esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach for Selecting the Most Explanatory Features for Facial Expression Recognition
## 3 Methodology
### Feature Extraction Methods
#### 3.1.1 Local Features  
The local features were established through the extraction of supervised features from facial patches, defined as the regions of the face that are active during different facial expressions. We used the following methods for the extraction of local features:  
* Gabor wavelet filters (GW): This technique has previously been applied successfully for facial expression recognition [18; 30]. In our work, 68 landmark points were selected. For each point, a patch sized \((2k+1)\times(2k+1)\) was used to compute the feature vector. Four scales and eight orientations were used to calculate the Gabor kernels, where \(k=7\). This selection generated a vector of 2176 (\(68\times 4\times 8\)) elements.
* Local binary patterns (LBP): this system has been widely used as a robust illumination-invariant feature descriptor [19; 27; 36; 37]. This operator generates a binary number by comparing neighboring pixel values with the center pixel value. The uniform LBP and rotation-invariant uniform LBP [38] were also used in the experiment. These methods generated a vector that was the same size as the image (\(256\times 256\)).
* Histograms of oriented gradients (HOG): This concept has also been successfully applied in facial expression recognition [19; 39]. The basic idea of HOG features is that the local object's appearance and shape can often be well-characterized by the distribution of local intensity gradients or edge directions, even without precise knowledge of the corresponding gradient or edge positions. The orientation analysis is  
Figure 1: Proposed classifier system, divided into three sections: feature extraction methods, sparse representation classification, and combination rules.  
robust to changes in illumination, as the histogram possesses translational invariance. The associated vector size was \(256\times 256\times 2\).
* Local phase quantization (LPQ): The LPQ feature [18; 40] is a blur-robust image descriptor. The LPQ descriptor is based on the intensity of low-frequency phase components with respect to a centrally symmetric blur. Therefore, LPQ employs the phase information obtained from the short-term Fourier transform, which is locally computed on a window around each pixel of the image. The vector size was \(256\times 256\).
* RAW: The image intensity has also been used as a feature vector (RAW) [17; 41]. Here, the vector size was \(256\times 256\).
## 5 Conclusions, Challenges, and Future Research Directions
### Future Research Directions  
To advance this work and address the identified challenges, the following directions are proposed:  
1. Using reanalysis data (e.g., CERRA) and satellite data (e.g., SENTINEL-5P from ESA), Copernicus program, MODIS (from NASA with Terra and Aqua satellites), in complementarity with data given by the National Environmental Agencies and independent low-cost sensor networks and applying fast-evolving technology for a better local understanding of the pollution phenomenon at a regional level.
2. Using other more refined hybrid machine learning techniques will improve air pollution modelling, prediction, and forecasting.
3. Developing triggering AI warnings when pollutant threshold values are exceeded. If authorities wisely distribute these warnings, they might become a norm to protect pollution-sensitive categories and help collaborate between local decision-makers, doctors, patients, and communities.  
Conceptualization, Y.E.M. and M.T.U.; methodology, Y.E.M.; software, Y.E.M.; formal analysis, Y.E.M. and M.T.U.; investigation, Y.E.M. and M.T.U.; resources, M.T.U.; writing--original draft preparation, Y.E.M. and M.T.U.; writing--review and editing, Y.E.M. and M.T.U.; visualization, Y.E.M.; supervision, Y.E.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: (a) The dataset, models, or codes supporting this study's findings are available from the corresponding author upon reasonable request. (b) All data, models, and code generated or used during this study appear in the submitted article.  
The Fulbright program's support is gratefully acknowledged. The dataset was downloaded from the National Environmental Agency (Romania) website, and the authors expressed their gratitude for the opportunity to work with open-source data. The authors declare no conflicts of interest.
## 4 Rate-Distortion Analysis of Using CS Encoding in WSN
### Simulation Set-Up and Rate-Distortion Criteria  
First, we consider a one-hop WSN, and use real temperature, humidity, and illumination signals, gathered by the 54-node WSN located in the Intel Berkeley Research Lab [7]. For the CS framework, it is assumed that the sensed signal is sparse in the DCT domain [6] and for sensing matrix construction, \(M\) random waveforms of an \(N\times N\) Noiselet [40] are used.  
The \(M\) quantized CS coefficients are assumed to be entropy encoded [41]. Therefore, it is reasonable to assume that the average rate spent to encode each signal sample is  
\[R=\frac{M}{N}\times H(\mathcal{Y}_{q}),\text{ bits/coeff}, \tag{13}\]  
where \(H(\mathcal{Y}_{q})\) denotes the entropy of the quantized coefficients set \(\mathcal{Y}_{q}\), which in our experiments is empirically obtained, and \(M\) is the quantity of transmitted coefficients per signal block of length \(N\)  
Figure 1: Compressive sensing (CS) coding/decoding in the wireless sensor network (WSN) scenario.  
This model (Equation (13)) is adopted in this work because it can be used to compare the rate for different \(M\), \(N\) and quantizer bit-depths \(B\).  
Distortion is defined in terms of the Normalized Mean Squared Error (NMSE)  
\[\text{Normalized MSE}(dB)=10\log_{10}\left(\frac{E\left[(\mathbf{x}_{i}- \hat{\mathbf{x}}_{i})^{2}\right]}{||\mathbf{x}_{i}||^{2}}\right). \tag{14}\]  
In Equation (14), \(E[\cdot]\) is the expected value operator.
# Strength Enhancement in Fused Filament Fabrication via the Isotropy Toolpath
## 1 Introduction  
The fused filament fabrication (FFF) process uses a heat-up nozzle to liquefy the thermoplastic material and deposit it in a layer-wised manner. Though it is being developed for decades and extends the manufacturability and design space compared with traditional manufacturing, the inconsistent and unsatisfactory mechanical properties of the build still hinder it from being accepted by major industries beyond the prototyping stage. With the requirement of being a functional part, mechanical strength is essential but difficult to control in the FFF process. The current workflow (Figure 1) in the FFF process generates the build plan for a given 3D model. The build outcomes heavily rely on the manual setup of the process variables in the process planning stage. A large variety of process variables--hatch spacing, building orientation, and extrusion rate--could control and optimize the process outcome, such as the mechanical strength. Table 1 provides an overview of the current strategy of enhancing mechanicalstrength by mainly focusing on optimizing process parameters and hybridizing different toolpath strategies. Table 1 presents the current methods to enhance process qualities in terms of build time, surface finish, and mechanical strength by varying process parameters, such as layer thickness, printing speed, and processing environment. However, the analysis of the fabrication strategy for the deposition path is not being studied. All of the above-mentioned studies mainly utilize the common linear/contour toolpath. However, these toolpath strategies will cause long filaments to pile in a parallel manner in a plane. Ultimately, this can cause the material to display anisotropic behavior. Thus, such in-plane anisotropy would induce poor mechanical strength. Besides the effect from process parameters and working environments, the leading causes of insufficient strength are low infill density, weak interfacial bonding, and in-plane anisotropy of the material [9; 10]. These physical phenomena are significantly influenced by the toolpath strategy and can be optimized by altering manufacturing strategy to achieve a desired mechanical strength. Studies reveal that higher density can lead to a stronger component; thus, a 100% solid infill is desired in our research to enhance the mechanical strength. In order to achieve a 100% density infill, two major toolpaths are commonly used in today's FFF process--the linear and contour toolpath [11]. Other existing toolpaths, such as honeycomb, triangle, cubic, and Gyroid [12], are hard to achieve a solid infill for any given geometry. All of the above-mentioned toolpath strategies can fulfill 100% infill.
## 4 Methodology  
As described in the previous section, the definition and selection of suitable terminal nodes is an important step in applying GP to develop new DRs. This is probably also the most difficult step, as it requires a certain amount of expert knowledge about the problem in order to define these terminal nodes in a meaningful way. However, even with good expert knowledge, there is no guarantee that the defined nodes will enable GP to obtain good priority functions. Therefore, the aim of this study is to investigate the process of designing the terminal set in more detail and determine how the inclusion and construction of different features and nodes affect the performance of GP.  
This investigation is divided into three phases, each focusing on the influence of the following factors: (1) atomic terminal nodes, (2) composite terminal nodes constructed based on the frequency of occurrence in the previous phase, and (3) composite terminal nodes designed manually. In the following, we describe each phase in detail.
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 5 Evaluation
### Results
#### 5.4.3 Mean Squared Displacement (MSD)  
A measurement of the variance between a particle's position and a reference position over time is the MSD. The most common method to assess the spatial extent of random mobility is to measure the area of the system that the random walker has explored. The distance between the two points is then square-calculated, and its value is then added to a list of MSD values. The particle's average displacement increases in a manner that is proportional to the number of time steps over time.  
Figure 7 shows the relationship between the MSD and the number of steps. The x-axis represents the number of steps taken in the random walk, and the y-axis represents the MSD. This figure provides insight into how the distance between points changes as more steps are taken. If the relationship between the MSD and the number of steps is linear, the movement is random. On the other hand, if the relationship is non-linear, it suggests that the movement is not random. As observed in the Xorshift and Mersenne twister QRNG results, the MSD exhibits a non-linear relationship with the number of steps, showing that these PRNG algorithms are not truly random. In contrast, the QRNG results display a linear MSD-to-steps relationship, confirming true randomness.  
Figure 7: Results of mean squared displacement to evaluate and compare the randomness of (**a**) Xorshift PRNG, (**b**) Mersenne twister PRNG, and (**c**) IDQ QRNG.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 5 Results and Discussion
### Discussion  
The experimental results suggest that optical flow is beneficial for binary classification tasks (e.g., two-class dataset) when appropriately optimized (Bi-LSTMOF-10). It enhances motion understanding and contributes to improved performance. However, excessively increasing the optical flow region leads to performance degradation due to excessive noise.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Accuracy (\%)** \\ \hline BiLSTM (No OF) & 92.00 \\ Bi-LSTMOF-10 & 54.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison on the 47-class dataset.  
Figure 9: Inconsistency matrices of the YOLOv8-Pose model on different datasets.  
For datasets with higher class diversity, optical flow does not provide an advantage and can even degrade classification quality. In the 13-class dataset, accuracy drops when optical flow is introduced, indicating the need for careful tuning. In the 47-class dataset, optical flow severely impacts performance due to noise values obtained for static signs, possibly by complicating training and obscuring key gesture features.  
These findings suggest that the effectiveness of optical flow depends on the complexity of the dataset. Although it helps in simpler cases, it requires careful optimization to avoid negative impacts on larger and more complex datasets, specifically, containing static classes.
# Elucidating the Mechanical Energy for Cyclization of a DNA Origami Tile
## 1 Introduction
This origami tile is designed in a square lattice and thus has an inherent structural curvature. In a previous publication [27], we used this tile in a series of experiments to study the energy needed for cyclization as functions of incubation temperature, origami size, and structural defect. The atomic force microscopy (AFM) measurements and mechanical analysis of elastic deformation were performed to provide insights into the reconfiguration, including relevant reaction rates and the bending spring constant of crossovers. While the experiment and elasticity theory revealed the overall aspects of the origami deformation, the detailed process was not known, because only the DNA conformations before and after the cyclization reaction were available. In the current study, we performed numerical simulations of the DNA origami tile cyclization by directly applying external forces. The multistep simulations of small, quasi-equilibrium deformations allowed us to elucidate the details of the entire cyclization process and calculate the mechanical energy associated with conformational change at each step. The computation was performed with coarse-grained molecular dynamics (MD) model in oxDNA, an open-source computational platform. The MD computation was also complemented by finite element method (FEM) simulations in CanDo, a free, online simulation software. The evolution of the simulated structure in each state shows how the DNA origami tile transforms gradually from its initial twisted conformation to the final cyclized configuration. The overall energy for the complete cyclization can be calculated by summing all the mechanical energies in the multistep simulations and is approximately 33.1 kcal/mol. In addition, we also calculated the energy needed to flatten the twisted DNA origami tile, which is ~26.7 kcal/mol. The energies for cyclization and flattening are in excellent agreement with previous experimental and theoretical results [27].
## 3 Dual-Band Doherty Power Amplifier
### \(\Pi\)-And T-Networks for Dual-Band DPA
The attempted designed dual-band DPA in [29] was found to work only in the lower band due to an incorrect estimate of the auxiliary output capacitance \(\mathrm{C_{ds}}\), where a low impedance was seen from the common node towards the auxiliary amplifier, and in this case, one of the Doherty operating conditions were not satisfied. Figure 8: Schematic dual-band circuit proposed by [18]. Figure 7: Dual-band DPA schematic [7]. Furthermore, dual-band three stage DPA was designed by [30]. The design was targeting 1.7 GHz and 2.4 GHz using three devices of 10 W Cree transistors so that 9 dB back-off can be achieved; the \(R_{opt}\) for the main, auxiliary 1 and auxiliary 2 power amplifier are 100 \(\Omega\), 50 \(\Omega\), and 50 \(\Omega\) respectively. The phase offset lines and the quarter wave transformer are designed and realized using \(\Pi\)-type. The designed PA was tested using wideband code division multiple access (WCDMA) and worldwide interoperability for microwave access (WiMAX) signals with 7 dB PAPR, where WCDMA signal had a bandwidth of 3.5 MHz and the WiMAX had a 5 MHz bandwidth. DPD was used to improve the linearity of the system. Form the discussed approaches; it can be noticed that both \(\Pi\)- and/or T-networks can be used as an equivalent circuit for the impedance inverter, at the same time, the shorted end of either network can be used as a dc feeding path for the designed power amplifier.
## 3 Model and Methods  
### Model Frame Structure  
Our backbone network for video-based Re-ID tasks follows the structural design of a pure transformer used in the Vision Transformer [5] and has the overall framework as shown in Figure 2. Based on the characteristics of a strong overall structure of images, we design a new method to partition image-feature embedding according to their correlations. Then, to improve the re-identification accuracy and robustness of a network, we design a simple and practical multi-grained method.  
The model is shown in Figure 2. Given a person video sequence \(v\), which contains \(t\) frames of images \(v\) = {\(v_{1}\), \(v_{2}\),..., \(v_{t}\)}, for any one image frame, the CAP module is first partitioned into \(N\) feature embeddings {\(F_{1}\), \(F_{2}\),..., \(F_{N}\)}, where the size of each feature embedding is 1 \(\times\)\(c\). A learnable random variable \(\Phi^{c}\in\mathbb{R}^{1\times c}\) is introduced as the final feature expression of the whole image. The \(\Phi^{c}\) and \(N\) feature embeddings are successively expanded and spliced horizontally to form the sequence \(F\) = {\(\Phi^{c}\), \(F_{1}\), \(F_{2}\),..., \(F_{N}\)}, \(F\in\mathbb{R}^{(1+N)\times c}\). Where \(\Phi^{c}\) is placed at the top of the sequence. To compensate for the problem of the position information in the image being lost after feature embeddings are flattened, a learnable random variable \(\Phi^{\text{loc}}\in\mathbb{R}^{(N+1)\times c}\) is introduced into the image to supplement the position information of the feature embeddings. Moreover, we follow the design in TransReID [7] to introduce a variable \(\Phi^{\text{cam}\cdot}\in\mathbb{R}^{1\times c}\) to save the camera information. To match the sequence size of \(\Phi^{c}\) and \(N\) feature embeddings, \(\Phi^{\text{cam}\cdot}\) is copied \(N\)+1 times and changed to \(\Phi^{\text{cam}}\in\mathbb{R}^{(N+1)\times c}\). For the person image, the first layer input of the network \(z^{0}\) can be expressed as follows:  
\[z^{0}=F+\Phi^{\text{loc}}+\Phi^{\text{cam}} \tag{1}\]  
We then use the \(l\)-1 transformer structure to calculate the input \(z^{0}\). Each transformer layer is composed of a multi-head self-attention (MSA) with a residual structure and a  
Figure 2: The overall framework of the APMG network.  
multi-layer perception (MLP) with a residual structure. For layer \(i\) of the network, the output \(z^{i}\) can be expressed as follows:  
\[z^{i\prime}=z^{i-1}+\text{MSA}(z^{i-1}) \tag{2}\]  
\[z^{i}=z^{i\prime}+\text{MLP}(z^{i\prime}) \tag{3}\]  
After \(l\)\(-\)1 transformer operations, the class token learns the feature information of the whole image and is finally divided by the MG module into eight class tokens with different levels of granularity.
## 4 New Form of QAOR and Optimal Value of \(r\)
### Determining \(r\) in QAOR  
**Theorem 6**.: _Given an initial value \(\mathbf{x}_{0}\), \(\mathbf{r}_{0}=\mathbf{b}-\mathbf{A}\mathbf{x}_{0}\) is known. For the QAOR method in Equation (36) if \(w\) is given, then_  
\[r=\frac{\mathbf{r}_{0}^{\mathrm{T}}\mathbf{A}\mathbf{v}_{0}}{\|\mathbf{A} \mathbf{v}_{0}\|^{2}} \tag{37}\]  
_is the optimal value of \(r\), where \(\mathbf{v}_{0}\) is obtained from a forward solution of_  
\[[(1+r)\mathbf{D}-w\mathbf{L}]\mathbf{v}_{0}=\mathbf{r}_{0}. \tag{38}\]  
Proof.: Upon letting  
\[\mathbf{v}_{0}=\frac{\mathbf{u}_{0}}{r},\]  
Equation (38) is a direct result of the second one in Equation (36) with \(k=0\). The first one in Equation (36) with \(k=0\) is  
\[\mathbf{x}_{1}=\mathbf{x}_{0}+r\mathbf{v}_{0}. \tag{39}\]  
Multiplying Equation (39) by \(\mathbf{A}\) and using Equation (11) yields  
\[\mathbf{r}_{1}=\mathbf{r}_{0}-r\mathbf{A}\mathbf{v}_{0},\]the squared norm of which is  
\[\|\mathbf{r}_{1}\|^{2}=\|\mathbf{r}_{0}\|^{2}-2r\mathbf{r}_{0}^{\mathrm{T}} \mathbf{Av}_{0}+r^{2}\|\mathbf{Av}_{0}\|^{2}.\]  
Let  
\[f=2r\mathbf{r}_{0}^{\mathrm{T}}\mathbf{Av}_{0}-r^{2}\|\mathbf{Av}_{0}\|^{2}\]  
be a decreasing function of the residual vector norm at the first step; we have  
\[\|\mathbf{r}_{1}\|^{2}=\|\mathbf{r}_{0}\|^{2}-f.\]  
We encounter a maximization problem to determine \(r\) via  
\[\max_{r}\{f=2r\mathbf{r}_{0}^{\mathrm{T}}\mathbf{Av}_{0}-r^{2}\|\mathbf{Av}_{ 0}\|^{2}\}.\]  
Adopting \(df/dr=0\), Equation (37) is derived.  
Equation (37) is a nonlinear equation for \(r\), because \(\mathbf{v}_{0}=[(1+r)\mathbf{D}-w\mathbf{L}]^{-1}\mathbf{r}_{0}\) obtained from Equation (38) is also a function of \(r\). It is a fixed point problem to seek the solution of \(r\), which can be solved by the iteration method to determine the optimal value of \(r\).
## 5 Discussion  
It is believed that the work presented here represents the first study of flaw detection in pipes using an ultrasonic-optical system and torsional ultrasonic guided waves. Torsional wave reception using FBG sensors was successful, and damage detection, monitoring, and localization were demonstrated. Additionally, one method for increased confidence for flaw detection was demonstrated, in which signals from a sparse array of optical fiber sensors were combined.  
A hybrid ultrasonic-optical inspection system has a wide variety of potential applications. One application is with permanently-mounted long-range ultrasonic guided wave pipe inspection collars. The optical fiber could be far from the excitation area and can increase the effective inspection range from a single sending collar position. Optical fiber sensors may also be used in places with limited access for traditional collars. The optical cable is very thin (as little as 80 \(\upmu\)m diameter for bare fiber), so it can easily be mounted under coatings. Additionally, the optical fiber sensors can withstand harsh environments such as high temperature, radiation, and high levels of electromagnetic interference (EMI). Finally, the optical fiber sensors have a much wider bandwidth than traditional piezoelectric or magnetostrictive sensors.  
Future work should focus on the development of ruggedized and miniaturized optical components and the demonstration of system effectiveness in field tests. Tests demonstrating an increase in inspection range on buried pipe could open avenues for additional applications. Optical fiber sensors provide unique advantages over traditional sensor types which will encourage their use in challenging inspection problems in the future.  
[PERSON] and [PERSON] contributed the ultrasonic guided wave and hybrid aspect of the research work including theory, experiment, and the write up. [PERSON], [PERSON], and [PERSON] contributed fiber optic Bragg grating experimental portion of the work and assisted in manuscript revision.  
The work carried out by [PERSON] and [PERSON] was funded by Naval Research Laboratory grant number N0001417 RX00182. The work carried out by [PERSON], [PERSON], and [PERSON] was carried out primarily at University of Nebraska-Lincoln and funded by the Office of Naval Research under Grant N000141712819 and Grant N000141410456.  
The authors would like to thank [PERSON] and [PERSON] at the Naval Research Laboratory for making this work possible. The authors would also like to acknowledge [PERSON] and [PERSON] for assistance with software and the experimental setup.  
The authors declare no conflict of interest.