esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Health Applications
#### 2.2.1 Patient Wearable Monitoring  
Health monitoring applications can be combined with wearable hardware with embedded biomedical sensors that provide the patient's health status in a remote environment or within a healthcare facility.  
A healthcare solution of this type, is examined in [13]. It uses real-time sensors incorporated in smartphones along with a barcode system to provide personalized medicine care assistance. The mechanism developed, performs Electrocardiogram (ECG) monitoring in real time. Also, the monitoring of the blood glucose level, blood pressure, and several kinds of diagnostics could be possible too, by using real-time sensors. This system developed is illustrated in Figure 7.  
An alert portable tele-medical monitor system (AMON) is proposed in [14], aiming to provide continuous monitoring for high-risk cardiac/respiratory patients. The system collects multiple vital signs, detects multi-parameter medical emergencies and is connected to a cellular telemedicine center (TMC). This system uses a wrist worn device, to monitor vital parameters of patients in order to provide an integrated picture of their health condition.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Design, Analysis, and Simulation of RC-NB-LDPC Codes
#### 4.1.3 Simulation of RC-NB-LDPC Codes  
This section performs performance simulation and analysis of the RC-NB-LDPC code. The parameters are as follows: the decoding algorithm is a Mixed-LOG-FFT-BP decoding algorithm, the maximum number of iterations is 20, the modulation mode is BPSK, and the channel is an AWGN channel. Monte Carlo simulation was performed and the following results were obtained.  
Figures 15 and 16 simulate the bit error rate (BER) performance of the RC-NB-LDPC codes with different code rates. As can be seen from the figure, the constructed LDPC code achieves better waterfall performance in a larger code rate range. Moreover, the constructed RC-NB-LDPC code achieves a performance gain of about 0.2-0.5 dB compared to the LDPC code in the DVB-S2 standard.  
Figure 17 simulates the frame error rate (FER) performance of the RC-NB-LDPC codes with different code rates. It can be seen that the RC-NB-LDPC code achieves better anti-interference performance in a larger code rate range.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Code Rates**} & \multicolumn{2}{c}{**Thresholds under AWGN Channel (dB)**} \\ \cline{2-3}  & **DVB-S2 Scheme** & **RC-NB-LDPC Codes** \\ \hline
7/8 & \(\_\) & \(\_\) & 2.87 \\ \hline
6/7 & \(\_\) & \(\_\) & 2.81 \\ \hline
5/6 & 2.86 & 2.72 \\ \hline
4/5 & 2.70 & 2.59 \\ \hline
3/4 & 2.58 & 2.41 \\ \hline
2/3 & 2.38 & 2.12 \\ \hline
1/2 & 2.02 & 1.80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Threshold Predictions of Designed Codes.  
Figure 14: EXIT chart of the designed degree distributions.  
Figure 18 simulates the average number of iterations of the RC-NB-LDPC code with a code rate of \(1/2\). As can be seen from the figure, the higher the \(E_{b}/N_{0}\), the smaller the average number of iterations. In addition, the RC-NB-LDPC codes have low decoding delay and are suitable for real-time communication scenarios.  
Figure 16: Designed 7/8 rate code performance.  
Figure 15: BER simulation comparison result.
# Correlation of Microstructure and Mechanical Properties of Metal Big Area Additive Manufacturing
## 2 Methods  
### Compensation Based Controls Approach  
The ORNL closed loop control methodology is based on a compensation-based approach to combat the dynamic nature of welding and provide uniform flat beads and layers of a desired height. The system is equipped with three different control strategies. The first strategy aids in facilitating the part location, the second strategy retains the optimal stand-off distance, and the third strategy is a feedback loop that adjusts deposition rate.  
Since GMAW is sensitive to the wire-workpiece distance, or standoff, it is critical to start the arc at the optimal vertical distance. To accomplish this first control strategy, the robot arm positions the welding head over the build zone and slowly lowers the weld head down until the welds' electronics can sense and establish a low current short duration arc between the wire stick-out and the workpiece. The wire stick-out is a constant in the control of the initiation of each weld pass. After the short time arc positioning sequence, the vertical distance of the weld head is recorded in the G-code software. The weld head retracts, and, on the next approach, the deposition arc is established.  
The second control strategy retains the optimal standoff distance over the whole deposition path. The deposition amperage variations are monitored by the welding system, and the robot control system automatically adjusts standoff based on the variations. This amperage signal is used to calculate the actual distance to the part. The controller adjusts the desired path in the vertical direction in real time to retain an optimal standoff distance based on current feedback. The two strategies combined still do not guarantee bead flatness or precision.  
The third control strategy is used to actively sense the feedback signal from the welding system and adjust the linear deposition rate (wire feed rate, kg per unit length). By doing so, the regions that are overbuilding receive less material, while depressed regions receive more, resulting in a flat uniform surface at the desired height. Figure 1 is a pictorial representation of the compensation-based approach using the three control strategies. The blue line represents the resulting geometry from uncompensated welds, showing the dynamic nature of welding that produces a rough surface. The red line represents the resulting geometry from the compensation-based approach in which the weld layer height remains constant and uniform. Figure 2 shows two walls built using an industrial welding robot the ORNL MBAAM closed loop control system [28]. Figure 2b shows a wall built without using the compensation-based approach and shows the rough, nonuniform layer height, as well as weld pool flow, off the sides of the wall. Figure 2c shows a wall that was built using the compensation-based approach. Here, each layer is flat and uniform, with a reduced surface roughness. The compensation-based approach exhibits a uniform build quality.
## 2 SITHON  
### SITHON project Overview  
The SITHON Project was funded in 2003 under the Operational Program of Competitiveness of the Greek Ministry of Development-General Secretariat for Research and Technology (Action 4.5.1: Natural Environment and Sustainable Development). The project lasted three years and was initiated in July 2003. The main objectives of the project were to foster collaboration between the private service sector, technological partners and research community, to demonstrate the synergy of evolving technologies (both terrestrial and airborne) for increasing the information content of data collected, and enhancing the timeliness in data collection, processing and transition towards an effective detection, monitoring and management of wildfire suppression actions.  
It is well known that time is a crucial parameter in fire combating and fire containment. The level of efficiency depends on the availability of fire fighting resources, the firemen endurance, as well as the knowledge of the terrain, the fire location and the ways to access to it. For this purpose the SITHON project applied remote sensing systems of terrestrial and airborne detection sensors for the localization, notification and monitoring of the active forest fires. The whole SITHON system comprises a wireless network of _in-situ_ optical cameras, and an airborne fire detection system based on a fully digital thermal imaging sensor. The network of sensors is linked to an integrated GIS environment in order to facilitate the fire fighting management and support the decision making process during crisis. The GISdata bases incorporates qualitative and quantitative information layers needed to estimate the fire risk, such as the vegetation types and quantity of fuel matter, the road network for accessing the active fires, the area's morphology, the sensitive and endangered locations (settlements, camps, folds, archaeological sites, etc), the high danger infrastructures (fuel stations, flammable materials, industrial areas, etc), the natural or artificial water reservoirs, etc. The platform of SITHON includes a Crisis Operating Centre, which receives information in the form of images and data from the wireless sensor detection systems, displays it on wide screen monitors and analyses it to derive the dynamic picture of fire evolution. In the following sections a detail description of the airborne thermal imaging part of the SITHON system is given.
## 5 Communication Performance in a Multi-Luminaire Scenario  
In this section, we apply the results obtained from Section IV to a typical indoor scenario, shown in Figure 11. This represents a room that is 3 m in length, 3 m in width, and 2.5 m in height is considered. The origin, \(O\), is on the floor at the center of the room. The receiver is placed at a table height of 0.7 m from the floor and is pointing upwards. Four luminaires are placed around the center of the ceiling. They are located at \((0.75\,\mathrm{m},0.75\,\mathrm{m},2.5\,\mathrm{m})\), \((-0.75\,\mathrm{m},0.75\,\mathrm{m},2.5\,\mathrm{m})\), \((-0.75\,\mathrm{m},\,-0.75\,\mathrm{m},2.5\,\mathrm{m})\), and \((0.75\,\mathrm{m},\,-0.75\,\mathrm{m},2.5\,\mathrm{m})\).  
The block diagram of the transmitter and the receiver in a multi-luminaire scenario is shown in Figure 12. The data for each luminaire is modulated using an ACO-OFDM modulator. ACO-OFDM with 4-quadrature amplitude modulation (QAM) is used for all luminaires. For each luminaire, the data is first modulated using 4-QAM and then mapped to the odd subcarriers of the OFDM signal. To make the OFDM signal real, Hermitian symmetry is imposed on the input to the inverse fast Fourier transform (IFFT). The signal at the output of the IFFT is then clipped at zero to create a unipolar signal. This discrete signal is converted to a continuous signal using a DAC. Time division multiple access (TDMA) is used to coordinate between luminaires by assigning a specific time slot to each luminaire in which they transmit their signal. This is important to avoid multiple access interference. Finally, the continuous signal is used to directly modulate the intensity of the corresponding LED luminaire.  
Figure 10: SNR in dB versus QADA location in an area equipped with a single transmitter with \(m=1\) and with the receiver held at an angle (tilted by \(20^{\circ}\) along the axis and \(5^{\circ}\) along the axis) with a FOV-C of \(63.4^{\circ}\) for (**a**) each quadrant; (**b**) the combined signal using MRC.  
Figure 11: 3-D view of the room configuration with four luminaires and a QADA receiver.  
Figure 12: System block diagram of the transmitter and the QADA receiver using ACO-OFDM as the modulation in multi-luminaire scenario and using TDMA as the multiplexing scheme.  
At the receiver, the quadrant PD converts the optical signal into an electrical signal. The signal from each luminaire is recovered during the corresponding time slot. After estimating the channel gains for a given luminaire, the four signals from that luminaire received during its time slot are equalized and then combined at the output of the FFTs using MRC. Finally, the transmitted data is recovered from the odd subcarriers and then decoded using a maximum likelihood decoder.
## 5 Discussion
### Significance Analysis of the Mean Entropy of the Datasets  
In this study, the Mann-Whitney U test [47] was employed to determine whether a statistically significant difference exists between the entropy distributions of the two datasets. The Mann-Whitney U test, also known as the Wilcoxon rank-sum test, is a non-parametric method used to compare the distributions of two independent samples. Unlike parametric tests, it does not assume a normal distribution of the data, making it particularly suitable for analyzing the entropy characteristics of InSAR coseismic observations. The test works by ranking all combined observations and calculating a U-statistic based on the  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coseismic InSAR Dataset** & **Mean Entropy** & **Standard Deviation of Entropy** & **Dataset Size (After DA)** \\ \hline Dataset for this study (A) & 7.58 & 0.25 & 14,000 \\ \hline [PERSON] et al. [21] dataset (B) & 7.55 & 0.42 & 184 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of information entropy statistics and scale for two coseismic InSAR datasets.  
sum of ranks within each group. The statistical significance of the observed difference is assessed using the corresponding \(p\)-value. If the \(p\)-value is below a predefined significance level (commonly 0.05), the null hypothesis is rejected, indicating a statistically significant difference between the two distributions.  
The results yielded a Mann-Whitney U-statistic of 1,051,840 and a \(p\)-value of 0.000006 (<0.05), indicating a statistically significant difference between the entropy distributions of the two datasets. This suggests that the two datasets exhibit fundamentally different information characteristics, with Dataset A containing significantly moreoseismic deformation information than Dataset B.  
This comparison is justified, as Dataset A comprises a significantly greater number of earthquake events and associated deformation scenarios (353 real seismic observations) than Dataset B, which contains only 32 observations. Moreover, Dataset A includes all seismic events present in Dataset B. Therefore, the observed statistical differences can be attributed not to fundamentally distinct deformation characteristics but to the greater diversity and information content present in Dataset A.
## 2 Deep Learning Model and Embedded Systems
### Nvidia AI Embedded Systems  
Deep learning can be sufficiently performed through GPU computation in a desktop environment. However, IoT and embedded systems are widely used because they have the advantage of relatively inferior processing performance and accuracy when compared to desktop environments, but have a small size and use less power. Therefore, Nvidia sells Jetson platforms, an embedded system. Jetson platforms are largely divided into Nano, TX2, and AGX Xavier. Similar to desktops, hardware performances such as those for CPU, GPU, RAM, and HDD are better, and the more features it provides, the more expensive it is [11]. Table 1 shows a comparison of the specifications of Jetson products.  
The Jetson Nano platform is a small AI computer that runs a deep learning neural network in parallel and has the performance and power efficiency required to process data from multiple high-resolution sensors simultaneously. It is the perfect level of platform to add advanced AI to embedded products and is used in embedded IoT applications, including network video recorders, home robots, and intelligent gateways with full analysis capabilities.  
The Jetson TX2 delivers unparalleled speed and power efficiency in embedded AI computing devices, enabling true AI computing through a wide range of standard hardware interfaces. The TX2 embedded module for installing AI applications on Edge Devices comes in three versions: TX2 (4 GB), TX2 (8 GB), and TX2i (Industrial). In particular, even if a TX1-based platform is purchased, it has the advantage of being able to migrate to TX2 4 GB.  
The Jetson AGX Xavier is an embedded system specifically designed for autonomous machines and has the best hardware performance on the market today. It can easily process sensor data and run AI software, and provides the best performance and power efficiency among Jetson platforms. AGX Xavier provides the best performance among Jetson platforms for high-speed computing, energy efficiency, and AI-based inference functions.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 4 Experiment
### Classification  
Regarding the task of rotation invariance, our network also demonstrates its strong performance. We conduct experiments on point cloud classification using the ModelNet40 dataset and compare the performance of our model with other networks under varying rotation conditions. The classification task inherently reflects the requirement of rotation invariance, and it serves as a valuable test for evaluating network performance. Table 1 presents the classification results of each model under different rotation settings.  
Due to the effective learning of intrinsic features in point clouds by vector neurons, our network not only exhibits the highest accuracy in non-rotated states but also demonstrates excellent recognition rates in rotated states. The best recognition results are achieved under rotations in SO(3), indicating the superior performance of our network and its robustness to rotated point clouds. The network's performance is minimally impacted by rotated point clouds, showcasing its effectiveness in handling rotational variations.  
Figure 7: Randomly rotated point cloud and their corresponding output results in 3D space. This figure shows the comparison between the pose output by the network and the origin point cloud for different point clouds rotated randomly under SO(3).
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 3 Methodology
### Feature Fusion Layer  
To integrate word-level information with the character-level information from BERT, inspired by the work in [32], we introduce an additive attention mechanism. This mechanism learns weighted combinations, assigning different weights to each vector output by the encoding layer, enabling the model to perform information fusion more flexibly and reduce the interference of redundant information. The architecture of the additive attention mechanism is shown in Figure 3.  
First, two different linear transformation matrices are used to generate the corresponding query (Q) and key (K) matrices, respectively, for the BERT output and the GNN output:  
\[Q=Linear_{Q}(BERT)\]  
\[K=Linear_{K}(GNN)\]  
For each BERT input sequence, we multiply the query matrix Q by a learnable parameter vector \(W_{a}\) to calculate the attention weight for each token:  
\[A_{weight}=Q\cdot W_{a}\]  
The attention weight represents the relevance of each token in the input sequence to the global context. Here, \(A_{weight}\) is the attention score for each token. Next, we normalize and scale the attention scores to obtain the attention weights A:  
\[A=Softmax\left(\frac{A_{weight}}{\sqrt{d}}\right)\]  
Figure 3: Additive attention mechanism.  
where d is the dimension of the query vector. The scaling operation helps stabilize gradients and prevent overlarge numerical values. The Softmax operation ensures that the sum of the attention weights for all tokens equals 1, allowing the weight distribution to reflect the importance of each token. By weighting and summing the query matrix with the attention weights A, we obtain the global attention vector \(q_{global}\):  
\[q_{global}=\sum_{i=1}^{n}A_{i}\cdot Q_{i}\]  
Here, \(q_{global}\) is the global query vector, representing the weighted aggregation of information, which captures the most important features in the input sequence. Next, the global query vector \(q_{global}\) undergoes an element-wise product with the matrix K to obtain the global context representation:  
\[Context=q_{global}\bigodot K\]  
Finally, a linear transformation layer is applied to further process the global context representation, yielding the final hidden representation \(h_{i}\) for each token:  
\[h_{i}=Linear_{final}(Context_{i})+Norm(Q)\]  
where \(h_{i}\) is the final token representation, incorporating both the semantic information from BERT and the syntactic information from the GNN. This enables the model to better understand the context and the dependency relationships between words. Norm refers to the normalization operation.
## 4 Experimental Results
### Use Different Mathematical Models to Obtain Prediction Patterns from Likelihood Ratios
A convenient way to interpret the scores resulting from the modeling is by converting them into 200 equal-area ranks after sequencing in descending order. Fixed recognizable groups of ranks are then associated with pseudo-colors, as shown in the legend of Figure 3. The 200 ranks are displayed in wider groups for lower ranks of lesser concern, and in successively narrower groups for higher ranks of greater concern, e.g., 12.5%, 10%, 5%, 2.5%, 1.5%, and top 1%, of the area of the SA. Figure 3: Prediction patterns using the different models: FZ, LI, LO, LR, and BP in (**a-e**), respectively, all using 70 at_ulpACDIS. Black patches are the oversized trigger zones of the 70 active landslides, 70 at. Colors in the legend indicate groups of ranks of % of the study area (SA) in ascending order. The illustration shows the prediction patterns from the five models, all overlaid with the distribution as black polygons of the 70 active landslide occurrences. The patterns represent the likelihood of future landslide occurrences in the SA. Comparing them, we can observe similarities among higher-ranking groups in the northern part of the study area but strong differences are found in the southern part. Wider patches with high values are in Figure 3b,c, from the LI and LO models, respectively. Discontinuous patches are in Figure 3a,c,d, for the FZ, LR, and BP models. Altogether, there is a similarity between Figure 3a,d,e), and some similarity between Figure 3b,c. Note that we have to compare the same classes, fixed groups of equal-area ranks, in zones of concern. They are the zones with relatively higher ranks but located far from the known occurrences. In particular, we can focus on the top 10% ranks (90-99% and top 1%) corresponding, for instance, to higher elevations (1740-2100 and 2480-2630 m a.s.l. ), high slope angles (37-57), and very low permeability, in addition to the particular types of land use and lithology (see Table 1). How \"good predictors\" are the prediction patterns? Which one is the best or preferable? For answering these questions, we will have to consider the prediction-rate curves in Figure 4, described in the next step. They will provide a measure of predictability of future landslide occurrences, i.e., in our case, the \"next seven.\"  
Figure 4: Prediction-rate curves are displayed generated by iterative cross validation using a sequential exclusion strategy of 7 out of 70 active landslides. (**a**) shows the aggregated iteration results for models FZ, LI, LO, LR, and BP, and (**b**) shows the individual curves for the 10 iterations of the FZ model and the aggregated curve as a thick red curve, corresponding to the red curve in (**a**).
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 4 Main Findings
### Acoustic Feature Analysis  
It is known that acoustic signals provide features useful for several applications, such as speech recognition [55], disease identification [49, 65, 68, 113], and recognition of notes with musical purposes [118].  
This subsection summarizes the statistical analysis techniques, traditional Machine Learning (ML) methods, and more complex algorithms that apply Deep Learning (DL) algorithms, all of which aim to detect patterns and associate features in audio recordings coming from the mouth of a person. ML and statistical analyses are used in all research articles to provide certainty of the results in each investigation. Both methods have been used to reach conclusions related to physical and emotional health.