it to a bidirectional channel, similar to a Transport Control Protocol (TCP) connection--hence the name--not constrained to follow the request/response protocol.  
Given that the doctor's interface is an HTTP client, we need to find where to run a server for it. It is impractical to implement the server in the patient's sensor since in that way we should traverse the NAT  
Figure 1: ECG visualization on doctor’s display. Both the laptop and the sensor are in Italy, while the relay server is geolocated in Ireland; on the right side, the report about transfer/loss rates.  
which is almost certainly in place on that side. Instead, we consider the use of a relay server in the cloud as a better solution, but we first examine the operation of the patient's side device.
## 3 Results and Discussion
Cost reductions are more substantial in configurations where the HP has operational priority, with the greatest reduction of 33% observed in the system featuring the largest HP unit (1000 kW). As anticipated, systems prioritising CHP operation show more modest cost reductions, up to 12% compared to the baseline. These results suggest that under favourable electricity pricing conditions, the implementation of medium- to large-scale HPs may be economically feasible. Figure 12: Comparison of system performance in different price variants in terms of (**a**) absolute values of levelized cost of energy for heating; (**b**) values of levelized cost of energy for heating relative to the base DH system. Accordingly, the long-term economic analysis focuses on Scenario 3, which reflects favourable electricity pricing conditions. To assess the feasibility of integration of HPs under these conditions, global costs over a 20-year period are calculated for each system configuration. The results for systems with prioritised operation of HP and CHP-HP are presented in Figure 13. Among these configurations, the CHP-HP/B system exhibits the highest levelized cost of heating \(C_{\text{LH}}\), resulting in the longest payback period of 7 years. In contrast, systems with prioritised HP operation demonstrate improved economic performance, achieving payback periods ranging from 4 to 6 years. The shortest payback period--4.7 years--is observed in the system with a 600 kW HP, followed closely by the 500 kW and 700 kW configurations. Figure 14 illustrates the results for systems with prioritised CHP operation under Scenario 3. Due to higher \(C_{\text{LH}}\) values, these configurations exhibit longer payback periods compared to those with prioritised HP operation. The shortest payback period in this group is 8 years, recorded for systems with 400 kW and 500 kW HP units. As the installed HP capacity increases, the payback period correspondingly extends, reaching up to 11.5 years for the system with a 1000 kW HP unit. Overall, the payback period increases with HP capacity. Furthermore, configurations in which the HP is integrated without operational priority are found to be economically unviable under the given conditions. Figure 13: Global costs over the considered period for systems with prioritised CHP-HP and HP operation in price Scenario 3.