esa-sceva/satcom-chunk-collection huggingface dataset:
# Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
## 3 Proposed Method
### Deep Learning Model Design  
We process the HRIR data provided in the database to obtain HRTF of different frequencies and then use spherical harmonic basis functions and SH coefficients to represent HRTF. Then a deep learning model is designed to predict the SH coefficients using head, torso parameters, frequency index, and the ear features output by the VGG-Ear Model.  
Figure 6 shows the block diagram of the model. The input of the deep learning model is the ear features obtained by the VGG-Ear model (10-d vector), head and torso parameters from CIPIC database (17-d vector), and frequency index (44-d vector). The specific frequency index is 44 frequency points from 1 to 87 with a uniform interval of 2, and the corresponding frequency range is 0-15 kHz. These parameters are separately input into the fully connected (FC) layer for encoding, and another FC layer is used to fuse the information. Finally, it is sent to the 1D convolutional neural network to obtain the predicted SH coefficients.  
Figure 5: The architecture diagram of the VGG-Ear.  
Figure 6: Block diagram of a deep learning network for predicting SH coefficients.  
By incorporating frequency information, the proposed personalization method can obtain HRTF prediction results for all 1250 directions (25 azimuths and 50 elevations) in one training. Therefore, the proposed method requires fewer models and fewer parameters in terms of the number of models to train.
###### Abstract  
Background: Dental bleaching has become an everyday procedure for treating teeth presenting discoloration, as a therapy on its own or as a part of restorative dentistry. The most widely used bleaching products available for vital teeth are hydrogen peroxide and carbamide peroxide, marketed as a range of products adapted to different treatment modes. The purpose of the study was to evaluate the clinical whitening efficacy of four high-concentration bleaching products for vital teeth applied in single in-office sessions. Methods: This clinical trial included 40 patients divided into four groups according to the concentration of the hydrogen peroxide-based (HP) product used (Group 1: HP 35%; Group 2: HP 37.5%; Group 3: HP 38%; Group 4: HP 40%). Each patient received one in-office whitening treatment of maxillary and mandibular anterior teeth (incisors, canines, and premolars), making a total of 791 teeth. The color changes produced in central incisors and canines were measured. Results: All groups underwent significant color changes from initial to final evaluations for color parameters L*, a*, and b*. No significant differences between the four groups were found for either the L* or a* parameters. Analysis of the b* parameter found significant differences between the HP 38% group and the HP 35% group. No significant differences in \(\Delta\)E were found between the four groups (ANOVA \(p\) = 0.174). HP 38% obtained the highest \(\Delta\)E (4.63), while HP 40% obtained 4.01. Conclusions: A single in-office whitening session, regardless of the bleaching product used, modifies tooth color effectively. All four products achieved increases in the L* parameter and significant reductions in parameters a* and b*. The \(\Delta\)E did not show significant differences between the four groups. In-office whitening with high concentration hydrogen-peroxide-based products was found to be an effective treatment for moderate and severe dental discoloration.  
combined vital tooth whitening; carbamide peroxide; tooth discoloration; dental bleaching; in-office; hydrogen peroxide
# Racial Identity-Aware Facial Expression Recognition Using Deep Convolutional Neural Networks
## 4 Results and Discussion
### Results Compiled without Racial Identity Aware Deep Convolutional Neural Network (DCNN)
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class Label** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline \multicolumn{5}{c}{**Moroccans**} \\ \hline Angry & 0.92 & 0.86 & 0.89 & 0.86 \\ \hline Disgust & 0.87 & 1.00 & 0.93 & 1.00 \\ \hline Fear & 0.95 & 0.95 & 0.95 & 0.95 \\ \hline Happy & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Neutral & 0.95 & 0.95 & 0.95 & 0.95 \\ \hline Sad & 1.00 & 0.94 & 0.97 & 0.94 \\ \hline Surprise & 0.94 & 0.94 & 0.94 & 0.94 \\ \hline
**Average** & **0.94** & **0.94** & **0.94** & **0.94** \\ \hline \multicolumn{5}{c}{**Caucasians**} \\ \hline Angry & 0.94 & 0.89 & 0.92 & 0.89 \\ \hline Disgust & 0.95 & 1.00 & 0.97 & 1.00 \\ \hline Fear & 1.00 & 0.67 & 0.80 & 0.67 \\ \hline Happy & 1.00 & 0.94 & 0.97 & 0.94 \\ \hline Neutral & 0.82 & 0.78 & 0.80 & 0.78 \\ \hline Sad & 0.65 & 0.87 & 0.74 & 0.87 \\ \hline Surprise & 0.95 & 0.95 & 0.95 & 0.95 \\ \hline
**Average** & **0.90** & **0.87** & **0.87** & **0.87** \\ \hline \multicolumn{5}{c}{**Taiwanese**} \\ \hline Angry & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Disgust & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Fear & 1.00 & 0.94 & 0.97 & 1.00 \\ \hline Happy & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Neutral & 0.93 & 1.00 & 0.97 & 1.00 \\ \hline Sad & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Surprise & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline
**Average** & **0.99** & **0.99** & **0.99** & **0.99** \\ \hline \multicolumn{5}{c}{**Americans**} \\ \hline Angry & 0.53 & 0.66 & 0.59 & 0.67 \\ \hline Disgust & 0.88 & 0.89 & 0.89 & 0.89 \\ \hline Fear & 0.71 & 0.83 & 0.77 & 0.83 \\ \hline Happy & 0.95 & 0.95 & 0.95 & 0.95 \\ \hline Neutral & 0.82 & 0.67 & 0.74 & 0.67 \\ \hline Sad & 0.60 & 0.60 & 0.60 & 0.60 \\ \hline Surprise & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline
**Average** & **0.78** & **0.80** & **0.79** & **0.80** \\ \hline \multicolumn{5}{c}{**Japanese**} \\ \hline Angry & 0.75 & 1.00 & 0.86 & 1.00 \\ \hline Disgust & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Fear & 1.00 & 0.83 & 0.91 & 0.83 \\ \hline Happy & 0.75 & 0.60 & 0.67 & 0.60 \\ \hline Neutral & 1.00 & 1.00 & 1.00 & 1.00 \\ \hline Sad & 0.70 & 0.64 & 0.67 & 0.64 \\ \hline Surprise & 0.83 & 1.00 & 0.91 & 1.00 \\ \hline
**Average** & **0.86** & **0.86** & **0.86** & **0.86** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Precision, recall, F1 score and accuracy score of our proposed method multicultural groups.
# Semantic Similarity Analysis for Examination Questions Classification Using WordNet
## 4 Result and Discussion
### Verb Extraction by Verb Form Analysis and Rules Applied  
Verb extraction (VE) is a module that identifies the type of word to be extracted from a question, such as \"VB\", \"VBD\", \"VBG\", \"VBN\", \"VBP\", and \"VBZ\". These verb forms are generally a verb but with additional conditions to differentiate themselves from each other, as shown in Table 6. Experiments were conducted to determine the accuracy of \"VB\" extracted from the questions with the verb tagged manually by an expert. Based on the result from the experiment conducted on 200 examination questions, an 80.5% accuracy was obtained.  
There were 8% of errors due to a lack of verbs tagged with \"VB\" in the questions. The errors always occurred in questions that had a verb as the first word of the sentence, but this was tagged as a noun by the POS tagger such as \"state\", \"design\", \"name\", \"list\",  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Verb Form** & **Verb Form Description** & **Verb Example** \\ \hline VB & Verb, base form & Take \\ \hline VBD & Verb, past tense & Took \\ \hline VBG & Verb, present participle & Taking \\ \hline VBN & Verb, past participle & Taken \\ \hline VBP & Verb, non-third person singular present & Take \\ \hline VBZ & Verb, third person singular present & Takes \\ \hline \hline \end{tabular}
\end{table}
Table 6: Verb forms description and its example.  
\"point\", and \"estimate\". This could be rectified by setting Rule 1, converting the first-word noun into a verb and referring to Table 7. Another 3% of errors was due to no \"VB\" being tagged in the examination question, though \"VBD\", \"VBG\", \"VBN\", \"VBP\", and \"VBZ\" were tagged. For example, \"calculate\" was tagged as a \"VBP\" instead of \"VB\". Rule 2 was applied to extract the verb with \"VBP\" and \"VBZ\". After setting these two rules, the accuracy of the correct verb identified was optimized from 80.5% to 91.5%.
## 3 Improved CKF Localization Algorithm Based on Volume Criterion
### SVD Method Decomposes Estimated Covariance Array
#### 3.1.1 State Prediction Section  
The estimated covariance is decomposed using the SVD method as follows (11):  
\[\mathbf{P}_{k-1|k-1}=\mathbf{U}_{k-1}\bigg{[}\begin{array}{cc}\mathbf{S}_{k-1}&0\\ 0&0\end{array}\bigg{]}\mathbf{V}_{K-1}^{\mathrm{T}} \tag{11}\]  
where \(\mathbf{S}_{k-1}\) generally denotes the diagonal matrix. \(S_{i}\) represents the eigenvalue of \(P_{k-1|k-1}\). The volume point is calculated as follows (12):  
\[\mathbf{X}_{i,k-1|k-1}=\mathbf{U}_{k-1}\sqrt{\mathbf{S}_{k-1}}\varepsilon_{i}+\mathbf{\hat{x} }_{k-1|k-1} \tag{12}\]  
where \(\varepsilon_{i}=\sqrt{n}[1]_{i}\). For a two-dimensional sphere (\(n=2\)), the sphere has intersections with both axes, as follows: \([1]_{i}=\Big{[}(1,0)^{\mathrm{T}},(0,1)^{\mathrm{T}},(-1,0)^{\mathrm{T}},(0,- 1)^{\mathrm{T}}\Big{]}\). \([1]_{i}\) denotes the \(i\)th column of the set of \([1]\).  
\(\mathbf{X}_{i,k-1|k-1}^{*}\) represents the new sampling point obtained by propagating the volumetric point through the state equation.  
\[\mathbf{X}_{i,k-1|k-1}^{*}=F(\mathbf{X}_{i,k-1|k-1}) \tag{13}\]  
The further state prediction value \(\mathbf{\hat{x}}_{k|k-1}\) and prediction covariance \(\mathbf{P}_{k|k-1}\) at moment \(k\) are as follows (14):  
\[\left\{\begin{array}{l}\mathbf{\hat{x}}_{k|k-1}=\frac{1}{2i}\sum\limits_{i=1}^{ 2n}\mathbf{X}_{i,k-1|k-1}^{*}\\ \mathbf{P}_{k|k-1}=\frac{1}{2i}\sum\limits_{i=1}^{2n}\mathbf{X}_{i,k-1|k-1}^{*}\mathbf{X}_ {i,k-1|k-1}^{*T}-\mathbf{\hat{x}}_{k|k-1}\mathbf{\hat{x}}_{k|k-1}^{T}+\mathbf{Q}_{k-1} \end{array}\right. \tag{14}\]
## 3 Results and Discussion
### Construction of Datasets  
#### 3.3.1 Data Image Source  
The GF-6 remote sensing image is used as the data source. The multi-spectral image with a resolution of 8 m and the panchromatic image with a resolution of 2 m are preprocessed, respectively, and then fused and registered to form a multi-spectral image with a resolution of 2 m.
# Seasonal Characteristics of Particulate Matter by Pollution Source Type and Urban Forest Type
## 3 Results
### Analysis of \(PM_{10}\) Concentrations in Each Plot  
#### 3.2.1 ANOVA of the Plot PM Concentration Results  
The Kruskal-Wallis test was employed to assess the difference in the median PM concentrations among groups. In the last row in Table 2, the chi-square value, which serves as the test statistic following a chi-square distribution, indicates whether the null hypothesis can be rejected. The results of the KW test in this study reveal significant differences in median PM concentrations among the four plots across all seasons (Table 2). The post hoc test was conducted to identify which groups exhibited significant differences in their median values. The results revealed distinct patterns: from the beginning of the year to early spring, no median difference was observed in forest areas. However, during spring, the median concentrations in both the road and downtown plots were higher than those in forest plots. In summer, significant differences were noted among the downtown, road, forest interior 1, and forest interior 2 plots, with downtown plots exhibiting the highest median concentration, followed by road, forest interior 1, and forest interior 2 plots in decreasing order. Similarly, at the end of the year, while there were no significant differences in median concentrations between the road, forest interior 1, and downtown areas, the results were significant, with each plot recording higher medians than those of forest interior 2. From the beginning of the year to early spring, there were no noticeable differences in the median PM concentrations between the plots. However, from spring to the end of the year, the PM concentrations in interior forest plot 1 remained consistently lower than those in urban and pollution source areas. Table 2 revealed the results in the industry complexes, and the post hoc test results highlighted distinct patterns. At the beginning of the year, industrial complexes and forests exhibited higher concentrations, with subsequent decreases in downtown and residential areas. In early spring, downtown areas displayed the highest concentrations, followed by industrial complexes, and residential areas had the lowest. PM concentration changes decreased in the order of industrial areas, forests, and residential areas throughout spring. Industrial complexes and downtown areas maintained higher PM concentrations than forests and residential areas. During summer, concentrations decreased in the order of downtown areas, industrial complexes, forests, and residential areas. In autumn, both industrial complexes and downtown areas showed higher concentrations than those of forests and residential areas. At year end, industrial complexes recorded higher concentrations than residential areas. Except for the winter season (the beginning of the year and at year end), the PM concentration of the forest was lower than the industrial area.  
Figure 4: Seasonal boxplot of PM\({}_{10}\) concentrations by pollution source.
# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
###### Abstract  
In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content's early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.  
machine learning; content pre-caching; zone; congestion control; VANET 2022 22 22 22 9157. [[https://doi.org/10.3390/s22239157](https://doi.org/10.3390/s22239157)]([https://doi.org/10.3390/s22239157](https://doi.org/10.3390/s22239157))
# Augmented Reality for PCB Component Identification and Localization
## 5 Results  
### Experiment 1  
The results for Experiment 1 are presented across three runs, each focusing on different variables to evaluate the efficiency of the HoloLens(tm) system compared to traditional methods in component localization tasks. As shown in Table 2, in Run 1, the impact of the method sequence was analyzed by dividing participants (\(n=10\)) into two groups: Group A performed tasks using the traditional method first, followed by the HoloLens(tm) method, while Group B followed the reverse sequence. Run 2 investigated the influence of component size (SD card and resistor) on localization times. Finally, Run 3 explored the effect of user expertise, comparing performance between experts (\(\geq\)8 years of experience) and non-experts (<8 years of experience).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **Description** \\ \hline Participant ID & Unique identifier for each participant (P1–P10) \\ Viewing Angle & One of the three predefined viewing angles \\ Component & Identifier for the PCB component being located \\ Time Taken & Time (in seconds) to complete the identification task \\ Screenshot Filename & File name of the screenshot captured \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of the Dataset Collected During the Experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Run** & **Focus** & **Groups** & **Participants** & **Variable** & **Analysis Focus** \\ \hline \multirow{4}{*}{Run 1} & \multirow{4}{*}{10 Components (All)} & Group A: Traditional First, & \multirow{4}{*}{5} & Method Sequence: & Impact of method \\  & & HoloLens(tm) Second (\(n=5\)) & & Traditional \(\rightarrow\) & sequence on \\  & & & HoloLens(tm) & localization time. \\ \cline{3-5}  & & Group B: HoloLens(tm) & & \multirow{2}{*}{5} & HoloLens(tm) & sequence on \\  & & Traditional Second (\(n=5\)) & & Traditional & localization time. \\ \hline \multirow{4}{*}{Run 2} & 2 Components (SD (Card and Resistor) & \multirow{2}{*}{Same Participants (\(n=10\))} & \multirow{2}{*}{10} & Component Size: SD (Card vs. Resistor) & Effect of \\  & & & & & component size on \\ \cline{1-1} \cline{3-5}  & 10 Components (All) & Experts (\(\geq\)8 years, \(n=6\)) and (\(<\)8 years, \(n=4\)) & & \multirow{2}{*}{10} & Expertise: Experts vs. (Non-Experts) & Effect of expertise (on localization time. \\ \cline{1-1} \cline{3-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment 1 Overview and Analysis Focus.  
The findings for each run are summarized below, with key trends and statistical comparisons presented in tables and figures.
## 2 Material and Methods
### Prediction Error Metrics  
The network performance function was determined by the mean square error (_MSE_) that is represented in Equation (3). For comparisons, the statistical approach of the mean absolute percentage error (_MAPE_) value was utilized and represented in Equation (4) [16].  
\[MSE=\frac{1}{n}\sum_{i=1}^{N}(t_{i}-o_{i})^{2}, \tag{3}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{N}\left|\frac{t_{i}-o_{i}}{o_{i}}\right|*100, \tag{4}\]  
where \(t\) represents the goal value, \(o\) represents the output, and \(N\) is the number of samples.
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 2 Model Description and Problem Formulation
### Target Dynamic and Measurement Models
In this paper, the measurement error covariance in dynamic model \(m\) is defined as \(\mathbf{R}_{n,t}^{m}\), and \(\mathbf{R}_{n,t}^{1}<\mathbf{R}_{n,t}^{2}<\cdots<\mathbf{R}_{n,t}^{M}\), where \(\mathbf{R}_{n,t}^{1}=\text{diag}(\sigma_{r,n}^{2}(t),\sigma_{\theta,n}^{2}(t))\) and \(<\) represents that each element in the left matrix is smaller than each corresponding element in the right matrix.
## 4 Cryptographic Algorithms  
### Prince  
PRINCE [35] is a lightweight symmetric block cipher with a substitution-permutation network (SPN) structure and is based on the so-called FX construction. It was designed to target low latency and unrolled hardware implementations. According to the author, when compared with the advanced encryption standard (AES), PRINCE was able to operate at much higher frequencies while utilizing less area with the same timing constraints and technologies.  
The block size of PRINCE is 64 bits, and the key size is 128 bits. The key is split into two 64-bit keys denoted as \(k_{0}\) and \(k_{1}\),  
\[k=k_{0}||k_{1} \tag{1}\]  
A sub-key \(k^{\prime}_{0}\) is then derived from \(k_{0}\), extending the key to 192 bits.  
\[\left(k_{0}||k^{\prime}_{0}||k_{1}\right):=\ (k_{0}||(k_{0}\gg 1)\ \oplus\ (k_{0}\gg 63)||\ k_{1}) \tag{2}\]  
The input is XORed with \(k_{0}\), and then processed via a core function, PRINCE\({}_{\text{core}}\), using \(k_{1}\). The output of the PRINCE\({}_{\text{core}}\) function is XORed by \(k^{\prime}_{0}\) to produce the final output. The decryption is conducted by exchanging \(k_{0}\) and \(k^{\prime}_{0}\) and using \(k_{1}\) XORed with a constant denoted as alpha in the core function. This overall procedure is depicted in Figure 1.  
The PRINCE\({}_{\text{core}}\) performs the encryption process, depicted in Figure 2, in twelve rounds, divided into five \"forward\" rounds, five \"backward\" rounds, and a middle round (that is counted as two). A forward round starts with a non-linear substitution layer S, a linear layer M of permutation, and then the XORed operation with the round constant and \(k_{1}\). The \"backward\" rounds are the identical inverse of the \"forward\" rounds with different round constants.  
In terms of hardware implementation, the target is to have a pipeline architecture with combinational sub-modules for the S layer and M layer. The algorithm was designed to be unrolled, which means that it should not contain any conditional loops. The key expansion and addition function (XOR) should then be simple to implement.  
Figure 1: PRINCE encryption process of a plaintext message \(m\) to a ciphertext \(c\), using \(k_{0}\) and \(k^{\prime}_{0}\) as whitening keys.  
Figure 2: Encryption process of PRINCE\({}_{\text{core}}\).
## 5 Conclusions, Limitations, and Future Recommendations
### Limitations  
One notable study limitation pertains to the inability to train the SVM algorithm on the aforementioned four matrices. Additionally, due to the lack of access to a supercomputer, the scholars of this study faced constraints in conducting broader testing.