esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
In our study we operate with the subsurface spectral remote sensing reflectance [noted in the literature as \(R_{raw}(\lambda,\)\(C,\)\(a,\)\(b_{b})\)], which is the upwelling spectral radiance just beneath the water-air interface, \(L(\)-0, \(\lambda)\) normalized to the downwelling spectral irradiance, \(E(\)-0, \(\lambda)\) at the same level [9]). Let \(S\) be the spectrum of \(R_{raw}\) which is calculated from remote sensing data or from _in situ_ optical observations (measured reflectance).  
Subsurface spectral remote sensing reflectance depends on the vector of all the major CPA concentrations and on their inherent optical properties (IOPs). These properties are the specific (normalized to the respective unit concentration) spectral absorption \(a\)* and backscattering, \(b_{b}\)* coefficients of the coexisting water constituents (e.g. \(a\)*\({}_{chl}\)\(a\)*\({}_{sm}\), \(a\)*\({}_{doc}\)\(b_{b}\)*\({}_{chl}\), \(b_{b}\)*\({}_{sm}\) ).  
Let \(T\) be the spectrum of \(R_{raw}\) calculated using the empirical equation derived from numerical Monte Carlo simulations by [PERSON] [9]:  
\[T=-\text{ 0.00036 + 0.110($b_{b}$/a)}-\text{ 0.0447($b_{b}$/a)}^{2} \tag{1}\]  
where \(a\) and \(b_{b}\) are the water column bulk absorption and backscattering coefficients correspondingly.  
Being additive by nature, the coefficients \(a\) and \(b_{b}\) are sums of respective contributions (\(C_{i}\)\(a\)*\({}_{b}\)\(C_{j}\)\(b_{b}\)*\({}_{j}\)) from the major CPAs:  
\[a=a_{w}+\sum_{i}C_{i}a_{i}^{*}\text{,} \tag{2}\]  
\[b_{b}=b_{b_{w}}+\sum_{j}C_{j}b_{b_{j}}^{*}\text{,} \tag{3}\]  
where \(i\) and \(j\) are the indices of the absorbing and scattering components (e.g. _chl, sm_ or _doc_). Using equations (1-3) we can calculate the reflectance spectrum T (reconstructed reflectance) for any vector of CPA concentrations if the set of respective IOPs (i.e. hydro-optical model) is given.  
The relationship (1) was developed for case 2 waters [10], similar approaches e.g. by [PERSON] are designed for case 1 waters.
# A Preliminary Study on Machine Learning Techniques to Classify Cardiovascular Diseases in Mexico
## 2 Materials and Methods
A critical step in this methodology involved separating the attributes into two groups: \"invasive\" and \"non-invasive\". The classification of attributes was performed based on expert consultation in the medical field. The distinction was made to prioritize features that can be obtained without specialized clinical procedures, thereby facilitating a rapid and accessible cardiovascular risk assessment. Non-invasive attributes were selected to minimize patient discomfort and simplify data collection. The eight non-invasive attributes considered were: age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, maximum heart rate, and exercise-induced angina. Blood pressure was measured on the upper arm (brachial artery) using a standard sphygmomanometer. The measurement was taken in a seated position after at least five minutes of rest, following standard clinical guidelines. Serum cholesterol and fasting blood sugar were obtained through venous blood samples, analyzed using enzymatic colorimetric methods commonly used in clinical diagnostics. The tests were conducted in a fasting state (at least 8 h) to ensure accuracy. The heart rate was recorded using the MAX30100 sensor, while the presence of angina during physical activity was determined based on patient self-report and physician evaluation. All studies and measurements were supported and validated by a health expert. To validate the  
Figure 1: Proposed methodology, using a new third stage. impact of these attributes on classification performance, the forward selection technique was applied. This method incrementally selected features that contributed most to model accuracy, ensuring optimal classification performance with a minimal number of attributes. This sequential feature selection method starts with an empty set of features and iteratively adds one attribute at a time, selecting the one that provides the most significant improvement in model performance at each step. The process follows these key steps:  
Initial Model Setup: The classification algorithm is trained using an empty set of predictors. Incremental Feature Addition: Attributes are added one-by-one based on their individual contribution to model performance, which is assessed using a predefined evaluation metric (AUC). Performance Evaluation: After each addition, the model's performance is measured. If adding a feature improves classification accuracy beyond a set threshold, the feature is retained. Stopping Criterion: The process continues until adding further attributes does not yield a statistically significant improvement in performance. Algorithm 1 shows the pseudocode of the forward selection algorithm implemented in this work. 1. Initialize:  
a. Create an empty set of selected features: \(Y_{\text{k}}=\{\emptyset\}\)  
b. Set iteration counter: \(k=0\)  
2. Repeat until stopping criterion is met:  
a. Select the best remaining feature:  
x* = arg max (x* \(\in\) X \(\setminus\) Y\({}_{\text{k}}\)) [J(Y\({}_{\text{k}}+\) x*)]
# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
## 4 Results and Discussion
### Seismic Multi-Attribute Prediction Result
#### 4.2.1 Attribute Optimization Result
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**R**} & **a** & **b** & **c** & **d** & **e** & **f** & **g** & **h** & **i** & **j** & **k** \\ \hline
**a** & \(1\) & \(0.9955\) & \(-0.8003\) & \(0.9949\) & \(0.9974\) & \(-0.8443\) & \(0.8661\) & \(0.2221\) & \(0.1029\) & \(-0.6572\) & \(0.9329\) \\
**b** & \(0.9955\) & \(1\) & \(-0.8023\) & \(0.9997\) & \(0.9882\) & \(-0.8418\) & \(0.8928\) & \(0.2871\) & \(0.1766\) & \(-0.6501\) & \(0.9407\) \\
**c** & \(-0.8003\) & \(-0.8022\) & \(1\) & \(-0.8056\) & \(0.7693\) & \(0.9752\) & \(-0.4967\) & \(-0.3558\) & \(0.2117\) & \(0.8204\) & \(-0.5618\) \\
**d** & \(0.9949\) & \(0.9998\) & \(-0.8056\) & \(1\) & \(0.9876\) & \(-0.8479\) & \(0.8927\) & \(0.2923\) & \(0.1828\) & \(-0.6519\) & \(0.9401\) \\
**e** & \(0.9974\) & \(0.9883\) & \(-0.7694\) & \(0.9875\) & \(1\) & \(-0.8228\) & \(0.8636\) & \(0.1668\) & \(0.0495\) & \(-0.6243\) & \(0.9397\) \\
**f** & \(-0.8443\) & \(-0.8418\) & \(0.9752\) & \(-0.8479\) & \(-0.8228\) & \(1\) & \(-0.5611\) & \(0.2853\) & \(0.1544\) & \(0.7664\) & \(-0.6451\) \\
**g** & \(0.8662\) & \(0.8928\) & \(-0.4967\) & \(0.8927\) & \(0.8636\) & \(0.5611\) & \(1\) & \(0.3978\) & \(0.3551\) & \(-0.4121\) & \(0.9652\) \\
**h** & \(0.2221\) & \(0.2871\) & \(-0.3559\) & \(0.2924\) & \(0.1668\) & \(-0.2853\) & \(0.3977\) & \(1\) & \(0.9811\) & \(-0.5609\) & \(0.2035\) \\
**i** & \(0.1209\) & \(0.1767\) & \(-0.2117\) & \(0.1829\) & \(0.0495\) & \(-0.1544\) & \(0.3551\) & \(0.9811\) & \(1\) & \(-0.3954\) & \(0.1372\) \\
**j** & \(-0.6572\) & \(-0.6502\) & \(0.8204\) & \(-0.6519\) & \(-0.6244\) & \(0.7664\) & \(-0.4121\) & \(-0.5609\) & \(-0.3954\) & \(1\) & \(-0.4362\) \\
**k** & \(0.9330\) & \(0.9407\) & \(-0.5618\) & \(0.9401\) & \(0.9397\) & \(-0.6451\) & \(0.9652\) & \(0.2035\) & \(0.1373\) & \(-0.4362\) & \(1\) \\ \hline \hline \end{tabular} (a) Root mean square amplitude; (b) maximum energy level; (c) instantaneous frequency; (d) maximum amplitude; (e) average energy level; (f) bandwidth deviation ratio; (g) arc length; (h) bandwidth ratio 9; (i) decile frequency 2; (j) high cross-section; and (k) total energy.  
\end{table} 
Table 2: Cross-correlation coefficients of selected attributes for the No. 2 coal seam.
## 3 Extended Kalman Filter-Based Relative Transfer Function Estimation
The _prediction step_, using the model (12), is applied for every frame \(t>0\), \[\widehat{\mathbf{a}}_{21,t|t-1}=\widehat{\mathbf{a}}_{21,t-1},\] (16) \[\mathbf{P}_{t|t-1}=\mathbf{P}_{t-1}+\mathbf{Q},\] (17) where \[\mathbf{P}_{t}=E\left[\left(\mathbf{a}_{21,t}-\widehat{\mathbf{a}}_{21,t} \right)\left(\mathbf{a}_{21,t}-\widehat{\mathbf{a}}_{21,t}\right)^{\top} \right],\] (18) \[\mathbf{P}_{t|t-1}=E\left[\left(\mathbf{a}_{21,t}-\widehat{\mathbf{a}}_{21,t |t-1}\right)\left(\mathbf{a}_{21,t}-\widehat{\mathbf{a}}_{21,t|t-1}\right)^{ \top}\right]\] (19) are error covariance matrices. The Kalman filter is initialized using the overall mean and covariance of the RTF, that is, \(\widehat{\mathbf{a}}_{21,0}=\boldsymbol{\mu}_{a_{21}}\) and \(\mathbf{P}_{0}=\mathbf{\Sigma}_{a_{21}}\). Further details about these parameters are provided in Section 3.2. The _updating step_ is applied to correct the previous estimation with the observations \(\mathbf{y}_{1,t}\) and \(\mathbf{y}_{2,t}\) (whose relationship is given by Equation (14)),  
\[\widehat{\mathbf{a}}_{21,t}=\widehat{\mathbf{a}}_{21,t|t-1}+\mathbf{K}_{t} \left(\mathbf{y}_{2,t}-\boldsymbol{\mu}_{y,t}\right), \tag{20}\]  
\[\mathbf{P}_{t}=\mathbf{P}_{t|t-1}-\mathbf{K}_{t}\mathbf{S}_{y,t}\mathbf{K}_{t} ^{\top}, \tag{21}\]  
where  
\[\mathbf{K}_{t}=\mathbf{C}_{ay,t}\mathbf{S}_{y,t}^{-1} \tag{22}\]  
is the Kalman gain, and  
\[\boldsymbol{\mu}_{y,t}=E\left[\mathbf{y}_{2,t}\right], \tag{23}\]  
\[\mathbf{S}_{y,t}=E\left[\left(\mathbf{y}_{2,t}-\boldsymbol{\mu}_{y,t}\right) \left(\mathbf{y}_{2,t}-\boldsymbol{\mu}_{y,t}\right)^{\top}\right], \tag{24}\]  
\[\mathbf{C}_{ay,t}=E\left[\left(\mathbf{a}_{21,t}-\widehat{\mathbf{a}}_{21,t|t -1}\right)\left(\mathbf{y}_{2,t}-\boldsymbol{\mu}_{y,t}\right)^{\top}\right] \tag{25}\]  
are necessary statistics, the estimation of which is developed in the next subsection. These equations correspond to the most general Kalman filter model as described in Reference [29]. We define the equations in terms of \(\boldsymbol{\mu}_{y,t}\), \(\mathbf{S}_{y,t}\) and \(\mathbf{C}_{ay,t}\) because the non-linearity of \(\mathbf{h}\) in Equation (14) makes their estimation non-trivial. To deal with non-linear functions, two variants of the Kalman filter are widely used: the eKF and the unscented Kalman filter (uKF) [29]. In the case of the model of (14), it could be demonstrated that both eKF and uKF yield the same closed-form expressions for \(\boldsymbol{\mu}_{y,t}\), \(\mathbf{S}_{y,t}\) and \(\mathbf{C}_{ay,t}\). We choose the eKF approach because it gives a more stable computational solution and it is easier to implement than the uKF one. In the next subsection the eKF approach is presented.
# Wireless Power Transfer via Subterahertz-Wave
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & [14] & [15] & [16] & [22] & **This Work** & **This Work** \\
**No.** & & & & & [18] & [24] \\ \hline
**Frequency** & & & & & & \\
**[GHz]** & 94 & 94 & 94 & 94 & **94** & **303** \\ \hline
**Transmission** & CPW & Slot \& GCPW & MSL & MSL & MSL & MSL \\
**Line** & & & & & & \\ \hline
**Technology** & Alumina & CMOS & CMOS 65 & & **NPC-F220A** & **NPC-F220A** \\  & & 0.13 \(\upmu\)m & nm & Duroid 5880 & **(Nippon** & **(Nippon** \\  & & & & & **Pillar** & **Pillar** \\ \hline
**Architecture** & Bow-tie slot & Taper slot & Dipole + & MSL patch 2 & **NGL patch 2** \\  & antenna + & Transition + & step-up & \(\times\) 2 array + & **Notch filter** & **Notch or** \\  & Rectifier & BPF + & Transformer + Rectifier & Rectifier & **+ Rectifier** & **Low-pass** \\  & & & & & **filter +** \\  & & & & & **Rectifier** \\ \hline
**Diode** & GaAs SBD (Virginia & CMOS & & & **GaAs SBD** (MA4E1310 & **GaAs SBD** (MA4E1310 & **MA4E1310** \\  & diode YDI & Shottky & diode & & & **MACOM)** \\  & ZDB) & & & & & \\ \hline
**Antenna** & & & & & & \\
**Gain [dBi]** & NA & 6.5 & NA & 12 & **-** & **8.32** \\ \hline
**Oscillator** & & & & & & \\
**Output** & \(\sim\)100 & \(\sim\)20 dBm & 0.14 & \(\sim\)5000 & **-0.4** & **33,400** \\
**Power [W]** & & & & & & \\ \hline
**Rectenna** & & & & & & \\
**DC Output** & 0.65 & 0.9 & 0.1 & 15 & **39** & **17.1** \\
**Power [mW]** & & & & & & \\ \hline
**RF-DC** & & & & & & \\
**Conversion** & 32.3 & 37 & 10 & 20.5 & **38** & **2.17** \\
**Efficiency** & & & & & & \\
**[\%]** & & & & & & \\ \hline
**Load** & & & & & \\
**Resistance** & 400 & 100 & 5000 & 200 & **130** & **130** \\
**[\(\Omega\)]** & & & & & & \\ \hline
**Rectenna** & & & & & & \\
**size [mm\({}^{2}\)]** & 5.62 & 1 \(\times\) 2.9 & 0.48 & \(\times\)5.1 \(\times\) 9.4 & **3.6 \(\times\) 5** & **1 \(\times\) 5** \\ \hline
**Rectenna** & & & & & & \\
**Power** & 0.17 & 0.31 & 0.038 & 0.3 & **2.38** & **3.43** \\
**density** & & & & & & \\
**[kW/m\({}^{2}\)]** & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of this work to earlier studies in W—mm band.
# Predictive Quantization and Symbolic Dynamics
## 7 Summary and Discussion  
In this paper, we surveyed a set of methods and algorithms that allow an analysis of information dynamics in a continuous time series or in signal data by a process of symbolization. The problem is discussed by addressing the steps of representation learning, embedding by reduction of the representation complexity using lossy coding, and finally the quantization of the continuous embedding into a discrete representation by maximizing the predictive information of the resulting symbolic sequence. The process of quantization is performed by setting a threshold for similarity between the embedding vectors for searching repeated sequences by following a chain of approximately similar suffixes. The resulting suffix-links structure is then used to derive a measure of predictive information by using the suffixes as a way to preform data compression by recopying blocks of current data points from their earlier occurrences. The extent of such compression is then used as a measure of the conditional entropy of the data based on their past. By performing an exhaustive search over possible threshold values, a sequence with maximal difference between its unconditional and conditional entropy, which comprises its predictive information measure, is chosen as the optimal symbolization sequence. The task of designing features for embedding time series data is one of the more challenging steps in this analysis. To address this challenge, applying methods of learning representation by neural embeddings, followed by a step of compression by rate reduction to optimize the temporal structure, can be a significant step towards more fully automating such complex signal analysis tasks. Investigating the trade-off between the fidelity and quality of data representation is formulated in terms of a predictive bottleneck that combines both the symbolic information rate of the latent representation at different encoding rates and the accuracy of the encoding-decoding scheme at each such rate. The survey concludes by showing several applications of symbolization, including an extension to transfer entropy estimation between two parallel sequences. It should be noted that the proposed symbolization method is not assured to achieve a generating partition, for which the topological entropy of a dynamical system achieves its supremum. Nevertheless, we hope that by carefully representing the data and maximizing the predictive qualities of the symbolized approximation, meaningful aspects of the data structure can be efficiently found.
## 3 System Design and Hardware Prototype
### Microcontroller  
Considering the requirement for machine learning capabilities, it might be assumed that using a powerful microprocessor is necessary. However, recent advancements have enabled the direct deployment of machine learning algorithms onto low-power microcontrollers [45]. There are cost-effective hardware development boards available off-the-shelf that integrate machine learning capabilities, low power consumption, IMU units, wireless connectivity, and a microphone into a single board, making them well suited for the SafeStride prototype.  
The Arduino Nano 33 BLE Sense, which incorporates the nRF52840 microcontroller, LSM9 DS1 IMU, MP34 DT05 microphone, and BLE connectivity, is a prominent example of such a device [46]. It has been widely used in research experiments, demonstrating its effectiveness in various applications that demand machine learning capabilities, low power consumption, and integrated sensor functionalities [47; 48; 49]. Alternatively, the Seeed Studio XIAO nRF52840 microcontroller provides comparable capabilities [50]. During the development of this project, the dataset was developed using the Arduino Nano 33 BLE Sense board.  
In this IoT application, the communication characteristics of the hardware components used are critical. Specifically, while the Arduino Nano is equipped with Bluetooth Low Energy, it does not support Wi-Fi wireless protocol. To address this limitation and enable wireless communication with both Bluetooth and Wi-Fi devices, as well as facilitate cloud communication with a web server, the ESP8266 has been incorporated as the main core. This component handles wireless communication by integrating Bluetooth and Wi-Fi functionalities. The nRF52840 microcontroller on the Arduino Nano board, meanwhile, is used for sensor data sampling and machine learning classification tasks. As illustrated in Figure 2, this configuration enables extensive wireless communication functions, paving the way for seamless connectivity and cloud-based interactions across multiple devices.  
Figure 1: Visual depiction of a SafeStride prototype integrated into a walker device.
## 2 Materials and Methods
### Neural Network Architecture  
The study utilized a neural network architecture tailored for the optimization of the UAV control systems. The network was constructed to efficiently process inputs and generate precise control outputs, ensuring optimal UAV navigation.  
Figure 1 illustrates the architecture of the neural network, detailing the flow from the input layer, through the hidden layers, and ending at the output layer. Each node in the input layer represents a specific input feature, while the nodes in the hidden and output layers represent the transformed features and final predictions, respectively. The connections indicate the flow of information, with weights and biases optimizing this flow, as described by the equation.  
The neural network is structured as a sequential model with three layers. Mathematically, each layer can be represented as:  
\[h_{i}=\tanh h(W_{i}\cdot x_{i}+b_{i}) \tag{1}\]  
where \(h_{i}\) is the output after activation, \(\tanh\) is the hyperbolic tangent activation function, \(W_{i}\) denotes the weight matrix, \(x_{i}\) represents the input vector, and \(b_{i}\) is the bias term [32; 44; 46].
## 2 Background and Related Work
### LLM-Based SQLI Black-Box Detection Methods  
In recent years, the rapid advancement in LLMs has sparked significant interest in their intersection with cybersecurity. LLMs boast extensive general knowledge and basic reasoning skills, allowing them to adapt rapidly to a wide range of downstream tasks. Given these capabilities, researchers have begun to explore the application of LLMs in SQLI black-box detection.  
[PERSON] et al. [16] introduced PentestGPT, an LLM-based automated tool for penetration testing. Their research underscores the considerable potential of LLMs in addressing challenges related to vulnerability mining and penetration testing within web security. PentestGPT employs round-robin scheduling among three modules, depending on LLMs' robust task planning and tool utilization capabilities to conduct automated penetration tests. Therefore, PentestGPT can perform SQLI black-box detection in targeted web applications with the help of the automated SQLI scanner Sqlmap. [PERSON] et al. [32] evaluated theability of LLMs to analyze vulnerabilities and suggest attack vectors during penetration testing, thereby illustrating the potential of LLMs in this domain. They concentrated on the performance of LLM Agents in penetration testing, citing automated LLM agents such as AutoGPT [33] and BabyAGI [34; 35], and envisaged a system with two layers: a high-level task planning layer and a low-level attack execution layer.  
However, both [PERSON] et al. and [PERSON] et al. focused primarily on the process of penetration testing without delving deeply into the effectiveness of LLMs in SQLI black-box detection. Although the code of PentestGPT is publicly available, it is not effective in performing SQLI black-box detection. In addition, [PERSON] et al. did not provide an open-source tool or include experimental data in their paper, limiting the wider application of this methodology in the field of SQLI black-box detection.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 6 Undemineralized Dentin Matrix (UDD)
### Clinical Studies
#### 6.2.1 Clinical Outcomes  
Clinically, the healing process was satisfactory, with no major post-operative complications, with less inflammation and rejection response, one potential limitation of other types of bone grafts [38; 57].  
One particular split-mouth randomized double-blind study deviated from the usual analysis of this subject. This study used UDD from lower third molar extractions and evaluated clinical outcomes such as: pocket depth, recession, clinical attachment level regarding the lower second molar, as well as patient-related outcomes: pain, healing and swelling [51]. After a 3-month observation period, in terms of pocket depth (control group: 3.43 \(\pm\) 0.79, UDD group: 2.86 \(\pm\) 0.9), gingival recession (control group: \(-\)2.29 \(\pm\) 1.25, UDD group: \(-\)2.86 \(\pm\) 0.9) and clinical attachment level (control group: 1.14 \(\pm\) 1.57, UDD group: 0 \(\pm\) 0), the differences found were not statistically significant (\(p>0.05\)) [51]. Finally, regarding patient-related outcomes (pain, healing and swelling), similar results were found, with no statistically significant differences found between groups (\(p>0.05\)) [51]. A similar result was found in a split-mouth clinical trial where the use of the lower third molar as a bone substitute resulted in a significant reduction of the pocket depth, mainly in the first 3 months [59]. After a 6-month-period, the bone density found in the test group was greater, with statistically significant difference (\(p<0.001\)) [59].
###### Abstract  
Articulatory speech synthesis has long been based on one-dimensional (1D) approaches. They assume plane wave propagation within the vocal tract and disregard higher order modes that typically appear above 5 kHz. However, such modes may be relevant in obtaining a more natural voice, especially for phonation types with significant high frequency energy (HFE) content. This work studies the contribution of the glottal source at high frequencies in the 3D numerical synthesis of vowels. The spoken vocal range is explored using an LF (Liljencrants-Fant) model enhanced with aspiration noise and controlled by the \(R_{d}\) glottal shape parameter. The vowels [\(\alpha\)], [i], and [u] are generated with a finite element method (FEM) using realistic 3D vocal tract geometries obtained from magnetic resonance imaging (MRI), as well as simplified straight vocal tracts of a circular cross-sectional area. The symmetry of the latter prevents the onset of higher order modes. Thus, the comparison between realistic and simplified geometries enables us to analyse the influence of such modes. The simulations indicate that higher order modes may be perceptually relevant, particularly for tense phonations (lower \(R_{d}\) values) and/or high fundamental frequency values, \(F0\)s. Conversely, vowels with a lax phonation and/or low F0s may result in in inaudible HFE levels, especially if aspiration noise is not considered in the glottal source model.  
v 2019; 9, 4535; doi:10.3390/app9214535