esa-sceva/satcom-chunk-collection huggingface dataset:
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 3 Simulation Results
### Angular Misalignment  
Lastly, the angular alignment capacity of the emitter-receiver system in highly turbid harbor II waters was assessed using a propagation distance of 0.5 m and a seven-lens scheme configuration. Three different configurations were simulated: (1) an aligned system, (2) only the emitter rotated in a given direction, and (3) only the emitter rotated in the same direction.  
Figure 13 shows the results for a rotation of 10\({}^{\circ}\). This analysis was performed for angles of 0\({}^{\circ}\), 5\({}^{\circ}\), 10\({}^{\circ}\), 15\({}^{\circ}\), and 20\({}^{\circ}\). As expected, we observed a worse performance when either the emitter or the plane of the sensors was rotated, as shown in Figure 14. When the emitter was rotated to compensate for fewer photons arriving at the sensors, an increase in the optimal divergence was seen. The opposite behavior was observed when the plane of sensors was rotated. The optimal divergence as a function of the angular misalignment is plotted in Figure 15 for both cases.  
Figure 12: MALO calculation for different water types at different Tx–Rx distances and for varying divergence angles, considering a seven-lens scheme.  
Figure 13: The MALO as a function of the divergence of the emitter for rotated communication links, considering a harbor II water type, a Tx–Rx distance of 0.5 m and a 7-lens scheme.
## 2 Proposed System
### Scada Hmi  
#### 2.4.1 Haiwell IoT Cloud HMI  
The Haiwell IoT Cloud HMI used in this experiment is the B7S-W series, as shown in Figure 3. It is developed based on the embedded Linux system with a 7-inch touchscreen display and scalable vector graphics (SVG) image editing and processing technology. It can communicate with popular industrial RTUs/PLCs such as Schneider, Siemens, Omron, Yokogawa, etc., using the standard Modbus protocol or their proprietary protocols. It also supports the MQTT, a popular protocol in IoT communication. The HMI specifications are given in Table 7.  
\begin{table}
\begin{tabular}{l c} \hline Power supply & 24 \(\pm\) 20\% V DC \\ \hline Software & Haiwell Cloud SCADA Version 3.36.9.8 \\ \hline Display size & 7-inch TFT \\ \hline Display resolution & 800 \(\times\) 480 pixels \\ \hline Touchscreen type & Resistive \\ \hline Memory & Flash: 2 GB; RAM: 512 MB \\ \hline I/O port & 1 \(\times\) LAN; 2 \(\times\) USB; 1 \(\times\) RS485; 1 \(\times\) RS232; 1 \(\times\) WiFi \\ \hline Common protocol & Modbus RTU; Modbus TCB \\ \hline IoT protocol & MQTT \\ \hline Supported RTU/PLC protocol & Haiwell; Schenider; Siemens; Panasonic; Mitsubishi; Omron; etc. \\ \hline \end{tabular}
\end{table}
Table 7: Haiwell IoT Cloud HMI specifications.  
Figure 3: Haiwell IoT Cloud HMI (B7S-W).  
The HMI is programmed and configured using the Haiwell Cloud SCADA software. Besides the visualization display, it provides real-time trends and stores historical data. The main advantage of this IoT-based SCADA system is that the HMI can be remotely accessed through the web or mobile application via the Haiwell cloud. The application on the HMI is synchronously accessed by all terminals. This means that when the user changes the menu on the HMI device, the display on the web or mobile application also changes accordingly, and vice versa.
# An Integrated Approach to Biomedical Term Identification Systems
## 4 An Architecture for Information Exploration
### Semantic Relational Network/conceptual Graph  
Due to the rich semantic content available after the recognition process, another functionality supported by the platform is the generation of a semantic graph over medical terms. In UMLS, the synonyms are grouped under a unique concept, and these concepts are linked with different types of relationships. Thus, UMLS is also a directed and labeled graph.  
For every medical term extracted from the graph it is possible to find the related UMLS concept and find the relations with the rest of concepts in the text by finding paths through the UMLS network. In this way, a subgraph of UMLS is generated, applying the Dijkstra algorithm to find the minimal  
Figure 8: Example of search in Google Scholar.  
Figure 10: Example of search in SciELO.  
path between a central concept and the rest. The central node is, by default, selected according to its frequency of occurrence in the source text.  
This graph is interactive, so the user can select any other node as central. This selection will trigger the regeneration of the graph, so minimal paths between this new central node and the rest are computed and rendered in a graphical representation on-the-fly. Due to the high number of concepts and relationships in UMLS, this is a very convenient approach for understanding how different concepts are interrelated and exploring UMLS relationships visually in an interactive manner.  
Furthermore, in order to increase the readiness of the visual graph we have relabeled these relationships. There are two main types of semantic relations in UMLS: _hierarchical_ (e.g., _'is a kind of'_, _'is a'_, _'part of'_) or _associative_ (e.g., _'location of'_, _'caused by'_). UMLS also assigns relationship types (UMLS abbreviations: [[https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/abbreviations.html](https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/abbreviations.html)]([https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/abbreviations.html](https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/abbreviations.html))) between concepts, for instance: narrower relationship (RN), synonymy (SY), parent relationship (PAR), among others.  
Figure 11 illustrates how, from the term identified as central _'cancer of colon'_, the other concepts are related to it. Intermediate nodes like _'colon'_ are included as they are needed to reach other present and recognized terms like _'intestino grueso'_ (_'large intestine'_) and _'polipo adenomatoso en colon'_ (_'colon adenomatous polyp'_).  
As UMLS is a multilingual resource, the graph could be shown in several languages (even different to that from the analyzed text).
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios
### Modeling Complex Targets: The Vehicle
#### 3.1.1 Vehicle Target Model  
The modeling of an extended target such as a car has been and still is the subject of several studies, especially in the context of vehicular sensing systems. A relatively simple yet effective model approximating a vehicle with a rectangle featuring 12 point reflectors is provided in [27]. In particular, each scatterer is defined by its visibility function, which  
Figure 3: Block diagram of the sensing processing chain exploiting BS cooperation, target classification, and target-specific tracking. The BSs scan the environment, generating range–angle maps, and resample them according to a predefined grid. Resampled range–angle maps are then shared with the FC and fused in a single map. Target classification is performed at the FC through map cropping and classification (blue block). Then, clustering is performed to merge detections generated by the same target (yellow block). Finally, tracking algorithms perform target state estimation (gray block).  
depends on the incident angle and RCS, and is positioned at specific locations, including the four corners, four wheelhouses, and four sides. The (dimensionless) visibility function takes the value 1.0 in the angular region, where the scatterer likely produces a high reflecting echo, gradually diminishing to zero in areas where the backscattered echo becomes feeble. The car sides are represented as point reflectors with narrow visibility functions and large RCS values. One example can be seen in Figure 2, where scatterers illuminated by the sensing beam reflect the signal if the visibility function is greater than 0. The RCS values of scatterers within an extended target (e.g., the 12 reflections of the car) or a point-like target are commonly described using statistical distributions from the Swerling family.
## 5 Summary
For high-strength nodular iron samples of ductile and ferritic malleable types, attenuation followed the Mason-McSkimin relation, coming from the scattering due to nodular graphite particles. Gray iron samples showed high attenuation with quadratic frequency dependence. Two possible mechanisms are suggested; the Datta-Kinra multiple scattering theory and Rose-Hsu theory with random surface reflection. (6) Engineering polymers exhibited linearly dependent frequency spectra, with observed attenuation levels agreeing with the literature values. Wood behaved similarly to polymers in terms of frequency dependence, but some samples showed quadratic-type or Mason-McSkimin relations. Strong anisotropic attenuation behavior was present in all wood samples. (7) Continuous fiber-reinforced composites (GFRC, CFRC, and KFRC) gave anisotropic attenuation behavior. In the fiber direction, the attenuation levels were relatively low at 70-155 dB/m at 1 MHz. In 60% of the off-fiber tests, the Biwa relation of linear and cubic spectra represented attenuation well. This cubic term is caused by the scattering of waves normally incident upon parallel fibers. (8) Most attenuation spectra for ceramics and rocks showed linear dependence, with some also showing the Rayleigh scattering effect. The quadratic behavior was also present in marble and in mortar samples with high void contents. In mortar samples with lower void contents, the Mason-McSkimin relation appeared, indicating a transition to the Datta-Kinra relation when more voids were present. The assembled attenuation data in this study could be useful in various engineering fields. However, it is hoped that new findings and correlations uncovered above stimulate more research to elucidate underlying mechanisms. This will require both theoretical and experimental efforts. For example, two of the contradictory effects of cold working still require convincing explanations.
# Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings
## Materials and Methods  
### Dataset  
We used two publicly available simulated spike signal datasets and an in-vivo spike signal dataset for our study. First, by using the simulated spike signals where each spike is labeled, we will present the accuracy of the tested models, and through testing on the in-vivo dataset, we will show the applicability of the models in real life applications. Dataset 1 is a simulated dataset provided from University of Leicester that is widely used in spike signal research [12; 27; 28]. The dataset was generated at sampling rate of 24 kHz. It is composed of four subset data named Easy1, Easy2, Difficult1, and Difficult2. Each subset data contains spikes from three different neuron types under four conditions of noise levels, 0.05, 0.10, 0.15, and 0.20, where the numbers represent the standard deviation of noise. Spikes are detected by setting a threshold value of \(4\sigma_{n}\) where action potentials values above the threshold are considered as spikes. \(\sigma_{n}\) is obtained as follows:  
\[\sigma_{n}=\text{median}\bigg{(}\frac{|x|}{0.6745}\bigg{)}\]The dataset is publicly available at [[https://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/spike-sorting](https://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/spike-sorting)]([https://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/spike-sorting](https://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/spike-sorting)). Dataset 2 is a simulation data containing various number of neural signal types [29]. This was recorded at a sampling rate of 24 kHz, and threshold value was set to 5\(\sigma_{n}\). For our experiment, five datasets each for different numbers of clusters (2-5) were selected. Dataset 2 is publicly available at [[https://www135.lamp.le.ac.uk/hgr3/](https://www135.lamp.le.ac.uk/hgr3/)]([https://www135.lamp.le.ac.uk/hgr3/](https://www135.lamp.le.ac.uk/hgr3/)). The in-vivo dataset was acquired from the cortex of a mouse using a tetrode. Neural activities were amplified (\(\times\)10,000), filtered (600 Hz-6 kHz), and digitized (30.3 KHz) using Digital Lynx (Neuralynx, Bozeman, MT, USA) [30; 31; 32]. Single units were manually isolated from trained expert using SpikeSort 3D (Neuralynx) and cluster quality was assessed by L-ratio, isolation distance, cross-correlation, and inter-spike interval (ISI) [33]. Single channel recordings from the tetrode recordings were used for our experiment.
## 4 Experiment Results and Discussion
### Vibration Control Experiments Based on Single Frequency Excitation  
#### 4.2.1 **Case-1**: Same Frequency yet Diverse Amplitudes  
Three groups of experiments were performed at the same frequency yet diverse amplitudes. The semi-active vibration control effects are shown in Figure 11, which are summarized in Table 3. The disturbance frequency reaches 0.23 Hz with a disturbance amplitude of about 9-12\({}^{\circ}\). After the dual joint semi-active control, the vibration reduction effect of 14.56-19.64 dB can be achieved.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Major Frequency**} & \multicolumn{2}{c}{**Amplitude**} \\ \hline \multirow{3}{*}{**I**} & \multirow{3}{*}{0.23 Hz} & \multicolumn{2}{c}{Without control} & \multicolumn{2}{c}{With control-6 A} \\ \cline{3-4}  & & Time domain & \(-\)10.84–13.19\({}^{\circ}\) & \(-\)1.48–1.48\({}^{\circ}\) \\ \cline{2-4}  & & Frequency domain & 11.71\({}^{\circ}\) & 1.22\({}^{\circ}\) \\ \cline{2-4}  & & Control effect & 89.58\% (\(-\)19.64 dB) \\ \hline \multirow{3}{*}{**II**} & \multirow{3}{*}{0.23 Hz} & \multicolumn{2}{c}{Without control} & \multicolumn{2}{c}{With control-6 A} \\ \cline{3-4}  & & Time domain & \(-\)11.18–11.42\({}^{\circ}\) & \(-\)2.11–2.10\({}^{\circ}\) \\ \cline{2-4}  & & Frequency domain & 10.43\({}^{\circ}\) & 1.95\({}^{\circ}\) \\ \cline{2-4}  & & Control effect & 81.30\% (\(-\)14.56 dB) \\ \hline \multirow{3}{*}{**III**} & \multirow{3}{*}{0.23 Hz} & \multicolumn{2}{c}{Without control} & \multicolumn{2}{c}{With control-6 A} \\ \cline{3-4}  & & Time domain & \(-\)8.92–8.98\({}^{\circ}\) & \(-\)1.51–1.46\({}^{\circ}\) \\ \cline{2-4}  & & Frequency domain & 9.23\({}^{\circ}\) & 1.26\({}^{\circ}\) \\ \cline{2-4}  & & Control effect & 86.35\% (\(-\)17.30 dB) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vibration control effect based on the same frequency yet diverse amplitudes excitation.  
Figure 10: Schematic diagram of experimental device.
# Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods
## Appendix B Trust-Region Subproblem Solution Algorithms  
```
1:Inputs: Current iteration \(k\), \(\delta\triangleq\delta_{k}\), \(g\triangleq g_{k}\) and \(B\triangleq B_{k}:\,\Psi\triangleq\Psi_{k}\), \(M^{-1}\triangleq M_{k}^{-1}\), \(\gamma\triangleq\gamma_{k}\)
2:Compute the thin QR factors \(Q\) and \(R\) of \(\Psi\) or the Cholesky factor \(R\) of \(\Psi^{T}\Psi\)
3:Compute the spectral decomposition of matrix \(KMRT^{T}\), i.e., \(RMR^{T}=U\hat{\Lambda}U^{T}\)
4:Set \(\hat{\Lambda}=\text{diag}(\hat{\lambda}_{1},\ldots,\hat{\lambda}_{k})\) such that \(\hat{\lambda}_{1}\leq\ldots\leq\hat{\lambda}_{k}\) and \(\lambda_{min}=\min\{\lambda_{1},\gamma\}\)
5:Compute the spectral of \(B_{k}\) as \(\Lambda_{1}=\hat{\Lambda}+\gamma I\)
6:Compute \(P_{\parallel}=QU\) or \(P_{\parallel}=(\Psi R^{-1}U)^{T}\) and \(g\parallel P_{\parallel}^{T}g\)
7:if\(\phi(0)\geq 0\)then
8:Set: \(\sigma^{*}=0\)
9:Compute \(p^{*}\) with (A3) as solution of \((B_{k}+\sigma^{*}I)p=-g\)
10:else
11:Compute a root \(\sigma^{*}\in(0,\infty)\) of (A8) by Newton method [38]
12:Compute \(p^{*}\) with (A3) as solution of \((B_{k}+\sigma^{*}I)p=-g\)
13:endif
```  
**Algorithm 1** TR subproblem solution with an L-BFGS compact matrix.  
```
1:Inputs: Current iteration \(k\), \(\delta\triangleq\delta_{k^{\prime}}g\triangleq g_{k}\) and \(B\triangleq B_{k}:\)\(\Psi\triangleq\Psi_{k^{\prime}}\)\(M^{-1}\triangleq M_{k}^{-1}\), \(\gamma\triangleq\gamma_{k}\)
2: Compute the thin QR factors \(Q\) and \(R\) of \(\Psi\) or the Cholesky factor \(R\) of \(\Psi^{T}\Psi\)
3: Compute the spectral decomposition of matrix \(RMR^{T}\), i.e., \(RMR^{T}=U\tilde{\Lambda}U^{T}\)
4: Set \(\tilde{\Lambda}=\operatorname{diag}(\tilde{\Lambda}_{1},\ldots,\tilde{\Lambda} _{k})\) such that \(\tilde{\lambda}_{1}\leq\ldots\leq\lambda_{k}\) and \(\lambda_{min}=\min\{\lambda_{1},\gamma\}\)
5: Compute the spectral of \(B_{k}\) as \(\Lambda_{1}=\tilde{\Lambda}+\gamma I\)
6: Compute \(P_{\parallel}=QU\) or \(P_{\parallel}=(\Psi R^{-1}U)^{T}\) and \(g_{\parallel}=P_{\parallel}^{T}g\)
7:if Case I: \(\lambda_{min}>0\) and \(\phi(0)\geq 0\)then
8: Set: \(\sigma^{*}=0\)
9: Compute \(p^{*}\) with (A3) as solution of \((B_{k}+\sigma^{*}I)p=-g\)
10:else if Case II: \(\lambda_{min}\leq 0\) and \(\phi(-\lambda_{min})\geq 0\)then
11: Set: \(\sigma^{*}=-\lambda_{min}\)
12: Compute \(p^{*}\) with (A10) as solution of \((B_{k}+\sigma^{*}I)p=-g\)
13:if Case III: \(\lambda_{min}<0\)then
14: Compute \(\alpha\) and \(u_{min}\) with (A12) for \(z^{*}=\alpha u_{min}\)
15: Update: \(p^{*}=p^{*}+z^{*}\)
16:endif
17:else
18: Compute a root \(\sigma^{*}\in(\max\{-\lambda_{min},0\},\infty)\) of (A8) by Newton method [38]
19: Compute \(p^{*}\) with (A3) as solution of \((B_{k}+\sigma^{*}I)p=-g\)
20:endif
```  
**Algorithm A2** TR subproblem solution with an L-SR1 compact matrix.
# Uncertainty Quantification for Infrasound Propagation in the Atmospheric Environment
## 3 Sobol Index for Sensitivity Analysis of the Tau-p Model
### Pseudocode for Tau-p Model Sensitivity Analysis  
First, the atmospheric parameter _met_ reference data with altitude \(z\) distribution are imported. A polynomial curve is fitted to the atmospheric background reference data. The maximum sampling Sobol number \(N_{max}\) is set, and the polynomial curve fitting coefficients are sampled to generate the atmospheric background data. The atmospheric background data are substituted into the tau-p model to calculate the corresponding output. Finally, the first-order Sobol index \(S_{m}\) and the total Sobol index \(S_{T_{m}}\) that correspond to each atmospheric parameter are calculated. For example, the pseudocode for analyzing the influence of the infrasound propagation distance \(R\) is shown in Algorithm 1. In this paper, the degree of influence of atmospheric physical parameters on infrasound propagation distance \(R\), maximum propagation height \(z_{max}\), and travel time \(t\) are analyzed separately. The corresponding sensitivity indices of each parameter are determined.  
```
0:\(met,N_{max}\);
0:\(S_{m},S_{T_{m}}\);
1: initialize: \(N=0\);
2: while \(N<N_{\max}\);
3: Compute: \(R\) using Equation (2)
4: Compute: \(S_{m}\) using Equation (15)
5: Compute: \(S_{T_{m}}\) using Equation (20)
6:\(N=N+1\)
7: end
```  
**Algorithm 1**Pseudocode for tau-p model sensitivity analysis.
## Appendix A Carrier-to-Noise Ratio of LEO NR Signal  
This paper focuses on the navigation performance analysis of LEO NR signals. For navigation researchers, the carrier-to-noise ratio (CNR) is often used in navigation performance analysis. Next, we derive the relationship between LEO satellite transmitting power and CNR at the receiver. Assuming the transmitting power of the LEO satellite is \(P_{\text{TX}}\), the received power \(P_{\text{RX}}\) after path loss (PL) attenuation is  
\[P_{\text{RX}}=P_{\text{TX}}-PL \tag{1}\]  
where the unit of \(P_{\text{TX}}\) and \(P_{\text{RX}}\) is dBm.  
Only the reference signal is used for navigation service in opportunity navigation scenarios. The reference signal includes system information broadcasting and channel estimation signals, occupying a subset of NR signals. Let \(\alpha\) denote the proportion of the reference signal in the OFDM's resource grid. Therefore, the navigation signal power \(C_{\text{nav}}\) in the receiver becomes  
\[C_{\text{nav}}=10\log_{10}(\alpha)+P_{\text{RX}} \tag{2}\]  
The resulting carrier-to-noise-density ratio is  
\[\frac{C_{\text{nav}}}{N_{0}}=P_{\text{TX}}-PL+10\log_{10}(\alpha)-N_{0} \tag{3}\]  
where \(N_{0}\) denotes the noise power spectral density ant its representative value is \(-203.98\) dBW/Hz [2].  
For a LEO satellite at 600 km altitude with reference signal ratio \(\alpha=19.44\%\), Equation (A3) simplifies to  
\[\frac{C_{\rm nav}}{N_{0}}\approx P_{\rm TX}+10.87\] (A4)  
The transmitting power of LEO satellites ranges 40-57 dBm (10 W-500 W) [79; 80]. Considering antenna gains (0-40 dBi), the equivalent isotropic radiated power (EIRP) exhibits significant variation. For brevity, this paper assumes \(P_{\rm TX}=55\)-65 dBm, yielding a navigation CNR of 65.87-75.87 dB \(\cdot\) Hz.
## 4 Hardware Design
### Hardware Implementation and Test of the QR decomposition  
QR decomposition is a matrix decomposition, for example of a given matrix A, into Q and R which are an orthogonal matrix and an upper right triangular matrix, respectively. On the algorithmic level there are several ways to achieve this, but for a hardware implementation usually 'Givens Rotations' are used to achieve this with the use of CORDIC operations. In order to apply the Givens Rotations to an \(8\times 8\) matrix, a systolic array was designed to accommodate the CORDIC vectoring and rotations. For the implementation of the proposed decoder the matrices were converted from complex to real, as shown in the design of Figure 8.  
As shown Figure 8 there are 2 main types of blocks in the systolic array. The first is the delay unit (DU), which takes inputs from the northern port and passes it to the eastern port as output after a specified delay. This specific delay is basically the time required for a processing element (PE) to produce an output. The PE is a processing element, which is where the real operations are carried out. A PE block takes 2 data inputs and produces 2 data outputs as well as 2 control signal inputs and 2 control signal outputs. Then, depending on the mode decided by the control signal coming in from the western port, the PE block operates in vectoring mode or rotating mode. These modes differ for complex numbers and real numbers. Since our implementation is for real numbers as shown in  
Figure 8: \(8\times 8\) Systolic Array Block Diagram for real numbers.  
Figure 8, we will discuss how the PE blocks operate for the real values. In vectoring mode, the PE block takes both inputs, \(x\) and \(y\), and calculates the angle \(z\) as follows:  
\[x_{i+1} =x_{i}-y_{i}\cdot d_{i}\cdot 2^{-i}\] \[y_{i+1} =y_{i}+x_{i}\cdot d_{i}\cdot 2^{-i}\] \[z_{i+1} =z_{i}-d_{i}\cdot tan^{-1}(2^{-i})\]  
where  
\[d_{i}=+1\text{ if }y_{i}<0\text{ and }-1\text{ otherwise} \tag{8}\]  
Then \(z\) is stored internally. Its outputs are the modulus of the inputs presented at the eastern port, and the southern port just gives out a zero. When the mode specifics CORDIC rotations, the inputs are multiplied by the angle \(z\) stored internally as mentioned earlier, and the rotated outputs \(x\) and \(y\) are presented at the eastern and southern ports respectively. To test the QR decomposition module, Modelsim waveforms were produced and compared to MATLAB results, which showed correct functionality of the implemented hardware.