esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 3 Methodology
### Dynamic Gates and Feature Fusion  
Dynamic gates and feature fusion are designed to integrate intent information, slot information, and text information. The main idea is to utilize intent and slot information to guide textual representation. The specific network structure is shown in Figure 2. The intent representation is fused with the slot representation and eventually represented as keys and values, which are used together with the text semantics to compute the attention weights. Specifically, 12 dynamic gates are specifically designed for fusing intent and slot features. It is worth noting that each of these 12 dynamic gates corresponds one of 12 encoders in the BERT [32] model, which helps to integrate the intent and slot features into the serialized textual representation while dynamically adjusting the importance of the intent and slot features. This complex fusion mechanism is able to efficiently integrate and reconcile text, intent, and slot information throughout the model architecture. Specifically, intent and slot features are spliced into a vector, and then fed into the 12 dynamic gates. The expression for the corresponding feature set vector is as follows:  
\[F=C(F_{I},F_{S}) \tag{7}\]  
where \(F\) denotes the feature after intent and slot fusion and \(C(\cdot)\) denotes the concatenation operation that stitches two 768-dimensional vectors into one 1536-dimensional vector.  
By resetting the weight of the input feature vector, we apply the dynamic gates mechanism to obtain the normalized vector \(G_{i}\) as follows:  
\[V_{i}=F\cdot w_{i}+b \tag{8}\]  
Figure 2: Schematic diagram of the dynamic gates and feature fusion module.  
where \(V_{i}\in[0,\,1],i\in[1,\,12]\) denotes the intent and slot fused features of the output of the \(i\)-th dynamic gate, \(\text{w}_{i}\in[0,\,1],i\in[1,\,12]\) denotes the weight of the \(i\)-th dynamic gate corresponding to the \(i\)-th layer encoder of BERT, and \(b\) denotes bias.  
Subsequently, the feature is split into key and value after passing through the activation function, the formulas of which are as follows:  
\[Key_{i},Value_{i}=S(f(V_{i})) \tag{9}\]  
where \(f(\cdot)\) denotes the activation function of Leaky ReLU and \(S(\cdot)\) denotes the splitting operation, dividing the 1536-dimensional vector output of the dynamic gates unit into two 768-dimensional vectors, which are the input of the BERT framework. \(Key_{i}\) and \(Value_{i}\) denote the key and value of the \(i\)-th visual feature corresponding to the \(i\)-th encoder of BERT, respectively.  
Finally, the contextual query (Q) provided by the textual modality meets the value (V) and key (K) based on the visual modality. Both of them are injected simultaneously into the BERT framework to compute the attention parameters between different modalities.
# In-Plane Monolithic Integration of Scaled III-V Photonic Devices
## 1 Introduction
For example, vertical-cavity surface-emitting lasers (VCSELs) are some of the most scaled and high-performance on-chip lasers, but they inherently emit out of plane and are therefore less suitable for waveguide integrated designs. We note that the desirable in-plane or lateral doping profile for the placement of electrical contact can be implemented for all the above methods in additional steps, either through the consecutive regrowth of III-V material on both sides or by ion implantation. This is the case for lateral-current-injection architectures [27] and might be suitable for the implementation of \(p\)- and \(n\)-regions for contacting and carrier injection or extraction. In this work, we will discuss the local integration of III-Vs self-aligned with silicon features using template-assisted selective epitaxy (TASE) [28; 29]. This technique was originally developed for electronic applications [30] and has more recently been extended to photonics [31; 32; 29; 33], where it enables the growth of complex nanostructures. We have explored several embodiments depending on the desired device geometry and applications. Figure 1: Schematic comparison for several integration methods for III-Vs (red) on Si (blue) and SiO\({}_{2}\) (grey). Color shading symbolizes the growth direction and thereby natural orientation of heterostructures or doping profiles. The in-plane TASE epitaxial growth extends along the surface of the wafer and may include in situ doping profiles or hetero-junctions [34; 35] oriented along the growth direction, perpendicular to the wafer surface (see Figure 1). This represents a fundamental difference to bonded devices which rely on the planar growth of different layers with composition and doping profiles in the vertical direction. Another distinguishing feature is that the III-V materials are grown such that they replace a silicon feature. This makes it self-aligned through a one-step lithographic process. This high degree of control allows us to place contacts accurately on the scaled devices and to create hybrid structures with silicon and III-V features. Here, we present our latest work on in-plane TASE growth on an SOI wafer, as this is most suitable for applications targeting the direct coupling to silicon waveguides and other passive silicon photonic features, and therefore highly promising for the realization of photonic integrated circuits (PICs). We will review and discuss the method as applied to these structures and additionally show previously unpublished work relating to InP-based hybrid III-V/Si photonic crystal emitters which highlight the benefits of locally placing gain material.
## 1 Introduction  
Epoxy composites field has been deeply investigated because of its common use in several manufactory processes [1]. They play a main role in both aviation and automotive sectors [2, 3]. The combination of epoxy matrix with nanostructured carbonaceous fillers (i.e., CNTs, carbon fibers, graphene) is one of the most investigated approaches used for high performance materials production [4, 5]. Despite their astonishing properties, the production of nanostructured carbon composites involves some unavoidable and rather critical issues. The most relevant is merely economic and it is due to the high price of the fillers for CNTs that can be as high as thousands of \(\boldsymbol{\upxi}\)/kg. Furthermore, nanostructured carbon is not easily dispersible as reported by many authors neither in epoxy resins [6] nor in thermoplastic matrices [7]. Among carbonaceous fillers, a non-traditional sustainable alternative is represented by the carbon residue of biomasses pyrolysis known as biochar [8].  
As recently reported by [PERSON] et al. [9; 10], biochar could be used as replacement for traditional high performance carbon fillers such as CNTs. Furthermore, biochars with different thermal histories lead to different properties of the epoxy composites [11]. Moreover, [PERSON] et al. [12] enlighten the relationship between different feedstocks and the mechanical properties of biochar. In a further study, [PERSON] et al. [13] tested biochar from different feedstocks in a polypropylene matrix showing an appreciable improvement of tensile and flexural properties. Despite these promising properties, biochar production is generally limited to small scale or not reproducible batch after batch. The poor reproducibility has many causes such as the quality of the feedstock and the process parameters. In this article, we reported a study about the incorporation of commercial standardized biochars derived from various feedstocks into an epoxy matrix. The high reproducibility of biochar batches grants the availability of larger amounts of standard materials. Furthermore, the variety of the feedstock used allows us to select the appropriate material for each task. In this study, we correlate the changes in brittleness and ductility of biochar-based composites with the morphological properties of the carbonaceous filler used. A hypothetical interaction mechanism is also proposed.
## 3 The Proposed Method
### Original Image Restoration
#### 3.3.1 Dual-Color Space Detection of Perturbed Pixels  
During generating the RAEs, only the original values of the perturbed pixels \((R_{0},G_{0},B_{0})\) are recorded. Our method can automatically detect the perturbed pixel in an RAE, so the perturbed pixel location \((x_{0},y_{0})\) is discarded and not recorded, which saves the embedding capacity for the auxiliary information and mends the images short of embedding capacity.  
The differences between the perturbed pixel and its adjacent pixels are remarkable, so we leverage the remarkable differences to detect perturbed pixel. In Figure 6, the RAE \(\mathbf{I^{\prime\prime}}\) is split into six channels, with three channels based on the RGB channels and three channels based on the HSV channels: \(\mathbf{I^{\prime\prime}}_{i}\) and \(\mathbf{I^{\prime\prime}}_{k}\), where \(i\in\{R,G,B\}\), \(k\in\{H,S,V\}\). In fact, using only the RGB channels is sufficient to successfully detect perturbed pixel in the majority of RAEs. However, by incorporating the HSV channels, the detection success rate can be increased to 99%. In the following sections, we will also validate the feasibility of this approach through experiments.  
Figure 5: Generation of reversible adversarial example (a 4 x 4 black as an instance).  
\[\mathbf{D}_{RGB}=\mathbf{D}_{R}+\mathbf{D}_{G}+\mathbf{D}_{B} \tag{6}\]  
\[\mathbf{D}_{HSV}=a\mathbf{D}_{H}+b\mathbf{D}_{S}+c\mathbf{D}_{V} \tag{7}\]  
\[\mathbf{D}_{DIFF}=\mathbf{D}_{RGB}+d\mathbf{D}_{HSV} \tag{8}\]  
A high-pass filter, a Laplacian operator, is applied on each channel to generate six response matrices: \(\mathbf{I}^{\prime\prime}_{(i-HP)}\) and \(\mathbf{I}^{\prime\prime}_{(k-HP)}\). In Figure 6, for visualization, the matrices have been normalized. The actual differences between the perturbed pixel and its adjacent pixels are larger than the visualization results.  
Next, the generated matrices are subtracted from their respective channels, and the absolute values of the differences are \(\mathbf{D}_{i}=|\mathbf{I}^{\prime\prime}_{i}-\mathbf{I}^{\prime\prime}_{(i-HP )}|\) and \(\mathbf{D}_{k}=|\mathbf{I}^{\prime\prime}_{k}-\mathbf{I}^{\prime\prime}_{(k-HP )}|\).  
Finally, two kinds of difference matrices, \(\mathbf{D}_{i}\) and \(\mathbf{D}_{k}\), are summed to obtain \(\mathbf{D}_{RGB}\) and \(\mathbf{D}_{HSV}\) according to Equations (6) and (7), where \(a=0\), \(b=0.95\) and \(c=0.05\) in our work. The difference matrix \(\mathbf{D}_{DIFF}\) is obtained according to Equation (8) where, in our work, \(d=1.5\). The location of the pixel with the highest value in \(D_{\text{DIFF}}\) is \((x^{\prime\prime},y^{\prime\prime})\), which is labeled by the red box, and considered as the location of the detected perturbed pixel in \(\mathbf{I}^{\prime\prime}\). \((x_{0},y_{0})\) is the location of the perturbed pixel in the original image. Generally, \((x^{\prime\prime},y^{\prime\prime})=(x_{0},y_{0})\). The workflow of the D-CSDPP is shown in Algorithm 2.  
Figure 6: Visualization of perturbed pixel detection.
# On Orthogonal Double Covers and Decompositions of Complete Bipartite Graphs by Caterpillar Graphs
## 3 Main Results
Then, there exists an ODC of \(K_{16,16}\) by \(K_{2,8}\); see Figure 16._  
Figure 16: Symmetric starter of an \(ODC\) of \(K_{16,16}\) by \(K_{2,8}\). In Theorem 8, we construct a symmetric starter of an \(ODC\) of \(K_{y^{2},y^{2}}\) by \(\frac{y}{2}K_{2,y}\) using a general symmetric vector. For more illustration, see Figure 17.  
**Theorem 8. **_Let \(y\equiv 2\) mod \(6\) and \(y\equiv 4\) mod \(6\), then the vector \(v(\frac{y}{2}K_{2,y})=(0,0,..,0,y,y,..,y,2y,\)\(2y,..,2y,3y,3y,..,3y,..,3y,..,..,y(y-1),y(y-1),..,y(y-1))\in\mathbb{Z}_{y^{2}}^{y^{2}}\), is a symmetric starter vector of an \(ODC\) of \(K_{y^{2},y^{2}}\) by \(\frac{y}{2}K_{2,y}\)._  
**Proof. ** From the vector \(v(\frac{y}{2}K_{2,y})\), we can define:  
\[v_{i}=\left\{ \begin{array}{ll}0&\text{if}\quad i<y,\text{or}\\ y&\text{if}\quad y\leq i<2y,\text{or}\\ 2y&\text{if}\quad 2y\leq i<3y,\text{or}\\ 3y&\text{if}\quad 3y\leq i<4y,\text{or}\\ \vdots&\vdots\\ y(y-1)&\text{if}\quad y(y-1)\leq i<y^{2}.\end{array} \right. \tag{22}\]  
Hence,  
\[v_{-i}=\left\{ \begin{array}{ll}y(y-1)&\text{if}\quad 1\leq i\leq y,\text{or} \\ y(y-2)&\text{if}\quad y<i\leq 2y,\text{or}\\ y(y-3)&\text{if}\quad 2y<i\leq 3y,\text{or}\\ y(y-4)&\text{if}\quad 3y\leq i<4y,\text{or}\\ \vdots&\vdots\\ y(y-y)&\text{if}\quad y(y-1)<i\leq y^{2}.\end{array} \right. \tag{23}\]  
Then,  
\[v_{i}-v_{-i}+i=\left\{ \begin{array}{ll}0&\text{if}\quad i=0,\text{or}\\ 0-y(y-1)+i&\text{if}\quad 1\leq i<y,\text{or}\\ y-y(y-2)+i&\text{if}\quad y<i<2y,\text{or}\\ 2y-y(y-3)+i&\text{if}\quad 2y<i<3y,\text{or}\\ \vdots&\vdots\\ y(y-1)-y(y-y)+i&\text{if}\quad y(y-1)<i<y^{2},\text{or}\\ y-y(y-1)+i&\text{if}\quad i=y,\text{or}\\ 2y-y(y-2)+i&\text{if}\quad i=2y,\text{or}\\ 3y-y(y-3)+i&\text{if}\quad i=3y,\text{or}\\ 4y-y(y-4)+i&\text{if}\quad i=4y,\text{or}\\ \vdots&\vdots\\ y(y-1)-y(y-(y-1))+i&\text{if}\quad i=y(y-1).\end{array} \right. \tag{24}\]Note that \(\left\{v_{i}-v_{-i}+i:\,i\in\mathbb{Z}_{y^{2}}\right\}=\mathbb{Z}_{y^{2}}\). \(\square\)  
Example 9 is a direct application of Theorem 8. **Example 9**. : _Let \(y=4\); so, the vector\((2K_{2,4})=(0,0,0,0,4,4,4,4,8,8,8,8,12,12,12,12)\). Then, there exists anODC of \(K_{16,16}\) by\(2K_{2,4}\); see Figure 18._  
Finally, we constructed the decompositions and \(OD\)Cs of complete bipartite graphs \(H\) by graphs \(G\). The results obtained in Section 3 are united in Table 2 as follows:  
It should be emphasized again that the main advantage of the symmetric starter vectors method is that it is much easier to implement than, for example, the direct method, function-half starter method, and matrices method. However, it also has the limitation that some graphs do not have a symmetric starter; so, the orthogonal double covers cannot be constructed by these graphs based on the symmetric starter method. In these cases, it is not applicable, and alternative methods must be used.
# An Efficient Carrier Synchronization Scheme for Demodulation Systems
## 3 Proposed Carrier Synchronization Algorithm
### Carrier Frequency Recovery Algorithm
Unit1 realizes the autocorrelation function at different intervals; Unit2 accumulates the autocorrelation values of different intervals; Unit3 calculates the cumulative sum of the autocorrelation values of the pilot block with different intervals; Unit4 realizes the cumulative autocorrelation values of multiple pilot blocks; and Unit5 estimates the CFO. Assuming that the number of cumulative autocorrelations of multiple pilot blocks is \(L_{0}\), then the accumulator loop is set from 0 to \(L_{0}-1\), and the \(pilot\_en\) signal is the enable signal of the accumulator. When the accumulator number is equal to \(L_{0}-1\), the cumulative autocorrelation result \(R\_Sum\) of multiple pilot blocks is an arctangent calculation to obtain the frequency estimate value \(\Delta\hat{f}_{L0}\), and the enable signal is generated to clear the accumulation value \(\sum\limits_{l=0}^{L_{0}-1}\sum\limits_{i=1}^{L-1}R_{l_{i}}(m)\) to zero. It can be seen from Unit4 that the increased resource consumption is limited. The RLR algorithm calculates the CFO and recovers the CFO through the feedback loop.
## 2 Step Modulation Principles
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Symbol** & **Description** & **Value** & **Units** \\ \hline \(g_{0}\) & Gain slope constant & \(3\times 10^{-12}\) & m\({}^{3}\) s\({}^{-1}\) \\ \(N_{t}\) & Carrier density at transparency & \(1.2\times 10^{24}\) & m\({}^{-3}\) \\ \(\tau_{N}\) & Total spontaneous emission carrier lifetime & \(3\times 10^{-9}\) & s \\ \(\tau_{P}\) & Average photon lifetime inside the cavity & \(10^{-12}\) & s \\ \(\Gamma\) & Mode confinement factor & 0.44 & - \\ \(\varepsilon\) & Gain compression factor & \(3.4\times 10^{-23}\) & m\({}^{-3}\) \\ \(\beta\) & Fraction of the spontaneous emission & \(4\times 10^{-4}\) & - \\ \(e\) & Electronic charge & \(1.6\times 10^{-19}\) & C \\ \(V\) & Active region volume & \(9\times 10^{-17}\) & m\({}^{3}\) \\ \(\eta_{0}\) & Differential quantum efficiency & 1 & - \\ \(h\) & Plank’s constant & \(6.6\times 10^{-34}\) & J-s \\ \(v\) & Frequency & \(193.9\times 10^{-12}\) & Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptions of parameters in Equations (1)-(3) and their values used in our numeral simulation. A dash (-) in the “Units” column means that the value is dimensionless. Figure 1: Evolutions in the carrier and photon densities during a gain switch cycle: (a) Modulated by rectangular waves; (b) modulated by step rectangular wave. According to the process of step-pulse modulation process, the primary function of the first-stage modulation current (FSMC) is to accumulate the carrier, while the output pulse characteristic is mainly affected by the second-stage modulation current (SSMC). So when \(T_{1}<t_{1}^{\prime}-t_{0}^{\prime}\), the carrier density in the laser does not reach the threshold after the end of the FSMC, until the SSMC has been injected for a period. Thus, the SSMC is not fully utilized for not the entire injected carrier is converted into the stimulated photon. When \(t_{1}^{\prime}-t_{0}^{\prime}<T_{1}<t_{2}^{\prime}-t_{0}^{\prime}\), the laser has entered the stimulated emission state at the end of the FSMC, and the SSMC will not entirely increase the carrier injection rate. As a result, combined with the studies of Reference [22], a simple design of the FSMC is \(T_{1}=t_{1}^{\prime}-t_{0}^{\prime}\), that is to say, the semiconductor laser works stably at the threshold level before the SSMC begins.
# Remote Sensing for International Peace and Security: Its Role and Implications
## 3 Results
### Remote Sensing in Tracking the Acts of Genocide
This enabled them to identify the potential destruction of villages and inform agencies like Human Rights Watch. The information can then also be used for corroborating eyewitness accounts for the destruction by refugees. [PERSON] et al. [83] combined qualitative data collected from personal narratives with the data obtained using the Geographical Information System (GIS) technologies to study mass atrocities in the northern part of Uganda. This study shows the role of GIS for cartographic functions andgeo-visualization. The previous case studies demonstrate the usefulness of remote sensing data to track cases of population displacement and genocide, supplementing independent eyewitnesses and other reports.
## 3 System Analysis for a Two-User System
### Computation of Secrecy Capacity  
Each node received a certain level of SINR or SNR depending on its channel condition and the interference. In this paper, SINR and SNR will be used interchangeably and is denoted as \(\zeta\). The respective received \(\zeta\) of the \(Bob_{2}\) and Eve are given as:  
\[\zeta_{2}=\min\left\{\frac{\zeta_{a1}}{\zeta_{11}+1},\zeta_{12}\right\} \tag{4}\]  
and  
\[\zeta_{e}=\zeta_{ee}, \tag{5}\]  
where \(\zeta_{a1}=\frac{\alpha_{1}P_{T}}{G}\alpha^{2}\), \(\zeta_{11}=\frac{P_{e}G_{11}}{\sigma^{2}}\), \(\zeta_{12}=\frac{\alpha_{2}P_{T}}{G}_{12}\sigma^{2}\) and \(\zeta_{1e}=\frac{\alpha_{2}P_{T}}{G}_{1e}\alpha_{2}G_{1e}+\sigma^{2}\) which follows the exponential distribution with parameter \(\lambda_{a1}=\frac{\alpha_{1}P_{T}}{G}\alpha^{2}\), \(\lambda_{11}=\frac{\alpha_{2}G_{11}}{\sigma^{2}}\), \(\lambda_{12}=\frac{\alpha_{2}P_{T}}{G}_{12}\sigma^{2}\), \(\lambda_{1e}=\frac{\alpha_{2}P_{T}}{G}_{12}\sigma^{2}\), \(\lambda_{1e}=\frac{\alpha_{2}P_{T}}{G}_{1e}A_{2}+\sigma^{2}\) and \(G_{ni}=K_{ni}d_{0}{}^{\gamma}d_{ni}{}^{-\gamma}g_{ni}\). For \(\lambda_{1e}\), it is assumed that \(\sigma^{2}=\sigma^{2}/G_{1e}\). The subscripts \(1\), \(2\), \(11\), \(12\), \(a1\) and \(1e\) show the parameters for \(Bob_{1}\)\(Bob_{2}\), between \(Bob_{1}\) and itself, \(Bob_{1}\) and \(Bob_{2}\), Alice and \(Bob_{1}\), \(Bob_{1}\) and Eve.  
The achievable data rate for the \(Bob_{2}\) and the Eve is given as:  
\[R_{2}=\log_{2}\Bigg{(}1+\min\left\{\frac{\zeta_{a1}}{\zeta_{11}+1},\zeta_{12} \right\}\Bigg{)}, \tag{6}\]  
and  
\[R_{1e}=\log_{2}\big{(}1+\zeta_{1e}\big{)}. \tag{7}\]  
The possible secrecy capacity of the system for R-IBFD system is given as  
\[C_{sec}=\max\{0,R_{2}-R_{1e}\}, \tag{8}\]  
\[C_{sec}=\max\Bigg{\{}0,\log_{2}\Bigg{(}\frac{1+\min\big{\{}\frac{\zeta_{a1}}{ \zeta_{11}+1},\zeta_{12}\big{\}}}{1+\zeta_{1e}}\Bigg{)}\Bigg{\}}. \tag{9}\]
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 3 Methods
### 3D Point-of-Regard Estimation
#### 3.3.3 3D Point-of-Regard Estimation and Calibration Experiment
Each person carries out a personal calibration process, and in one calibration process a person needs to observe 30 different target points. In each observation, the calibration target is randomly placed at any position 0.8 m to 1.5 m away from the person, and it is necessary to ensure that the person can observe the target in comfort. After collecting these experimental data, the calibration results are first calculated, and then these data are used to calculate each person's spatial coordinates of the PoR during calibration, and these calculated spatial coordinates of the PoR will be used to calculated the distances between themselves and the ground truth of the target point. Finally, we plot these experimental results in a graph according to the number of the calibration points. The experimental results are shown in Figure 9.  
The experimental results show that when the number of the calibration points exceeds 15, the errors drops to a low and stable level. However, too many calibration points will make the calibration process complicated. We have also learned from the calibration processes of some commercial eye tracking devices, which usually use nine calibration points. Therefore, in subsequent experiments and practical use, the proposed gaze tracking system usually set the number of the calibration points to nine.