esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The study enrolled 10 patients (3 males; 7 females) selected in the UOD of Implant prosthesis of the Oral and Maxillofacial Sciences Department of Policlinico \"Umberto I\", La Sapienza\" University of Rome. The study was rightly approved by the Department/Istitutional Review Board, approval n. 132/2023 Prot. N. 0001829, on 27 September 2023 of Sapienza University of Rome.  
Patients were selected based on the following inclusion and exclusion criteria, identified through medical history and physical examination.  
Inclusion Criteria:  
- Adult patients, 18 years older;  
- Patients with at least a class IV bone defect, according to [PERSON] and Howell [14];  
- Patients in good general health;  
- Patients with good oral hygiene and high motivation.  
Exclusion Criteria:  
- Heavy smokers (>10 cigarettes per day);  
- Patients with untreated periodontal disease;  
- Pregnant patients;  
- Patients with uncontrolled diabetes (glycated haemoglobin level > 7 mg/dL);  
- Patients undergoing chemotherapy and/or with a history of radiation therapy in the head and neck region;  
- Patients with liver, blood, or kidney diseases;  
- Patients with immunosuppression, ongoing corticosteroid use, or inflammatory and autoimmune diseases of the oral cavity.  
According to the Cawood and Howell classification, 3 patients had a class IV bone defect, while 7 had a class V bone defect (Table 1).  
All patients (3 males; 7 females) presented with both horizontal and vertical bone defects requiring guided computer-aided design for the planned prosthetic restoration. In all cases, bone reconstruction was performed using a customized membrane.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Patient** & **Gender** & **Age** & **Defect** & **Site** & **Height** & **B-O** & **M-D** \\
**(C. and H.)** & & **(mm)** & **Width** & **Width** & **Width** \\ \hline
1. & M & 28 & IV & 1.2–2.2 & 17.5 & 3.1 & 24 \\
2. & M & 28 & V & 3.2–3.5 &  
The data from Table 1 show that 5 patients had an upper jaw bone defect with more than two missing teeth, and 5 had a lower jaw bone defect, including 3 with more than two missing teeth, 1 with two missing teeth, and 1 with a single missing tooth.  
The pre-surgical prosthetic plan involved rehabilitating all patients with an FP-1 type prosthesis, according to the Misch Classification.  
FP-1: Fixed prosthesis on implants, replacing only the crown of the missing tooth.  
The same protocol was followed for all patients.
## 4 Materials and Methods
### EO Composition Analysis and Sample Preparation
#### 4.4.1 GC-EIMS Analysis  
The GC-EIMS analysis was performed using an Agilent Technologies GC-MS instrument, consisting of a 6890 N gas chromatograph, equipped with an autoinjector model 7683 and coupled to a mass spectrometer detector (MSD), model 5973 INERT (Santa Clara, CA, USA). The MSD was set in SCAN mode (scan range: _m/z_ 40-350), with the electron ionization (EI) source set at 70 eV. Two columns of different polarities were installed: a non-polar DB-5 ms column (5%-phenyl-methylpolysiloxane, 30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness; J & W Scientific, Folsom, CA, USA) and a polar HP-INNOWax column (polyethylene glycol, 30 m \(\times\) 0.25 mm i.d., 0.25 \(\upmu\)m film thickness; J & W Scientific, Folsom, CA, USA) columns. The oven temperature was increased with a gradient ramp of 3 \({}^{\circ}\)C/min from 60 \({}^{\circ}\)C to 250 \({}^{\circ}\)C, which was maintained for 5 min. The ion source temperature was set at 250 \({}^{\circ}\)C, and the carrier gas was helium (GC purity grade from Indura, Guayaquil, Ecuador), set at the constant linear velocity (flow rate) of 1 mL/min. 1 \(\upmu\)L of each EO solution was injected in triplicate with a split ratio of 40:1.  
The identity of each EO component was assigned by comparing the corresponding GC-EIMS spectrum and the calculated linear retention index (LRI) with the data reported in the literature. LRIs were calculated in accordance with [PERSON] and [PERSON] [53], using a homologous mixture of \(n\)-alkanes (C\({}_{9}\)-C\({}_{25}\)) injected under the same condition as the _H. laricifolium_ EO samples.
## 3 Materials and Methods
### Model Selection
#### 3.4.2 Facebook Prophet Forecasting Model  
Facebook's Prophet network is a strong tool that can accurately predict time-series data using daily observations of patterns at different scales. Forecast time-series data are derived from additional models, where nonlinear trends account for seasonal, weekly, and daily periods, including holiday results. This tool works best during periods with stable seasonal results and a few seasons of historical data. Prophet deals with missing data and trend changes and can often adapt to deviations. It allows users to make more accurate forecasts faster than other time series forecasting strategies, requiring very little computer time. Prophet is at the level of other models and quickly generates predictions in seconds. This tool can be used to generate accurate weather forecasts even from incomplete or black data without manual work. In addition, the Prophet has many \"human\" seasons of the week and year [28; 29].  
As a powerful tool for Python and R released in 2017 by Facebook, Prophet models time series datasets with trends, seasonality, and holidays. Prophet takes a few seconds to fit the model with tunable parameters, and it is represented by the Formula (7) [30]:  
\[y(t)=g(t)+s(t)+h(t)+\epsilon_{t} \tag{7}\]  
The equation provides a comprehensive prediction formula for the Prophet forecasting model, wherein the anticipated outcome, y(t), is determined by the linear or logistic equation, g(t); seasonality based on the chosen period, such as yearly, monthly, or daily, is denoted as s(t); holiday-related anomalies are denoted as h(t); and unforeseen errors are denoted as \(\epsilon_{t}\). The model encompasses multiple parameters that can be fine-tuned to enhance forecasting accuracy. Depending on the intended use, the model can be classified as linear or logistic. Linear models normalize outliers and do not impose a maximum or minimum threshold. Conversely, logistic models are suitable for saturated forecasts that require defining the highest and lowest values [30].
## 5 Discussion
### Comparison of Range and Azimuth Phase Gradient Stacking  
The application of the range and azimuth direction to seismic fault zones and active faults has been investigated [26; 27; 28], but the detection of landslides using only the range or azimuthal direction is subject to partial erroneous detection of landslides [34]. This is mainly due to the fact that gradients mainly detect boundary information, so landslides with deformation appear as two neighboring regions in the detection results of a single direction phase gradient stacking [34], which leads to a situation where some landslides are not signaled in one direction but are signaled in other directions. For example, the two small landslides in Box 1 of Figure 9a-e cannot be detected in G\({}_{0}\), while the landslide boundaries can be clearly detected in other directions. As for the landslide in Box 2 in Figure 9, the landslide boundary information is detected in the results of all four directions (Figure 9a-d), while in the fused results, the results of all four directions are combined. Since there is always a difference between the landslide direction and the irradiation direction of the radar imaging [10], the landslide morphology detected by applying phase gradient stacking in different directions is obviously different, such as the landslide in Box 3 in Figure 9. Although the landslide information can be detected in the results of all four directions, the landslide is distributed along the range towards an angle of about 45\({}^{\circ}\), so it shows more prominence in G\({}_{45}\). Secondly, the landslide boundary information detected in G\({}_{135}\) can better complement the results of G\({}_{45}\), so the fusion of all the results can obtain more complete landslide boundary information. Overall, the fused results can combine information from all directions, and the fusion of the four methods can attenuate some of the noise signals and better highlight the landslide signals.
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
###### Abstract  
This study investigates the structure of shear-wave velocities (Vs) in the shallow layers of the Oran region, north-west of Algeria, using non-invasive techniques based on ambient vibration arrays. The region has experienced several moderate earthquakes, including the historical Oran earthquake of 1790. Ambient vibration measurements were carried out at 15 sites throughout the study area. Two methods were used: spatial autocorrelation (SPAC) and frequency-wavenumber analysis (f-k), which allowed us to better constrain Rayleigh wave dispersion curves. The inversion of the dispersion curves derived from the f-k analysis allowed for estimating the shear-wave velocity profiles and the \(V_{s30}\) value at the sites under study. The other important result of the present study is an empirical equation that has been proposed to predict \(V_{s30}\) in the Oran region. The determination of near-surface shear-wave velocity profiles is an important step in the assessment of seismic hazard. This study has demonstrated the effectiveness of using ambient vibration array techniques to estimate the soil Vs structure.  
ambient vibration; velocity profiles; seismic hazard; array measurements; spatial autocorrelation (SPAC); frequency-wavenumber analysis (f-k); \(V_{s30}\); shear-wave velocity +
Footnote †: journal: _sciences_
## 3 Proposed Model
### Question Reasoning
#### 3.4.1. Question Embedding and Path Embedding  
The model's question embedding module initially sends the question \(S_{E}\) to the Bert encoder for word-level encoding in order to acquire the word matrix \(\mathbf{E}_{B}=[\mathbf{e}_{1}^{g},\mathbf{e}_{2}^{g},\cdots,\mathbf{e}_{n}^{g}]\). Then \(\mathbf{E}_{B}\) is transmitted to FFN, where the fully connected layer is configured for two purposes: one, to minimize the dimensionality of \(\mathbf{E}_{B}\) for following operations, and the other, to enable the question encoding module to learn. After the fully connected layer, \(\mathbf{E}_{B}\) yields the question matrix \(\mathbf{E}_{F}\in\mathbb{R}^{n\times m_{f}}\), where \(m_{f}\) is the dimensionality of a single vector. Finally, a pooling layer is used to reduce the dimension of \(\mathbf{E}_{F}\) to obtain the question vector \(\mathbf{e}^{q}\in\mathbb{R}^{b}\), so that the output of the question embedding module is of fixed dimension. The pooling method is chosen as mean-pooling, as shown in Equation (14):  
\[\mathbf{e}_{q}=mean\ pooling(\mathbf{E}_{F}) \tag{14}\]  
The path embedding module follows the same structure as the question embedding module in that both use Bert as an encoder to obtain the word matrix, then FFN to reduce the dimensionality while making the path embedding module capable of learning, and finally mean-pooling to reduce the dimensionality of the path matrix so that it is similar to the question vector \(\mathbf{e}^{q}\) for similarity calculation. Because the path information is represented by a set of triples, it must be connected to form a sentence based on the same triple nodes before being fed into the model, and the sentence is then sent to the path embedding module. Although the network structure of the question embedding and path embedding modules is identical, the parameters are not.
## 3 Results
### Evaluation Metrics  
Different metrics were used to compare the segmented images with the ground truth and various neural networks or machine learning algorithms that exist in order to evaluate AI segmentation models' performance. Evaluation metrics can be divided into three categories: overlap and similarity metrics, distance metrics, and volumetric metrics. Overlap and similarity metrics define how well the segmented regions agree with a known reference standard. They include accuracy (detection accuracy (DA), identification accuracy (FA), pixel accuracy (PA)), sensitivity or recall, precision, Boundary F1 (BF) score, Jaccard coefficient (JS), intersection over union (IoU), and Dice index or Dice similarity coefficient (DSC), mean Dice and symmetric best Dice (SBD)) [28; 30; 33; 47; 48; 60; 61]. Distance metrics describe the difference in distance between the segmented graphic contours, or surface pixels, and the reference standard. They include average system surface distance (ASSD), Hausdorff distance (HD), also known as maximum symmetric surface distance (MSSD), average median surface deviation, and mean absolute deviations (MADs) [51; 52; 57]. Volumetric metrics evaluate the performance of the tooth segmentation model from a volumetric perspective. They include volume differences (VD) and relative volume differences (RVD) [52; 59]. Other metrics include object include ratio (OIR), Chamfer-L2, normal consistency (Normals), and occupancy accuracy (OccAcc), also applied to measure the coherence and accuracy of the segmentation algorithms on surfaces and space vectors [41; 61]. In addition, some studies have reported the segmentation time [35; 39; 43; 51; 54] and the data size of different models [36] as additional evaluation criteria.
# Design and Kinematic Analysis of Deployable Antenna for Bionic Bird Tail Feather
## 4 Kinematic Analysis
### Kinematic Simulation  
The angular velocity variation is an important indicator of the motion law of the mechanism. The first-order derivative of the position equation can represent the angular velocity and velocity equation of the point, and the first-order derivative of the center-of-mass position equation can represent the velocity variation law of the center of mass.  
The center-of-mass coordinate system is established at the center-of-mass position of each linkage, and the displacement vector of the single set of expandable structure is plotted, as shown in Figure 20, and the angular velocity in Expression (7) and the center-of-mass linear velocity in Expression (8) of each linkage are obtained.  
\[\left\{\begin{array}{l}\omega_{1}=\hat{\theta}_{1}z_{1}\\ \omega_{2}=\hat{\theta}_{2}z_{2}\\ \omega_{3}=\hat{\theta}_{3}z_{3}\\ \omega_{4}=\hat{\theta}_{3}z_{4}\end{array}\right. \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(x_{a}\) & \(x_{d}\) & \(I_{\rm AI}\) & \(l_{\rm CD}\) & \(l_{\rm DE}\) & \(l_{\rm EI}\) & \(l_{\rm OC}\) & \(l_{\rm BC}\) & \(l_{\rm BJ}\) \\ \hline
15 & 12 & 160 & 12 & 12 & 255 & 407 \(-\) 1\(\times t\) & 75 & 120 \\ \hline \end{tabular}
\end{table}
Table 1: Kinematic parameters of mechanism (unit: mm).  
Figure 19: Part of the synchronous connecting rod.  
\[{a_{1}}^{2}={a_{ix}}^{2}+{a_{iy}}^{2} \tag{12}\]  
where \(\omega\), \(v\), and \(a\) denote the angular velocity, velocity, and angular acceleration of the mass, respectively. \(r\) denotes the unit vector, \(\hat{s}\) denotes the slider displacement velocity vector, and \(\hat{\theta}\), \(\bar{\theta}\) denote the first- and second-order derivatives of the angle, respectively.  
The changes in kinematic parameters were computed, as depicted in Figure 21a-c. The 3D model generated using UG_NX 12.0 software was imported into ADAMS for the simulation. The resulting data were then cross-referenced with the MATLAB outcomes to validate the accuracy of the kinematic analysis. Figure 21d illustrates the angular displacement curves of the rod in both MATLAB and ADAMS software, demonstrating congruence and affirming the validity of the kinematic analysis theory.
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
## 3 STN-Homography
### Training and Results  
When the STN-Homography model was trained, we used the momentum optimizer with a momentum value of 0.9, batch size of 64, and an initial learning rate of 0.05. During the first 1000 training steps, we linearly increased the learning rate from 0.0 to the initial learning rate of 0.05. We then continued to train 110,000 steps. During this period, we updated the learning rate from 0.05 to 0.0 with the cosine decay method [28].  
We used two losses for the training of the STN-Homography, i.e., the L2 loss for the regressed output \(\overline{H}_{ba}\) and the L1 loss for the pixelwise photometric loss. To explore the impact of these two losses on the performance of the STN-Homography model, we conducted several experiments with different loss weights, while we kept the other training parameters unchanged. We tested the mean corner error of the trained model on the 30000 test samples generated from the COCO 2014 test set. The results are listed in Table 1. The mean corner error was obtained based on the calculation of the L2 distance between the target and the estimated corner locations, and are based on the average of the four corners and all the test samples.  
As it can be observed in Table 1, when the weight of the L2 loss remains unchanged, increasing or decreasing the weight of the L1 loss will lead to poor accuracy of STN-Homography model. In particular, if the weight of the L1 loss is increased, the accuracy of the model will become rather poor, thus indicating that the L2 loss has a more profound impact on the performance of our STN-Homography model. The experiment in which the L2 loss weight increased from 1.0 to 10.0 and in which the L1 loss weight 1.0 was kept unchanged also confirmed it, and achieved the smallest mean corner error of 4.85 pixels.  
There are two reasons for retaining the L1 loss in our STN-Homography model. One of these reasons is the fact that the L1 photometric loss can improve the network accuracy (as depicted in Table 1 when the L2 loss weight is kept unchanged to a value of 1.0, and when the L1 loss weight is increased from 0.1 to 1.0, thus resulting in the decrease of a mean corner error from 6.21 pixels to 5.83 pixels). The other reason is the fact that we can conduct semi-supervised training with the L1 photometric loss that allows some missing training samples compared to the ground truth \(\overline{H}_{ba}\) (as depicted in [25], which only uses the L1 photometric loss to conduct unsupervised training).
## 6 Numerical Simulation, and Experiment
### Behavior under Uncorrelated Sources  
The SHANM based approach is compared with the baselines using uncorrelated sources. It is assumed two equal power independent sources arrive at the SAA from \((\theta,\varphi)=(30^{\circ},60^{\circ})\) and \((40^{\circ},130^{\circ})\). A total of 250 snapshots were employed per trial and SNR values varied from 0 to 20 dB.  
The RMSE values of the SH-ANM-ESPRIT, TSDA, SH-ESPRIT, and CV-VSBL approaches are as depicted in Figure 3. For azimuth, SH-ANM-ESPRIT gives a similar level of accuracy as the TSDA approach, and gives better performance in elevation estimation. In addition, the proposed method exhibits superiority in performance when compared with other methods. It is important to note that the \(l_{1}-norm\) dependent approach shows worse performance among sparsity-based approaches. This could be because of the problem faced while choosing the regularization variable, and the impacts to the truncating SVD processing.  
Furthermore, a comparison of the methods against the number of snapshots at 0 dB SNR is presented. Two uncorrelated sources are positioned at \((\theta,\varphi)=(30^{\circ},60^{\circ})\) and \((36^{\circ},138^{\circ})\). Figure 4 shows that SH-ANM-ESPRIT exhibits the best performance.  
Figure 3: Comparison of performance at various SNRs under uncorrelated sources at \((\theta,\varphi)=(30^{\circ},60^{\circ})\) and \((40^{\circ},130^{\circ})\). SNR is from 0 dB to 20 dB, 250 snapshots. (**a**) Azimuth. (**b**) Elevation.  
Figure 2: Error in DOA estimation against angle of separation in an ideal (no noise) single snapshot scenario.  
In addition, the SH-ANM-ESPRIT method is compared with the \(l_{1}-norm\), CV-VSBL, TSDA, and SH-ESPRIT methods against the angle of separation in terms of resolution probability. The interval of the grid is between \(1^{\circ}\) with 3 dB SNR. Assuming two uncorrelated sources are at \((\theta,\varphi)=(30^{\circ},60^{\circ})\) and \((40^{\circ}+\theta,60^{\circ}+\theta)\), the elevation of two signals requiring solution and that both \(|\hat{\theta_{1}}-\theta_{1}|\) and \(|\hat{\theta_{2}}-\theta_{2}|<|\theta_{1}-\theta_{2}|/2\), and the probability of resolution of the azimuth could be described in a similar way.  
In all, the SH-ANM-ESPRIT approach performs better than other approaches, as demonstrated in Figure 5. Therefore, SH-ANM-ESPRIT shows a super-resolution ability of DoA estimation.
# EEG-Based Attention Classification for Enhanced Learning Experience
## 2 Materials and Methods
### Brain Functional Network Analysis (BFNs)  
Binary brain functional networks (BFNs) were constructed from the thresholded adjacency matrices and analyzed using graph-theoretic metrics. The nodes of the network represent EEG electrodes, and the edges represent functional connections based on the synchronization of EEG signals between pairs of electrodes.  
The following graph-theoretic metrics were calculated:  
* _Network Degree (ND):_\(ND_{i}=\sum_{j=1}^{N}A_{ij}\), which represents the total number of edges connected to node \(i\), where \(A_{ij}\) is the corresponding element in the adjacency matrix.
* _Clustering Coefficient (Cc):_\(Cc_{i}=\frac{2T_{i}}{k_{i}(k_{i}-1)}\), where \(T_{i}\) is the number of triangles through node \(i\), and \(k_{i}\) is the degree of node \(i\). This metric evaluates the local interconnectivity or cliquishness of a node's neighborhood.
* _Path Length (PL):_\(PL=\frac{1}{N(N-1)}\sum_{i\
eq j}d_{ij}\), the average of the shortest path lengths \(d_{ij}\) between all pairs of nodes \(i\) and \(j\). It reflects the network's overall routing efficiency.
* _Global Efficiency (GE):_\(GE=\frac{1}{N(N-1)}\sum_{i\
eq j}\frac{1}{d_{ij}}\), which quantifies the efficiency of information exchange across the whole network by considering the inverse of the shortest path lengths.
* _Network Density (NDn):_\(NDn=\frac{2E}{N(N-1)}\), where \(E\) is the number of existing edges and \(N\) is the number of nodes. This metric describes how densely the network is connected.
* _Small-World Network Coefficient (SW):_ The small-world property was quantified using the small-worldness coefficient: \[SW=\frac{\gamma}{\lambda},\quad\gamma=\frac{C_{\text{real}}}{C_{\text{rand}}}, \quad\lambda=\frac{L_{\text{real}}}{L_{\text{rand}}}\] where \(C_{\text{real}}\) and \(L_{\text{real}}\) are the clustering coefficient and characteristic path length of the actual brain network, and \(C_{\text{rand}}\), \(L_{\text{rand}}\) represent the same metrics computed from randomized networks that preserve node degree distributions.
* _Small-Worldness Criterion:_ The network is considered to exhibit small-world characteristics when: \[\frac{C_{\text{real}}}{L_{\text{real}}}>\frac{C_{\text{rand}}}{L_{\text{rand}}}\] indicating higher clustering and similar or shorter path lengths than a random graph. This comparison assesses whether the constructed brain network exhibits small-world properties relative to randomized surrogate networks.