esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Sensitivity Comparison  
The sensitivity comparison of vapour trace-detection of explosives based on CMO (chemo-mechanical sensing with optical detection) and CE (capacitive censing with electronic detection) is based on Equation (5) by normalizing both responses to approximately the same conditions; for some parameters this is possible, for the others it is not, because of a different mechanism of operation. To calculate the figure of merit (FOM), one needs to first calculate the sensitivity under approximately the same conditions regardless of the principle, volume, power consumption, _etc._, but at the same density of target molecules, the same pressure, the same temperature, and the same measurement bandwidth. The same vapour pressure is assured by using the same vapour generator as explained in Section 4.1. The bandwidth of each measurement is normalized to 1 Hz; the temperature is kept constant during the measurements (constant temperature is important for chemo-mechanical sensing and much less for  
Figure 19: NMR spectra of the material collected from 1000 L of gas mixture using a cold trap. The flow through a glass tube immersed in liquid nitrogen was set to 100 mL/min (as during normal operation of vapor generator). A peak of solvent deuterated chloroform (CDCl\({}_{3}\)), a water peak (H\({}_{2}\)O), a CH\({}_{2}\)Cl\({}_{2}\) peak, and TNT peaks (Me–Ar and Ar–H) can clearly be seen, all other impurities are present only in traces. CH\({}_{2}\)Cl\({}_{2}\) is a solvent, which was used for deposition of TNT onto glass wool.  
Figure 18: Photograph of a glass tube, used for cold trapping of TNT molecules from the gas phase. The right side (A) of the glass tube was close to room temperature, while the left side (B) was immersed in liquid nitrogen during purging with 1000 L of N\({}_{2}\) carrier gas with TNT molecules.  
capacitive sensing). The comparison of two sensitivities is in favour of capacitive sensing with electronic detection since \(S_{\mathrm{TNT,CE}}\) \(\equiv\) 3 \(\cdot\)10\({}^{-12}\)/\(\sqrt{\mathrm{Hz}}\) is smaller than \(S_{\mathrm{TNT,CMO}}\) \(\equiv\) 300 \(\cdot\)10\({}^{-12}\)/\(\sqrt{\mathrm{Hz}}\). This conclusion is valid at room temperature and at normal pressure. For extreme conditions where AFM is usually used (very good vacuum and extremely low temperature) the chemo-mechanical sensing with optical detection would most likely outperform the capacitive sensor with the electronic detection system by several orders of magnitude.  
Since the application requires operation at room temperature and normal pressure, and with as simple handling as possible, the CE system outperforms the CMO system, not only in the sensitivity, but also in functionality, temperature independence, volume, power consumption, construction simplicity and the stability of its operation, and potential price. The results of a comparison based on our comparative study are presented in Table 1.
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 2 Theoretical Background  
To overcome known limitations affecting the performance of Inertial Navigation Systems (INS) based on consumer-grade MEMS sensors, a redundant configuration of cost-effective accelerometers and gyros placed on the faces of regular polyhedron has been investigated. Thanks to the offered opportunity of averaging acceleration and angular rate values coming from different sensors, the considered geometric configuration allows a preliminary self-calibration and/or compensation of typical inertial sensors uncertainty sources (such as bias instability and random walk-driven drift, in the following referred also to as bias drift for the sake of brevity), thus reducing the overall uncertainty in attitude, position and speed measurements [15]. To better appreciate the proposed method, some fundamental theoretical notes about the main topics are given in the following.
## 2 Incremental State Model
If Equation (5) is applied for a previous sample, the following is obtained:  
\[x(k) =a_{x}+Ax(k-1)+Bu(k-1) \tag{6}\] \[y(k-1) =a_{y}+Cx(k-1)\]  
Subtracting (6) from (5) yields  
\[x(k+1) -x(k)=A(x(k)-x(k-1))+B(u(k)-u(k-1)) \tag{7}\] \[y(k) -y(k-1)=C(x(k)-x(k-1))\]  
The incremental state \(\Delta x\) and the manipulated input \(\Delta y\) are defined as follows:  
\[\Delta x(k) =x(k)-x(k-1) \tag{8}\] \[\Delta y(k) =u(k)-u(k-1)\]  
Substituting (8) into (7) yields  
\[\Delta x(k+1) =A\Delta x(k)+B\Delta u(k) \tag{9}\] \[y(k) =y(k-1)+C\Delta x(k)\]  
In this formulation, \(p\) new states are introduced, achieving the following:  
\[y(k) =a_{y}+Cx(k)\]0 \[y(k) =a_{y}+Cx(k)\]1  
Thus, the new incremental state model can be expressed as follows:  
\[\left[ \begin{array}{c}y(k+1)\\ \Delta x(k+1)\end{array} \right] =\left[ \begin{array}{cc}I&CA\\ 0&A\end{array} \right]\left[ \begin{array}{c}y(k)\\ \Delta x(k)\end{array} \right]+\left[ \begin{array}{c}CB\\ B\end{array} \right]\Delta u(k) \tag{11}\] \[y(k) =\left[ \begin{array}{cc}I&0\end{array} \right]+\left[ \begin{array} []{c}y(k)\\ \Delta x(k)\end{array} \right]\]Thus, a new extended incremental state vector, \(x_{a}(k)\in\Re^{(q+n)}\), is defined:  
\[x_{a}(k)=\left[ \begin{array}{c}y(k)\\ \Delta x(k)\end{array} \right] \tag{12}\]  
The incremental state model in matrix notation is:  
\[x_{a}(k+1) =A_{a}x_{a}(k)+B_{a}\Delta u(k) \tag{13}\] \[y(k) =C_{a}x_{a}(k)\]  
where \(A_{a}\in\Re^{((q+n)\times(q+n))}\), \(B_{a}\in\Re^{((q+n)\times p)}\) y \(C_{a}\in\Re^{(q\times(q+n))}\).
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 4 Methodology
### Attention-Based Transformer Encoder
#### 4.3.2 Attention-Based Multi-Hop Fusion Layer  
For the multi-hop neighborhood augmentation output of the Transformer encoder, \(\mathbf{H}=[\mathbf{H}_{0};\mathbf{H}_{1};\ldots;\mathbf{H}_{K}]\in\mathbb{R} ^{|E|\times(K+1)\times d_{m}}\), where \(\mathbf{H}_{0}\) represents the entities' own features and \([\mathbf{H}_{1};\ldots;\mathbf{H}_{K}]\) denotes the \(K\)-hop neighborhood features. To effectively integrate the feature representations from different hops, we employ an attention mechanism to assess the correlation between each neighborhood and the entity representations, thereby facilitating the fusion of \(K\)-dimensional features. Specifically, we first compute a normalized attention coefficient for each hop neighborhood feature,  
\[\alpha_{k}=\frac{exp((\mathbf{H}_{0}||\mathbf{H}_{k})\mathbf{W}_{a}^{\top})}{ \sum_{i=1}^{K}exp((\mathbf{H}_{0}||\mathbf{H}_{i})\mathbf{W}_{a}^{\top})}, \tag{12}\]where \(\mathbf{W}_{a}\) is a learnable weight, \(||\) denotes the concatenation operation, and \(i\in[1,K]\). In EA tasks, the entity itself contains more valuable information, so we incorporate the concept of residual network here. We introduce a learnable weight coefficient, \(\beta\), to balance the contribution of entity self-embedding and neighborhood embedding in the training process, described as follows:  
\[\mathbf{\tilde{H}}=\beta\cdot\mathbf{H}_{0}+(1-\beta)\cdot\sum_{k=1}^{K} \alpha_{k}\mathbf{H}_{k}, \tag{13}\]
# A Feasibility Study for a Nonlinear Guided Wave Mixing Technique
## 3 Experimental Setup
### Standard for Guided Wave Mixing Signal Detection  
It is necessary to give standards for guided wave mixing signal detection, and to determine whether a signal is a mixing signal. The frequency of the two primary waves and mixing wave is set by Equations (15) and (18). Table 3 shows the signal data used for analysis to determine standards for detecting a mixing signal. Primary waves are generated individually using No. 1 and 3 in Table 3. Primary waves are generated together using No. 2 in the table. These three sets of data are sufficient for analysis. No. 4 is provided for when the sum of Nos. 1 and 3 is removed from No. 2 for signal processing.  
A guided wave mixing single is detected when the following standards are met: Data types No. 2 and 4 have a mixing frequency component (5.77 MHz) in the frequency domain.  
The mixing frequency component of No. 2 has much greater amplitude than the other data types in the frequency domain.
## 4 System Implementation and Experimental Results  
This section describes in detail the implementation of the proposed adaptive human evaluation strategy framework and presents the numerical analysis performed using software programs. The software program implemented as shown in Figure 4 consists of six different functional panels. Table 2 summarizes the functions of each panel. Figure 4: Adaptive human evaluation strategy framework software program using the SGD algorithm for RL of a dynamic complex network. The first panel is called \"Application for basketball AI game,\" where the artificially coexisting and dynamically changing network topology problem discussed in this paper is applied to an AI basketball game. To determine the basketball game conditions, the number of times to repeat the lesson and the ranges of the attacking and defending teams are determined. The second panel, called \"Basketball AI game player network and system reward calculation,\" shows the basketball team network fluctuating dynamically during repeated learning, while calculating the system reward using the general RL algorithm. The third panel, \"Basketball AI game player position network topology distribution,\" depicts the position distribution of basketball players on the two-dimensional (2D) plane in the repetitive learning, as well as the three-dimensional (3D) mesh. Each change is shown in detail in Figure 5, and the cumulative changes as the learning is repeated are evident. The fourth panel is called \"Adaptive human evaluation estimation and calculation,\" where the evaluation is made through human intervention when the players of the attacking team choose the direction in which to pass the ball, and the reward is given as a real number between -5 and 5. The user directly enters the number in the form of a real number into the software system. The human evaluation rewards evaluated in this manner are handled more effectively in the fifth panel. In the fifth panel, \"Updating human evaluation reward using SGD algorithm (in repeated learning process),\" the SGD algorithm is used adaptively to update the reward value that the human user entered through evaluation. Finally, as shown in Figure 6, the system and adaptive human evaluation rewards calculated in the second and fifth panels are appropriately calculated to derive the final reward value and proceed with the learning. This final sixth panel is called \"Reward update monitoring\" and shows the rewards and average rewards in repeated learning of basketball games depicted using a complex and dynamically changing network with the proposed framework.
## 4 Evaluation
### Performance-Based Evaluation
#### 4.2.3 Success Rate Improvement  
One of the key indicators of the system's effectiveness is the success rate, which refers to the percentage of exercises that students successfully completed without requiring external assistance, such as manual intervention from an instructor or searching for solutions online. A well-adapted learning environment should provide sufficient guidance to struggling students while maintaining an appropriate level of challenge for those who are progressing well.  
In this study, success rate improvement is defined as the percentage of exercises completed successfully without requiring external assistance. However, it is necessary to clarify what qualifies as external assistance to ensure a precise interpretation of the results.  
Figure 4: Error rate reduction before vs. after RL optimization.  
For the purposes of this evaluation, an exercise is considered successfully completed if the student does not require instructor intervention or external resource consultation, such as searching for solutions online. The use of system-generated hints is not considered external assistance, provided that the student does not exhibit excessive reliance on them.  
To distinguish between independent learning and over-dependence on hints, a threshold was applied:  
* If a student used hints for less than 30% of the exercise duration, the attempt was still considered successful, as minimal guidance can reinforce learning without compromising problem-solving abilities.
* If hints were used excessively (more than 30% of the session) or if instructor intervention was required, the attempt was not counted as a successful independent completion.  
By comparing Phase 1 (static fuzzy weights) and Phase 2 (reinforcement learning-optimized fuzzy weights), we can assess whether dynamic adaptation improved student success rates. The results (Table 4, Figure 5) indicate a noticeable improvement, particularly for moderate and slow learners, confirming that adaptive UI elements are essential for optimizing the learning experience.
## 2 Electromagnetic Radiation Testing Method  
Electromagnetic compatibility and communication antennas are two important fields in modern electronic and communication systems. They are interconnected, ensuring the normal operation of devices and good communication quality. The electromagnetic compatibility of communication equipment significantly affects communication quality and the stability of communication networks. In the field of wireless communication, due to the limited frequency resources, different communication systems must coexist within the same frequency bands. By utilizing the gain from antennas during reception and transmission to arrange testing environments, we can attempt to replicate the working state of communication antennas in complex real-world environments, improving overall system performance and reliability. In this regard, electromagnetic compatibility testing has become a necessary step in the development of communication equipment.
## 4 Energy Replenishment Strategy
### Sojourn Time and Charging Power Allocation  
In an attempt to maximize the lifetime of the network, the MCGV works by charging cells to charge for nodes. According to the order which the MCGV follows, an optimization problem for the sojourn time and the allocation of the charging power is proposed below.  
\[\max\sum_{m\in CV}\sum_{i\in c_{m}}\log(1+\frac{\eta_{i}P_{i}(t_{o}+t_{c_{m}}) }{E_{i}}), \tag{5}\]subject to  
\[\sum_{i\in c_{m}}P_{i}\leq P, \tag{6}\]  
\[\eta_{i}P_{i}t_{c_{m}}+E_{i}\leq E_{o},i\in c_{m}, \tag{7}\]  
\[t_{c_{m}}/ce_{c_{m}}=t_{c_{k}}/ce_{c_{k}}\gamma m,k\in cv, \tag{8}\]  
\[\sum_{m\in AS}t_{c_{m}}\leq T-t^{move}-t_{o}\cdot|CV|, \tag{9}\]  
where \(P_{i}\) denotes the charging power of node \(v_{i}\). \(t_{c_{m}}\) and \(t_{c_{k}}\) are the sojourn times for charging cell \(c_{m}\) and cell \(c_{k}\), respectively. \(t^{move}\) represents the time the MCGV spends visiting every cell in \(CV\), and \(|CV|\) is the number of cells that the MCGV needs to visit. \(T\) is the time of a round.  
According to the charging power constraint in Equation (6), the total power charging one cell must be less than the MCGV's maximum power. The battery capacity constraint in Equation (7) states that the sum of supplementary energy and residual energy for any node in a round cannot exceed the battery capacity of sensors. Complying with these constraints contributes to a good charging performance and ensures that the MCGV does not charge nodes that are fully charged.  
In view of the energy balance constraint in Equation (8), the duration for charging each cell is positively correlated with its own discharge rate. The cell with a high discharge rate is allocated more time to be charged. The charging time constraint in Equation (9) shows that the time for charging is related to mobile time, data gathering time, and round time  
In light of the objective function in Equation (5), the charging performance of all designed schemes is calculated. According to the optimal solution, the MCGV allocates sojourn time for each charging cell in \(CV\) and the charging power for each node in the same cell based on multi-point charging technology. As a result, the charging for all nodes becomes more efficient.
## 4 Discussion  
Based on the ultrasonic measurements of Ti6 Al4V alloy and the determined basic acoustic properties, tests on the adhesive-sample joints were performed. Decibel drop was determined as a coefficient that can be used to evaluate and monitor the condition of the structural adhesive-alloy Ti6 Al4V joint made both by modern 3D printing technology and in a standard way. This made it possible to develop guidelines applicable to the condition monitoring procedure for this type of joint, which is shown in Figure 9.  
The procedure for monitoring the condition of an adhesive joint requires the selection of an ultrasonic transducer head with the appropriate frequency as well as transducer diameter depending on the type and parameters of the tested material. In addition, it is important to determine the basic acoustic parameters on a reference sample of the material made by additive manufacturing technology. Monitoring the condition of the joint involves checking the decibel drop for selected areas of the joint and comparing the obtained values with those determined on the reference sample. This type of approach gives quantitative results in terms of the adhesion of the adhesive to the material surface. This is a different proposal for locating defects or monitoring the condition of components compared to those in publications [34; 35] and focuses primarily on qualitative evaluation and visualization of defects in the material structure. Lamb waves [36], as well as guided waves [37; 38], are also used in adhesive bond testing. However, it is important to expand testing capabilities using longitudinal waves propagating through the adhesive joint area.  
The proposed method for continuous monitoring of the condition of an adhesive joint or a component made with additive manufacturing technology is primarily applicable at the operation stage of parts and machines. In the literature [39; 40; 41], there are known methods for monitoring the condition of parts made by 3D metal printing technology that allow non-contact evaluation of individual layers and existing defects in the manufactured part. Nevertheless, this is possible only in the laboratory and under constant conditions [42]. Determination of decibel drop measure is possible under various conditions and can be carried out periodically or continuously with a remote data transmission method for assessing the condition of the adhesive bond and other parts manufactured with additive manufacturing technology. Similar conclusions from the use of decibel drop measure were obtained by the authors of the paper [43].
# Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification
## 4 Experiments
### Ablation Studies
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**CLINC**} & \multicolumn{4}{c}{**StackOverflow**} & \multicolumn{4}{c}{**BankKING**} \\ \cline{3-14}  & **Methods** & **Acc-** & **F1-** & **F1-** & **F1-** & **Acc-** & **F1-** & **F1-** & **F1-** & **Acc-** & **F1-** & **F1-** & **Acc-** & **F1-** & **F1-** & **F1-** \\  & **ALL** & **ALL** & **OOD** & **IND** & **ALL** & **ALL** & **OOD** & **IND** & **ALL** & **ALL** & **OOD** & **IND** \\ \hline \multirow{11}{*}{25\%} & w/o Shared & 87.24 & 78.31 & 91.53 & 77.96 & 89.19 & 81.78 & 92.72 & 79.6 & 76.79 & 72.75 & 82.51 & 72.23 \\  & w/o Margin & 83.85 & 29.71 & 90.82 & 28.1 & 93.31 & 87.97 & 95.58 & 86.45 & 82.86 & 48.89 & 89.66 & 46.75 \\  & w/o ALL & 91.74 & 83.19 & 94.76 & 82.89 & 74.57 & 70.46 & 80.6 & 68.43 & 79.47 & 71.86 & 85.04 & 71.16 \\ \cline{1-1}  & Ours & **92.90** & **85.38** & **95.51** & **85.12** & **93.26** & **87.54** & **95.58** & **85.93** & **88.36** & **80.27** & **92.16** & **79.64** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performances of the proposed method and ablation models. The best performances are indicated in bold, and the second-highest performances are underlined.
## 4 Implementation Scenario and Conceptual Framework
### Computing Resource Utilization of NTCA Using Different ML Models  
#### 5.6.1 Memory Usage  
Figure 17 shows that the NB traffic classifier agent design uses the least average memory of 141.1 megabytes (MB) to train the model and classify new data instances. Despite the fact that the DT-based NTCA design offers the best classification accuracy of 99.504%, it uses the most memory resources amounting to 150 MB. This gives the system designer a chance to make a trade-off between the classification accuracy and memory usage while trying to choose a traffic classifier agent design.  
Figure 16: Comparison of NTCA classification latency.  
Figure 17: Comparison of NTCA memory usage.
## 1 Introduction  
Carotenoids encompass a diverse spectrum of organic pigments, ranging from yellows to brilliant oranges and deep reds [1; 2]. These compounds offer more than just coloration; they act as precursors to several hormones and play multiple biological roles, including light-harvesting, photoprotection, and providing distinct hues to various organisms [3; 4; 5; 6; 7; 8; 9; 10]. From a biotechnological perspective, specific carotenoids such as \(\beta\)-carotene, lycopene, and lutein have predominantly served as food colorants, antioxidants, and essential components in animal feed [11; 12; 13]. In addition to their conventional applications, these pigments have found practical uses in the fields of mutraceuticalids, cosmetics, and pharmaceuticals [14; 8; 15]. The marine environment, abundant with diverse microorganisms, holds significant potential for the exploration of novel carotenoid-producing organisms.  
In the Antarctic Ocean, carotenoids are ecologically pivotal, functioning as essential bioindicators of environmental transformations. The extreme conditions prevailing in the Antarctic, characterized by severe cold, intense ultraviolet radiation, and extensive sea ice, pose distinctive challenges for carotenoid research. A comprehensive analysis of the spatial distribution, abundance, and heterogeneity of carotenoids would elucidate the complex adaptive mechanisms of Antarctic marine microbes. Such understanding would not only contribute to ecological knowledge but also catalyze the advancement of novel biotechnological applications of these compounds, underscoring their potential in bioprospecting endeavors [16; 17; 18; 19].  
While numerous carotenoid structures, especially those derived from microbial sources, have been identified, significant gaps still exist in our understanding of the genetic and enzymatic processes governing their biosynthesis [20]. The rapid expansion of metagenomics, supported by an increasing repository of microbial genomic sequences, provides an opportunity to unravel the enigmatic biosynthetic pathways of natural carotenoids [21]. This genomic wealth has the potential to facilitate the metabolic engineering of distinctive and economically beneficial carotenoid derivatives. Furthermore, exploring the involvement of marine microorganisms in carotenoid biosynthesis presents a promising avenue for investigation. However, the exploitation of this vast structural diversity is currently limited by the deficiency of specific and efficacious enzymatic catalysts. These organisms are likely to possess many yet-to-be-discovered genes essential for carotenoid production [22], which may lead to the creation of new carotenoid variants and expand our understanding of their wide-ranging biological functions.  
In consideration of these factors, our research begins with a metagenomic analysis at two distinct depths near King Sejong Station in the Antarctic Ocean. This study aims to decode the genetic intricacies underlying carotenoid biosynthesis in Antarctic Ocean microorganisms, thereby enriching the scientific conversation within this domain.