esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Ultrasonic Experimental Setup  
Ultrasonic testing is a non-destructive technique that utilizes high-frequency sound waves to evaluate the internal structure, integrity, and physical properties of materials. In this work, the information about the longitudinal and shear velocities in the printed PLA specimens was collected using the UT technique. For this purpose, the experimental setup included a RITEC RAM-5000 high-power pulse generator, a RITEC Diplexer, a PXI chassis with an embedded digitizer, a PC for data visualization and recording, and UT transducers. UT signals were measured using an A103S-RM 1 MHz longitudinal wave transducer and two V153-RM 1 MHz shear wave transducers. A frequency of 1 MHz was chosen as the most suitable frequency for a specimen of the given dimensions and material. The schematic of the experimental setup is depicted in Figure 2.  
Figure 1: Printed specimens. Arrow shows the build direction.  
PLA materials have a relatively high attenuation coefficient, which affects the intensity of the ultrasonic signal travelling through the material. Therefore, two different measurement approaches were utilized in this work: pulse echo and through transmission. The pulse-echo approach was used for measuring the longitudinal speed of sound. In this method, a transducer acts as both a transmitter and as a receiver. The through transmission approach was used for measuring the shear speed of sound. In this approach, one transducer is used as a transmitter and the second one as a receiver.  
During the tests, specimens with transducers were placed in clamps (see Figrue 2 insert for details) to maintain tight and consistent contact. Consistency in the conducted experiments is essential, considering that various environmental factors such as contact pressure and the thickness of the couplant's layer could significantly influence the test results. In order to resolve this, the signal consistency was maintained by controlling the ultrasonic signal levels. The ultrasonic intensity was adjusted by tuning the transducer contact pressure to yield the same level of horizontal cursors within the software interface. These cursors were adjusted up to the maximum and minimum amplitudes of the transmitted signal, which was the same for all measurements. The received signal was scaled in such a way that the maximum and minimum amplitudes of the first echo reached these cursors, as depicted in Figure 3. Ultrasonic measurements were taken three times for each specimen; a constant level of contact pressure was maintained during the experiment to maintain signal consistency, as described above.  
Figure 3: The process of adjusting the signal.  
Figure 2: Experimental setup.
# EANT: Distant Supervision for Relation Extraction with Entity Attributes via Negative Training
## 4 Experiments
### Ablation Study  
The proposed EANT model mainly consists of the sentence encoder, entity attribute encoder and the negative training process. In order to verify the effectiveness of different modules of our model, we designed two variants of the model to perform the ablation experiments.  
Figure 4 depicts the PR curves of the different variants of the model. **EANT-woEA** is the method without the entity attribute encoder, which only uses the sentence encoder to obtain the sentence representation. **EANT-woNT** is the method without negative training, which obtains the final relation classifier by positive training only. From the figure, we can observe that removing any one module will result in a loss of model performance. This illustrates the positive effect of incorporating entity attribute information and negative training on model performance improvement.
###### Abstract  
The THz wireless transmission system based on photonics has been a promising candidate for further 6G communication, which can provide hundreds of Gbps or even Tbps data capacity. In this paper, 144-Gbps dual polarization quadrature-phase-shift-keying (DP-QPSK) signal generation and transmission over a 20-km SSMF and 3-m wireless 2 \(\times\) 2 multiple-input multiple-output (MIMO) link at 500 GHz have been demonstrated. To further compensate for the linear and nonlinear distortions during the fiber-wireless transmission, a novel joint Deep Belief Network (J-DBN) equalizer is proposed. Our proposed J-DBN-based schemes are mainly optimized based upon the constant modulus algorithm (CMA) and direct-detection least mean square (DD-LMS) equalization. The results indicate that the J-DBN equalizer has better bit error rate (BER) performance in receiver sensitivity. In addition, the computational complexity of the J-DBN-based equalizer can be approximately 46% lower than that of conventional equalizers with similar performance. To our knowledge, this is the first time that a novel joint DBN equalizer has been proposed based on classical algorithms. It is a promising scheme to meet the demands of future fiber-wireless integration communication for low power consumption, low cost, and high capacity.  
Terahertz-band; multiple-input multiple-output; polarization multiplexing; seamless integration; Deep Belief Network +
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# Precursory Tidal Triggering of the 2011 Mw 9.0 Tohoku Aftershocks
## 2 Data and Methods  
### Data Selection  
Earthquake data of the 2011 Tohoku event are available in the USGS earthquake database ([[https://earthquake.usgs.gov/earthquakes/search/](https://earthquake.usgs.gov/earthquakes/search/)]([https://earthquake.usgs.gov/earthquakes/search/](https://earthquake.usgs.gov/earthquakes/search/)), accessed on 1 July 2022). To fully analyze the correlation between all aftershocks and Earth tides, an earthquake catalog from 11 March 2011 to 10 April 2011 was selected in a rectangular region between latitudes 34\({}^{\circ}\) N-41\({}^{\circ}\) N and longitudes 138\({}^{\circ}\) E-146\({}^{\circ}\) E. In this area, 3058 (M > 4.0) earthquakes have been recorded by the USGS in the month following the mainshock. The spatial distribution and magnitude-time (M-t) maps of the selected seismic sequences are shown in Figures 1 and 2, respectively. Seismic activity was still occurring at a higher level one month after the mainshock. The catalog is subdivided into several subsets using a depth of 30 km and a magnitude of 5 as thresholds (Table 1) to test tidal triggering variations that would depend on the earthquake magnitude and depth. The number of earthquakes included in each subset is listed in Table 1; these numbers are deemed sufficient to run the tidal statistical test.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number (N)** & **All Depths (D)** & **0 \textless{} \textless{} 30 km** & \(D\geq\) 30 km \\ \hline All magnitudes (\(M\)) & 3058 & 1146 & 1912 \\ \(0<M<5\) & 2494 & 874 & 1620 \\ \(M\geq 5\) & 564 & 272 & 292 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of earthquakes in different subsets.  
Figure 2: M–t plot of the Mw 9.0 earthquake sequence in Japan in 2011.
## 5 Results
### System SINR Improvement at \(-5\) dB Injection Level  
In Figure 5, the SINR performance improvement of DDA-FRESH over RLS-FRESH, for the first 10,000 samples, is presented at SNR 5 dB and injection level \(-5\) dB. Clearly from Figure 5, we can see that the decoding operation of the NOMCR scheme using the auto-encoder based FRESH equalizer outperforms the conventional RLS-FRESH scheme. At SNR 5 dB and injection level \(-5\) dB, the performance of the DDA-FRESH significantly improves after 100 samples. This is attributed to the enhancement in cyclostationarity recognition performance due to iterative interference estimation and successive cancellation procedure.  
The achievable capacity advantage of LDM based NOMCR system over conventional orthogonal multiple access (OMA) based cognitive transmission is presented in Figure 6 above. As evident, the use of NOMCR scheme clearly outperforms the conventional cognitive orthogonal access scheme.  
Figure 5: SINR vs total number of samples at SNR 5 dB and injection level \(-5\) dB, demonstrating comparative performance of RLS-FRESH and DDA-FRESH equalizers with regards to successive interference cancellation based NOMCR decoding.  
Figure 6: Achievable capacity comparison between conventional OMA and proposed FC-F-OFDM-on-OFDM based LDM system.
## 3 Methodology  
### Ground Testing  
Before launch, the residual magnetic field of the satellite was not tested. As the magnetic field measured by the magnetometers had high oscillations during orbit, we decided to measure the residual magnetic field of the satellite. This test was conducted within the magnetic shield chamber belonging to ISAS/JAXA (Institute of Space and Astronautical Science/Japan Aerospace Exploration Agency) located in Sagamihara, Japan. It is challenging to perform this experiment in a normal environment, due to the Earth's magnetic field as well as disturbance magnetic fields. Therefore, the experiment was conducted in the magnetic shield room. Figure 5 shows the test setup.  
Different operation modes were provided as input to the Birds-3 FM (Flight Model) backup, and magnetometer readings inside the CubeSats were checked, the difference between the FM and the FM backup being that no solar cells were attached to the FM backup. Normal sampling (NS), high sampling (HS), and ADCS modes were executed. The normal-sampling mode was the nominal mode of the CubeSats. In high-sampling mode, the sensor data were collected every 5 s. The residual magnetic field after deducting the ambient magnetic field in the shield room was as follows: X: 16,570.8 nT (165.70 mG), Y: 1070.8 nT (10.70 mG), and Z: 2283.3 nT (22.83 mG).  
We have mentioned three calibration parameters, namely offsets, non-orthogonality errors, and gains. Offsets are affected by residual magnetic fields. Non-orthogonal errors are due to manufacturing errors. Gains are treated as the sensitivity of the magnetometer. Among the parameters, only the offset values are affected due to the residual magnetic field inside the satellite.  
Figure 6 shows the experimental results. Blue indicates the X-axis of the magnetometer; orange indicates the Y-axis of the magnetometer; and gray indicates the Z-axis of the magnetometer.  
Figure 5: Residual magnetic field test setup.
## 3 Methods
### Evaluation  
To evaluate the coherence of topics generated by embedding models, we employed _Normalized Pointwise Mutual Information (NPMI)_, a widely used metric for measuring word associations within topics. NPMI captures the semantic coherence of a set of words that form a topic, based on how frequently these words co-occur in the corpus. It ranges from (complete negative association) to 1 (complete positive association), where higher values indicate better coherence among words in a topic.  
The formula for NPMI is given by:  
\[\text{NPMI}(w_{i},w_{j})=\frac{\log\frac{P(w_{i},w_{j})}{P(w_{j})P(w_{j})}}{- \log P(w_{i},w_{j})}\]  
where - \(P(w_{i},w_{j})\) is the probability of words \(w_{i}\) and \(w_{j}\) co-occurring within a specific window in the corpus. - \(P(w_{i})\) and \(P(w_{j})\) represent the individual probabilities of occurrence for words \(w_{i}\) and \(w_{j}\).  
NPMI serves as an important measure in determining whether the words grouped under a topic make sense as a coherent, interpretable concept. This metric was crucial in our analysis, allowing us to assess the quality of topics generated by different embedding models on both the iVoting and Join platforms.  
In addition to coherence, _topic diversity_ is another essential factor that ensures the uniqueness of topics in a model. We evaluated diversity by examining the top 25 words of each topic cluster. To quantify diversity, we ensure that each cluster presents words that are not highly repetitive across different topics. The diversity score can be defined as the proportion of unique words across all topics, which can be calculated using:  
\[\text{Diversity}=\frac{\text{Number of unique words across all topics}}{\text{Total number of words across all topics}}\]  
A higher diversity score indicates that the topics generated by the model are more distinct from each other, ensuring that the top words in one topic do not overly overlap with those in other topics. This metric is particularly useful in ensuring that the clustering provides a rich, varied representation of the underlying textual data, which is essential for e-participation platforms to capture a wide range of public opinions and themes.  
Both NPMI and diversity measures were applied to evaluate the quality of topics generated by the embedding models, providing insights into the balance between coherence and topic uniqueness in the results.
## 3 Results and Discussion
### Characterization of Aqueous Vine Leaves Extract
#### 3.1.2 Total Flavonoids Content from the Aqueous Vine Leaves Extract  
The regression equation of the standard calibration curve, absorbance versus concentration of quercetin, was y = 0.001\(\cdot\)x + 0.002 (R\({}^{2}\) = 0.978), wherein y represents the absorbance and x represents the concentration of quercetin used as standard. The total flavonoid content of the extract was expressed as mg equivalent quercetin per gram of solid extract (mg QE/g solid extract). The total flavonoid content of the vine leaves solid extract was 29.85 mg QE/g solid extract.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 5 Food Safety and Traceability in the Wine Industry
### Detecting and Managing Microbiological Risks
#### 5.2.1 Applications in Food Safety and Risk Management  
AI applications extend beyond operational efficiencies to play a crucial role in enhancing food safety and managing microbiological risks. For instance, AI systems can detect mycotoxins and other contaminants, ensuring that the wine produced meets stringent safety standards. The use of machine learning models for predicting contamination events allows for proactive management and mitigation of risks, thereby safeguarding consumer health [13]. [PERSON] et al. conducted a bibliometric review on artificial intelligence in food safety, highlighting its capability to detect contaminants like mycotoxins and predict contamination events [13].  
Furthermore, the study by [PERSON] et al. (2022) emphasized the broad applications of AI in food safety, including risk prediction, monitoring, and optimization throughout the supply chain [12]. The authors noted that AI could significantly enhance public health systems by providing early warnings of outbreaks and identifying sources of contamination.  
Moreover, AI technologies can integrate data from various points in the supply chain to provide comprehensive traceability from vineyard to consumer. This capability is vital for maintaining consumer trust and meeting regulatory requirements. AI-driven traceability systems ensure that every bottle of wine can be traced back to its origin, improving transparency and accountability in the production process [126]. [PERSON] et al. (2021) investigated intelligent supervision systems for food traceability, emphasizing how integrating data from multiple points in the supply chain can enhance transparency and accountability [126].
## 4 Application to an Additive Manufacturing Benchmark Test
### Validation of the Finite Element Model  
Using the baseline parameters, the simulation results were validated using two different analyses. In the first case study, the normal residual elastic strains were compared with the experimental results at two specific locations (z = 9.536 and 2.216) in the bridge. Figure 9a-c show a comparison of normal strains in the x, y, and z-directions, respectively, at the line z = 9.536, whereas Figure 10a-c show that comparison at z = 2.216. The simulation results at both values show a good correlation with the experimental results. It is essential to mention here that the experimental strain results at z = 9.536 were extracted only up to the 60 mm length of the bridge. The relative L2 norm error values for the x, y, and z-direction strains at z = 9.536 were 0.4041, 0.6202, and 0.7820, respectively. The small difference in the results may be due to the different scan strategies used in the experimental and numerical simulations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mesh Size (mm)** & **Relative L2 Error Norm** & **Computation Time** \\  & & **(Minutes)** \\ \hline
0.4 & 0.9924 & 45 \\
0.3 & 0.9642 & 60 \\
0.2 & 0.8588 & 155 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discrepancy in different mesh size results.  
Figure 8: Mesh convergence study.  
For the second validation case study, we analyzed the deflection of a partially separated bridge from the baseplate as shown in Figure 11, in which all 12 bridge legs were separated from the substrate. The partially separated bridge was then allowed to move upwards without any external force, except for the internal residual stresses. The deflection of the bridge in the z-direction was measured at 11 ridges on the bridge's top surface. In Figure 12, we show the comparison of the deflection results from a simulation and an experimental study. The relative L2 error norm between the simulation and experimental results is 0.0629, proving a good correlation between the results.  
Figure 10: The (**a–c**) represents the comparison of experimental and simulation residual strain at z = 2.216 in the x, y and z-directions respectively.  
Figure 9: The (**a–c**) represents the comparison of experimental and simulation residual strain at z = 9.536 in the x, y, and z-directions respectively.
## 1 Introduction
### Remote Sensing for Elastic Wave Detection with Laser Doppler Vibrometer  
As for remotely detecting elastic waves propagated through concrete, non-contact acoustic systems with a laser doppler vibrometer (LDV) for detecting signals have been reported [13; 14; 15]. These research approaches provide remote and rapid operations for inspection works based on the elastic wave method. In this study, a laser doppler vibrometer is also introduced, in addition to conventional accelerometers, to detect the elastic wave in concrete. The signal output from LDV is generally a continuous analog voltage that is directly proportional to the velocity component at targeted surface along the laser beam direction. The system specification of LDV used in this study is given as digital velocity decoder with 0.5 Hz-22 kHz frequency band and velocity resolution is \(<\)0.02 (\(\upmu\)m/sec)/\(\upmu\)Hz. The signal detectable distance of LDV is from 0.1 to 30 m. This enables the non-contact vibration measurements of concrete surface and obtain the signal information required for the SIBIE analysis in addition to the conventional contact signal acquisition system with accelerometers.
# IW-NeRF: Using Implicit Watermarks to Protect the Copyright of Neural Radiation Fields
## 2 Related Work
### Digital Watermarking for 3D Data  
Within the realm of 3D digital watermarking, traditional methods involve embedding watermarks in various representations of 3D models, such as point clouds [21], voxels [22], or triangular meshes [23], through transformations like translation, rotation, scaling [24], and parameter modifications [25]. Notably, [PERSON] et al. [26] introduced a technique employing layered artifacts of 3D-printed objects for watermarking, while [PERSON] et al. [27] enhanced robustness by leveraging wavelet transform to amplify grid saliency. Recognizing the complexities of traditional 3D digital watermarking approaches, researchers have turned towards integrating deep learning techniques in this domain. [PERSON] et al. [28] pioneered a deep learning-based 3D mesh watermarking network, introducing a more versatile framework for 3D mesh watermarking. Building upon this, [PERSON] et al. [29] successfully embedded messages within 3D meshes and retrieved them from 2D renderings. However, existing methodologies predominantly cater to explicit 3D models, posing challenges when applying them to neural radiance field (NeRF) models lacking specific 3D structures, thereby limiting their efficacy in ensuring copyright protection for NeRF representations.
## 2 Overall Analysis Approach and Implemented Methods
### Structural Sizing
The buckling strength, i.e., the terms on the right-hand side of the Equation (22) second and third lines, is derived from the design-by-formula approach given by the \"Alternative Rules\" of ASME Boiler and Pressure Vessel Code [39] and is the most recurring formula for the tank geometries considered here. The whole set of formulas obtained by varying geometric parameters, such as thickness-to-radius and length-to-radius ratios, was implemented in the design procedure but is not reported here for the sake of conciseness; the reader interested in such details can refer to Section 4.4.12 of [39]. * If any of the previous verifications is not fulfilled, the thickness \(tc\) is recalculated within an iterative loop in which masses and inertial loads are updated at each step. The following aspects are worth mentioning:
* Equation (19) as well as the first line of Equation (22) are valid for cylindrical tubes with thickness-to-radius lower than 0.5, whereas Equation (17) is valid for endcaps with thickness-to-radius lower than 0.356. The fulfilment of these constraints is checked at the end of the sizing procedure;
* The third line of Equation (22) is valid for values of \(\frac{R_{str}}{tc}\leq 1000\) ([39]), higher values require a design-by-analysis approach to assess adequate protection against buckling;
* A buckling verification for endcaps was not implemented since they are not subject to compression, as usually happens for tanks that undergo external pressure loads;
* According to [26], hydrogen tanks need to be verified also at the burst condition, defined as a load case in which only a pressure load equal to twice the limit design pressure (\(\Delta p\)) is applied. In such condition, it is required that the tank can carry the load without catastrophic failures. In the procedure adopted here, Equation (17) introduces a safety factor of 3.5 between the maximum allowable stress and the ultimate strength of the material, hence burst condition is self-verified;
* For the case of aluminium alloy AA2219, the authors of [26] report a fatigue analysis for a LH\({}_{2}\)-propelled transport aircraft assuming 50,000 service hours corresponding to 10,000 ground-air-ground cycles. For such case, a 172 MPa limitation to circumferential stress of a tank's skin results in a life reduction factor of 4, i.e., a fatigue life of 40,000 cycles. In the case considered here, taking AA2219 data in Table 4 into account, the allowable value resulting from Equation (17) is 145 MPa, well below the limit indicated above.