esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
The material properties, span-to-height ratio (_L/h_), power law (_p_), beam theory, and end conditions were provided to the ANN architecture as input data. Thus, it can be implied that the ANN method accurately represents the behavior of FGM beams. The ANN system was trained through a dataset provided by the results of previous studies on FG beam vibrations. The analytical solution was analyzed for a higher-order shear deformation theory that considered only the first mode (_m_ = 1) of the simply supported FG beam. Based on all of the information above, the following results were obtained:  
1. It was observed that the results obtained with HSDT substantially overlap with other higher-order shear deformation theories used in the literature. 2. Non-dimensional frequency values obtained from the ANN converged significantly to the results obtained with the analytical solution, with a very low margin of error. 3. The accurate natural frequency values can be attained by taking the shear effect into account (shear deformation effects lead to a decline in the natural frequencies). 4. It can be implied that the non-dimensional frequency values obtained by higher-order shear deformation theory and classical beam theory for the analytical and ANN solutions converge depending on an increase in the span-to-height ratio. 5. The ANN algorithm can be improved by providing more datasets to predict deformation parameters with smaller amounts of error. The authors believe that the results listed above will be an important resource in the design of functionally graded materials that are frequently used in many fields such as energy, construction, mechanical engineering, electronics, biomedicine, aerospace, and defense. Theoretical and numerical analysis, review and editing, M.C. ; computational research and writing, E.G. ; computational research and writing, E.E.O. ; numerical analysis and writing, E.D. ; theoretical analysis, review and editing, R.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. The authors declare no conflict of interest.
## 4 Experiments
### Parameter Settings  
For AGCHK, we set the maximum number of iterations _max_iter_ to 20. According to Section 3.2.1, the range of heat diffusion becomes larger as the scaling parameter \(s\) increases. Cora and Citeseer might have similar parameter settings since they have the close node and edge sizes, and the parameter \(s\) might be smaller due to their lower quantity. Pubmed has more nodes and edges; thus, its parameter \(s\) might be larger. Since Wiki has more edges and fewer nodes, we set \(s\) very small so that AGCHK can leverage a fine structure. In view of the above analysis, for Cora, \(s=3.3\) and \(\varepsilon=10^{-4}\), for Citeseer, \(s=2\) and \(\varepsilon=10^{-5}\), for Pubmed, \(s=8\) and \(\varepsilon=10^{-5}\), and for Wiki, \(s=0.5\) and \(\varepsilon=10^{-5}\).  
For other baseline methods, we keep the same parameter settings as the original papers. For AGC [7], we set _max_iter_ to 60. For Deepwalk [2], denote by 10 the number of random walks, 128 the number of latent dimensions of each node, and 80 the path length of each random walk. For DNGR [18], the autoencoder is constructed by three layers, where the hidden layers are 512 neurons and 256 neurons respectively. For GAE and VGAE [4], we construct encoders with 32-neuron hidden and 16-neuron hidden layers respectively, and we use the Adam optimizer to train the encoders for 200 iterations with a learning rate 0.01. For MGAE [5], denote by 0.4 the degree of corruption level \(p\), 3 the number of layers, and \(10^{-5}\) the coefficient \(\lambda\). For ARGE and ARVGE [6], we build encoders with 32-neuron and 16-neuron hidden layers respectively, and their discriminators consist of two hidden layers, which are composed of 16 neurons and 64 neurons, respectively. On Cora, Citeseer, and Wiki, we use Adam optimizer to train ARGE and ARVGE [6] for 200 iterations, where their encoder and discriminator learning rate are both 0.001. On Pubmed, we train ARGE and ARVGE [6] for 2000 iterations, where the learning rates of the encoder and the discriminator are 0.001 and 0.008, respectively.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 5 Main Implementation and Results
### Background and Related Work  
To provide a comprehensive analysis of the potential market and competitive landscape, Table 2 has been developed to systematically compare existing technologies with our proposed solution. This comparison evaluates our solution combined with five leading manufacturers that offer systems for monitoring steam trap performance. The table outlines key parameters, including technological approaches, functional features, target industries,  
Figure 19: Steam leak frequencies between 10 kHz and 40 kHz.  
Figure 18: Steam leak frequencies between 30 kHz and 40 kHz.  
and communication protocols. Notably, all competing devices rely either on battery power (integrated with wireless communication protocols) or wired power (e.g., Modbus).  
Our solution differentiates itself through its innovative power system, which utilizes residual heat as an energy source rather than conventional batteries. This unique approach not only reduces dependency on finite energy sources but also aligns with sustainability goals. Additionally, the integration of long-range wireless communication networks further distinguishes our technology. Unlike traditional short-range wireless systems, such as WirelessHART, or wired solutions that require significant investment in gateways and infrastructure, our approach eliminates these costly requirements. By leveraging energy harvesting and long-range connectivity, our solution offers a more sustainable, cost-efficient, and scalable alternative for industrial monitoring applications, setting a new benchmark in the field.  
These solutions demonstrate the diversity in technologies and protocols available to cater to specific industrial needs, from real-time wireless communication (WirelessHART, BLE, RF) to reliable wired options like Modbus.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 6 Experimental Setup
### Methodology  
Before reporting and discussing our results (see Section 7), different aspects of how we carried out our experiments must be clarified:Both the MST- and SAT-based models were initially pretrained using the weights publicly released by [PERSON] et al. [22] for the Spanish language (see Section 4.1).
* The MST-based system was fine-tuned using the entire speaker-dependent partition of the LIP-RTVE database, which is considered our task-adapted VSR system.
* An SAT-based system using the corresponding speaker-specific training data was independently estimated for each speaker considered in our study, i.e., twenty SAT-based systems were defined.
* When applying the TS-SAT strategy, the previously estimated MST-based system was used for initialisation, which can be considered an adaptation of the model to the task. Then, we followed the same fine-tuning scheme described for the SAT strategy to obtain a TS-SAT-based system for each speaker.
* Regarding the Adapters-based method, different experiments were conducted using the entire speaker-dependent partition training set. However, no acceptable recognition rates were obtained when adapting the Spanish VSR model provided by [PERSON] et al. [22] to the LIP-RTVE task using Adapters. Therefore, we decided to use the MST-based model for initialisation. Similar to the TS-SAT strategy, this can be seen as starting from a task-adapted model. Then, speaker-specific adapter modules were injected and estimated for each speaker considered in our study while keeping the original VSR system backbone frozen, as described in Section 5.2.
* Experiments were conducted using either the training or development set for adapting. However, it should be noted that TS-SAT-based systems were always based on the MST-based system estimated with the training set, while in the second step the training or development set was used depending on the experiment. Conversely, when applying the Adapters-based method, depending on whether we used the speaker-specific training or development set, for initialisation we used the MST-based model estimated with the corresponding dataset from the entire speaker-dependent partition.
* All these VSR systems, as suggested in Section 6.1, were evaluated on the test set corresponding to each of the speakers selected in our study. The MST-based system was the same regardless of the evaluated speaker. Conversely, for the rest of the methods, the corresponding speaker-adapted system was used in each case.
* The LM used in all the tests was the one described in Section 4.2. Unlike the VSR system, it was not re-estimated in any of our experiments.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 2 Methodology and Data
### Aerosol Optical Properties
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \(r_{M}\) [\(\mu\)m] & \(r_{Min}\) [\(\mu\)m] & \(r_{Max}\) [\(\mu\)m] & \(S\) \\ \hline Sulfate & 0.0695 & 0.005 & 0.3 & 2.03 \\ Organic Carbon & 0.0212 & 0.005 & 0.3 & 2.00 \\ Black Carbon & 0.0118 & 0.005 & 0.3 & 2.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Lognormal size distribution parameters for dry OC, BC, and sulfate. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Bin** & \(r_{Min}\) [\(\mu\)m] & \(r_{Max}\) [\(\mu\)m] \\ \hline
1 & 0.03 & 0.1 \\
2 & 0.1 & 0.5 \\
3 & 0.5 & 1.5 \\
4 & 1.5 & 5.0 \\
5 & 5.0 & 10.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Minimum and maximum radius of the five size bins that describe the dry sea salt size distribution. \((1-f^{d})\), respectively (Equation (20)). The dry particle densities for all GOCART species are listed in Table 7.  
\[D_{s}=f^{d}D_{s}^{d}+(1-f^{d})D_{w}=GF^{3}D_{s}^{d}+(1-GF^{3})D_{w} \tag{20}\]  
Aerosol scattering and extinction coefficients, as well as scattering-matrix expansion coefficients for hydrated sea salt, sulfate, and carbonaceous aerosols were calculated with a Mie-scattering code [52] using an effective refractive index from the volume fraction weighted sum of the water refractive index and dry aerosol refractive index from the OPAC database [53]. Dust optical properties were derived by integrating the [PERSON] et al. [54] database, which includes nonspherical effects (see [PERSON] et al. [55] for details). The spectral dependence of the dust complex refractive index was described by merging together three datasets: (1) Shettle and Fenn [56] for wavelengths greater than 1.02 \(\upmu\)m, (2) AERONET observations for 0.44 \(\upmu\)m to 1.02 \(\upmu\)m [57], and (3) OMI aerosol index observations at 354 nm and 388 nm [31]. A fit by a log-power law curve was used to merge the complex refractive index values together [55]. The layer aerosol single scattering albedo and scattering matrix is calculated with an optical thickness weighted sum of the species single scattering albedos, \(\omega_{s,l}\), and scattering matrices, \(\mathbf{B}_{s,l}\):  
\[\omega_{aer,l}=\frac{\sum_{s}\omega_{s,l}\beta_{s,l}m_{s,l}^{d}}{\tau_{aer,l}} \tag{21}\]  
\[\mathbf{B}_{aer,l}=\frac{\sum_{s}\mathbf{B}_{s,l}\omega_{s,l}\beta_{s,l}m_{s,l}^{d}}{ \tau_{aer,l}} \tag{22}\]
# IoTactileSim: A Virtual Testbed for Tactile Industrial Internet of Things Services
## 1 Introduction
### Research Contributions  
The primary contributions of this work are summarized below as:  
* We presented the details of TI in the context of various industrial environments and discussed some emerging applications of the tactile IIoT.
* A hybrid virtual testbed, IoTactileSim, is proposed by combining a network emulator and an industrial robotic simulator to simulate tactile IIoT applications and investigate their performance.
* We designed the IoTactileSim by adopting a hierarchical approach, where the network is divided into two parts; a core and an edge layer. The core layer consists of SDN routers to perform intelligent routing, while the edge layer performs as an intelligent support engine for tactile IIoT services.
* The proposed IoTactileSim identifies the challenges imposed by the tactile IIoT and their strict QoS/QoE requirements. Moreover, it focuses on investigating the communication network parameters (latency, reliability) and other configurations corresponding to the identified requirements.
* We conduct two different experiments in the tactile IIoT environment to evaluate the performance and present the potential of the proposed IoTactileSim testbed.
# Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions
###### Abstract  
Currently Global Navigation Satellite Systems (GNSSs) are the primary source for the determination of absolute position, navigation, and time (PNT) for merchant vessel navigation. Nevertheless, the performance of GNSSs can strongly degrade due to space weather events, jamming, and spoofing. Especially the increasing availability and adoption of low cost jammers lead to the question of how a continuous provision of PNT data can be realized in the vicinity of these devices. In general, three possible solutions for that challenge can be seen: (i) a jamming-resistant GNSS receiver; (ii) the usage of a terrestrial backup system; or (iii) the integration of GNSS with other onboard navigation sensors such as a speed log, a gyrocompass, and inertial sensors (inertial measurement unit--IMU). The present paper focuses on the third option by augmenting a classical IMU/GNSS sensor fusion scheme with a Doppler velocity log. Although the benefits of integrated IMU/GNSS navigation system have been already demonstrated for marine applications, a performance evaluation of such a multi-sensor system under real jamming conditions on a vessel seems to be still missing. The paper evaluates both loosely and tightly coupled fusion strategies implemented using an unscented Kalman filter (UKF). The performance of the proposed scheme is evaluated using the civilian maritime jamming testbed in the Baltic Sea.  
m _Article_  
Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions
## 2 Materials and Methods
### Preparation of the MIP Coatings on the Quartz Chips  
Each molecularly imprinted polymer-coated quartz chip was prepared according to the reported method, with minor modifications [27]. Briefly, 0.102 g of IBA (template) was placed in a glass vial and completely dissolved in 11.3 mL of chloroform (solvent), and then 0.172 g of MAA (monomer), 1.584 g of EGDMA (cross-linker), and 0.016 g of AIBN (initiator) were added. The prepolymer solution was sonicated for 10 min. Subsequently, a silylated chip was inserted into the solution and was deoxygenized with a stream of nitrogen for 10 min. The vial was sealed and placed in an oil bath for polymerization at 50 \({}^{\circ}\)C for 6 h. Next, the chip was pulled out carefully, resulting in a thin layer of the MIP coating on the quartz chip surface. This MIP-coated chip was placed in another empty vial filled with N\({}_{2}\) and then heated to 60 \({}^{\circ}\)C for 24 h to cure the coating. The coated chip was eluted with a 10% (\(v\)/\(v\)) acetic acid methanol solution to remove the template until it could not be detected via a UV spectrophotometer. Five repetitions of the coating procedures were performed. A non-imprinted polymer (NIP)-coated chip was prepared simultaneously according to the same procedure described above but without adding the template (IBA).
# Nanosized Ni/SBA-15 Catalysts for CO\({}_{2}\) Reforming of CH\({}_{4}\)
## 2 Materials and Methods  
### Catalyst Preparation  
To prepare mesoporous nanoparticle S15 silica, 600 mL distilled water and 120 mL concentrated HCl (37%) were mixed by stirring at room temperature in a 1 L glass bottle. Twenty-four grams of Pluronic 123* (MW = 5800, Aldrich) was placed in HCl solution and stirred for half an hour until homogenization. Then, 48 g TEOS (tetraethoxysilane, 98%, Alfa Aesar) was added under stirring at 45 \({}^{\circ}\)C for 24 h. During this time, the clear solution changed to a white suspension. Finally, the solution was aged at 90 \({}^{\circ}\)C for 48 h without stirring. Cooled dispersion was filtered using a glass frit (G3). The solids were washed twice in 200 mL of distilled water and filtered again. The filter cake (S15) was dried at room temperature for 24 h then at 120 \({}^{\circ}\)C for 4 h. The dry S15 was calcined in air at a 50 \({}^{\circ}\)C/h heating rate for up to 550 \({}^{\circ}\)C for 6 h, in a muffle.  
S15 was doped with 5 wt % of Ce and Zn using Ce(NO\({}_{3}\))\({}_{3}\) and Zn(NO\({}_{3}\)), respectively, by the wet impregnation method. The modified support was dried overnight at 120 \({}^{\circ}\)C and calcined at 600 \({}^{\circ}\)C in air for 4 h. The support was co-impregnated by a solution of the metal precursor (Ni (NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O and Co (NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O), with a total loading of 5 wt %. The mole ratio of Co and Ni in the bimetallic catalyst was 1:1 Co/Ni. The prepared catalysts were reduced at 600 \({}^{\circ}\)C for 1 h under a 20% H\({}_{2}\)/Ar atmosphere. The unmodified catalysts are referred to as 5% Co/S15 (Co/S15), 5% Ni/S15 (Ni/S15), and 5% Co-Ni/S15 (Co-Ni/S15). The modified bimetallic catalysts are denoted as 2.5% Co-2.5% Ni/Ce-S15 (Co-Ni/Ce-S15), and 2.5% Co-2.5% Ni/Zn-S15 (Co-Ni/Zn-S15).
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
## 3 Tile-Type Transmitter Module Based on HTCC Technology  
The proposed Ka-band transmitter module can achieve four-channel signal amplification, with independent control of single-channel amplitude and phase. The key requirements of the performance index are listed in Table 1. These design requirements are decomposed from the link analysis of the phased antenna system, and derived from the requirements of the pointing accuracy of the antenna beam. This design uses HTCC packaging to integrate the active chips on a substrate and transmit signals on different layers of the HTCC substrate. Compared to the traditional single-sided configuration scheme, the new scheme employs the double-sided configuration. On the one hand, it can reduce loss and noise and improve reliability. On the other hand, it makes the component compact in size, light in weight, and flexible in shape. This design requires research on  
Figure 2: The schematic diagram of tile-type architecture.  
multiple technologies such as microwave multi-layer substrate production technology, device packaging, and inter-layer vertical interconnection and signal transmission.  
The HTCC substrate material exhibits excellent resistance to high temperatures, corrosion, and thermal shock, ensuring exceptional stability and reliability. Since multi-layer co-sintering can be achieved, the HTCC process can achieve high-density wiring, thereby improving device performance. Furthermore, HTCC ceramic materials have low insertion loss and high-quality factors, making them suitable for high-frequency applications. Based on the advantages above, the HTCC packaging is used in the proposed design. The characteristics of the HTCC material are listed in Table 2.
# The Failure Law and Combined Support Technology of
###### Abstract  
In order to effectively address stability control technology issues of soft surrounding rock roadways in deep mines. This study analyses the deformation and failure characteristics of the surrounding rock of a \(-\)962 m horizontal track roadway with original support conditions based on a severe deformation case that occurred in a mine. Upon establishing a mechanical model of surrounding rock failure zoning for circular roadways, which is based on the relationship between the stress-strain curve of soft rocks and the secondary stress distribution and strength of surrounding rock, this study explores the influence of rock strength indicators, disturbance degree, and support resistance on the stress distribution of the surrounding rock. The failure or instability mechanism of high-stress soft and weak surrounding rock is revealed on this basis. A multi-stage strengthening combined support technology is proposed, which consists of \"high-strength prestressed anchor bolt (cable) supports as the core, deep and shallow hole groutings as the foundation, bottom angle, and floor anchorage grouting reinforcements as the key.\" Moreover, numerical simulation and engineering practice optimize and verify the support scheme. The results show that after adopting the multi-stage strengthening combined support technology, the deformation of the surrounding rock of the roadways was only 12.6-14.3% of that under the original supporting parameters, and the deformation rate was still less than 0.2 mm/d even after 40 days. The proposed surrounding rock support method realizes the stability control of the roadway, which also has specific reference significance for similar projects.  
high-stress; soft-rock roadway; combined support; numerical simulation; engineering practice +
Footnote †: journal: Applied sciences
## 3 Measurement Set-Up and Prototype
### Software Scheme  
The software scheme is shown in Figure 3, where the different functions are distributed in modules supported by a set of technologies. This software allows the capture, collection and post-processing of the signals at the physical, network and application levels.  
The functions of the software are defined as follows:  
* Measurement software: This section is divided into three modules whose purpose is to request network parameters, and capture and save them.
* Radio and GNSS parameter request module: Developed in Python. It is responsible for requesting, in 1-second intervals, the radio and mobile communication parameters and GNSS parameters, such as speed and geographical coordinates.
* Data capture module: using Wireshark. The wired interface captures the network, transport and application data, whereas the Wifi interface captures the radio and GNSS data.
* Measurement software control module: Developed in '.cmd' batch programming, it is responsible for synchronising the two previous modules and saving the data.
* Processing software: Divided into two modules whose purpose is to transform the data and check the quality of the network for signalling services.
* Data processing module: Developed in Python, it is responsible for transforming the files and filtering the resulting files from the measurement software ('.pcapng') to others ready for analysis.  
Figure 2: Prototype and measurement set-up architecture.