esa-sceva/satcom-chunk-collection huggingface dataset:
# Formation Mechanisms and Kinetic Modeling of Key Aroma Compounds During Qidan Tea Roasting
## 3 Results and Discussion
### Determination and Analysis of Amino Acid Content
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**No**} & **Amino** & **Molecular** & \multirow{2}{*}{**Standard Curve**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multicolumn{6}{c}{**Concentration (mg/mL)**} \\ \cline{4-9}  & & & & **0 h** & **2 h** & **4 h** & **6 h** & **8 h** & **10 h** & **12 h** \\ \hline
1 & Asp & 133.1 & y = 106.13x + 2.263 & 0.9926 & 28.35 \(\pm\) & 29.11 \(\pm\) & 23.19 \(\pm\) & 20.42 \(\pm\) & 16.27 \(\pm\) & 18.13 \(\pm\) & 23.43 \(\pm\) \\
2 & Glu & 147.1 & y =186.24x \(-\) 1.652 & 0.9947 & 59.99 \(\pm\) & 48.54 \(\pm\) & 42.29 \(\pm\) & 41.79 \(\pm\) & 34.93 \(\pm\) & 23.75 \(\pm\) & 16.83 \(\pm\) \\  & & 102.4 \(\pm\) & 2.93 \(\pm\) & 3.52 \(\pm\) & 3.57 \(\pm\) & 1.47 \(\pm\) & 9.11 \(\pm\) & 0.42 \(\pm\) & 26.07 \(\pm\) & 27.37 \(\pm\) & 24.40 \(\pm\) \\
3 & Ser & 105.1 & y = 129.82x \(-\) 4.001 & 0.9736 & 26.31 \(\pm\) & 4.53 \(\pm\) & 4.17 \(\pm\) & 6.07 \(\pm\) & 28.60 \(\pm\) & 3.13 \(\pm\) & 21.48 \(\pm\) \\
4 & His & 155.2 & y = 244.16x + 2.171 & 0.9947 & 21.11 \(\pm\) & 23.06 \(\pm\) & 28.31 \(\pm\) & 23.94 \(\pm\) & 14.41 \(\pm\) & 22.66 \(\pm\) & 12.17 \(\pm\) \\  & & 3.18 \(\pm\) & 1.12 \(\pm\) & 13.21 \(\pm\) & 1.59 \(\pm\) & 11.12 \(\pm\) & 1.59 \(\pm\) & 11.12 \(\pm\) & 12.71 \(\pm\) & 23.18 \(\pm\) \\
5 & Gly & 75.1 & y = 173.12x + 0.806 & 0.9867 & 2.64 \(\pm\) & 1.94 \(\pm\) & 1.01 \(\pm\) & 1.01 \(\pm\) & 1.01 \(\pm\) & 1.01 \(\pm\) & 1.01 \(\pm\) \\  & & & 0.25 \(\pm\) & 0.51 \(\pm\) & 0.08 \(\pm\) & - & - & - & - \\
6 & Thr & 119.1 & y = 118.05x \(-\) 0.813 & 0.9946 & 2.13 \(\pm\) & 12.68 \(\pm\) & 12.02 \(\pm\) & 6.10 \(\pm\) & 5.29 \(\pm\) & 6.84 \(\pm\) & 3.24 \(\pm\) \\  & & & 10.18 \(\pm\) & 12.68 \(\pm\) & 1.20 \(\pm\) & 6.10 \(\pm\) & 5.29 \(\pm\) & 6.84 \(\pm\) & 3.24 \(\pm\) \\  & & & 2.13 \(\pm\) & 1.09 \(\pm\) & 4.36 \(\pm\) & 0.04 \(\pm\) & 1.52 \(\pm\) & 0.57 \(\pm\) & 0.04 \(\pm\) \\
7 & Arg & 174.2 & y = 272.84x \(-\) 3.179 & 0.9892 & 8.82 \(\pm\) & 13.90 \(\pm\) & 14.41 \(\pm\) & 9.30 \(\pm\) & 10.10 \(\pm\) & 5.48 \(\pm\) & - \\  & & & 2.98 \(\pm\) & 4.38 \(\pm\) & 6.38 \(\pm\) & 1.73 \(\pm\) & 4.79 \(\pm\) & 1.98 \(\pm\) & - \\
8 & Ala & 89.1 & y = 150.76x + 3.308 & 0.9878 & 11.99 \(\pm\) & 12.67 \(\pm\) & 10.90 \(\pm\) & 7.36 \(\pm\) & 3.21 \(\pm\) & 4.77 \(\pm\) & 3.12 \(\pm\) \\  & & 3.82 \(\pm\) & 4.43 \(\pm\) & 2.16 \(\pm\) & 1.71 \(\pm\) & 1.36 \(\pm\) & 1.07 \(\pm\) & 0.78 \(\pm\) & 5.0 \(\pm\) \\
9 & Tyr & 181.2 & y = 269.11x \(-\) 1.592 & 0.9831 & - & - & - & - & - & - \\
10 & Cys & 121.2 & y = 193.99x + 0.254 & 0.9885 & 1.77 \(\pm\) & 4.11 \(\pm\) & 4.36 \(\pm\) & 9.18 \(\pm\) & 3.40 \(\pm\) & 2.47 \(\pm\) & 1.20 \(\pm\) \\  & & & 111.67 & y = 111.16x \(-\) 1.667 & 0.9972 & 7.02 \(\pm\) & 12.78 \(\pm\) & 10.96 \(\pm\) & 8.13 \(\pm\) & 5.44 \(\pm\) & 2.54 \(\pm\) & 1.43 \(\pm\) \\  & & & 3.53 \(\pm\) & 1.03 \(\pm\) & 2.35 \(\pm\) & 2.74 \(\pm\) & 4.43 \(\pm\) & 0.
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 2 Polypyrrole-Based Electrodes
### 2D Nanostructures  
Two-dimensional nanomaterials are widely used as sensor electrodes as they exhibit uniformed structures in a wide surface area, making them ideal for the production of thin films. Most PPy-based films are easily synthesized using electrochemical deposition techniques and can be used to generate biosensor electrodes via the introduction of biological receptors [87; 88]. Electrochemical deposition is a simple method used to obtain conducting polymer thin films containing electrolytes incorporated as dopants. Another class of 2D nanostructure films, namely, composite-based conducting polymers, can be employed for various functions. [PERSON] et al. synthesized double-stranded calf thymus DNA-functionalized PPy-polyvinyl sulphonate thin-films using electrochemical deposition for detecting 2-aminoanthracene [87]. [PERSON] et al. fabricated polymer composite films consisting of  
Figure 2: (**a**) Synthesis of methyl orange–iron chloride template CPPy nanotubes for 17\(\beta\)-estradiol-based biosensors [85]. Copyright 2016, Royal Society of Chemistry. (**b**) Platinum-coated CPPy nanofibers for the production of nonenzymatic biosensors capable of detecting oxalic acid [86]. Copyright 2018, Royal Society of Chemistry.  
PPy-dodecylbenzene sulphonate for the synthesis of cholesterol-sensing electrodes [88]. [PERSON] et al. manufactured a composite ink made from PPy and polyvinyl alcohol mixtures that were then used to generate nanofilms for screen printing [89]. This conductive polymeric ink-based electrode was an effective biosensor for detecting glucose complexed glucose oxidase.
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 3 Biomedical Applications of Magnetic-Plasmonic Nanocomposites  
The dual modalities of a magnetic plasmonic nano-system open up a range of potential biomedical applications. The multimodal nature of these nanoparticles paves the way for their use as theranostic agents--i.e., materials that can be used to both diagnose, or quantify a disease, and also simultaneously provide treatment. In an ideal world, a single nanoparticle with these theranostic properties should be easy to functionise and the size and properties should be tunable.  
When designing nanoparticles for use in biomedical applications, a number of factors must be taken into consideration. The nanoparticles should be taken up only by targeted cells, and should not be present in any significant concentration in non-targeted tissues. Efficient renal clearance from the body is necessary to minimise toxicity, and for any drug to be considered feasible for widespread use by the FDA (US Food and Drug Administration), it must be cleared from the body within a reasonable timeframe [58; 59]. Hydrodynamic diameter is also of utmost importance, as the nanoparticles must be small enough to not be cleared through the mononuclear phagocyte system, but large enough so that they do not become coated with serum proteins, thus increasing the hydrodynamic diameter and hindering renal elimination [60].
## 5 Numerical Simulation and Discussion  
The purpose of this section is to conduct the micromechanical simulations of the digital sample reconstructed from X-ray CT slice to predict the viscoelastic responses with/without damage behavior.  
Figure 8: Sketch map of elements and nodes label for the CT image pixel matrix.  
Figure 9: Pixel-based numerical model of HMA mixture (AC-20): (**a**) the whole model; and (**b**) the local graph.  
The aggregates are considered as a linear elastic material and the modulus of elasticity and Poisson's ratio for the aggregates are assumed to be 25 GPa and 0.25, respectively. The mask phase is considered as a linear viscoelastic material. Parameters for the viscoelastic constitutive and damage of the mastic phase are determined in Sections 3.1 and 3.2. The numerical viscoelastic constitutive model is implemented within the FE code using FORTRAN. The ABAQUS user material subroutine (UMAT) is applied for this purpose.  
In order to investigate the viscoelastic behavior of asphalt mixture with and without damage, four different compressive strain levels (10 \(\mu\varepsilon\), 20 \(\mu\varepsilon\), 50 \(\mu\varepsilon\), and 70 \(\mu\varepsilon\)) were applied to the 2D digital sample generated in Section 5.2 at 20 \({}^{\circ}\)C.
# Strategies to Combat Multidrug-Resistant and Persistent Infectious Diseases
## 3 Repurposing Treatments against MDR and Persistent Bacteria
### Anti-Inflammatories as Antibacterial Agents  
[PERSON] and colleagues recently used in silico tools to study the antibacterial effects of acetaminophen and ibuprofen, anti-inflammatory, anti-pyretic, and analgesic drugs, against red-complex pathogens (_Porphyomonas gingivalis, Treponema demicola_, and _Tannerella forystthia_), as these bacteria are associated with inflammatory conditions associated with periodontal disease [57]. Acetaminophen and ibuprofen were found to interact by in silico approximations with bacterial cytoplasmic proteins involved in cellular processes, metabolism, and virulence. The authors claimedthat bioinformatic prediction tools revealed multiple epitopes in the virulent proteins that should be targeted in in vitro assays.  
Glucocorticoids are a well-known class of anti-inflammatory drugs, but their use in patients with sepsis is highly controversial because of the immunosuppressive effects of these compounds [58]. Betamethasone is an anti-inflammatory steroid belonging to this class. Indeed, once in the nucleus, betamethasone stimulates the transcription and translation of lipocortin and vasocortin, two proteins that inhibit the release of inflammatory mediators such as prostaglandandins, leukotrienes, and histamine [59]. Although it may seem counterproductive to use these drugs in sepsis patients, as they mitigate the immune response, [PERSON] and colleagues showed that the topical steroid betamethasone was effective, by itself, for treating external otitis caused by _P. aeruginosa_ and _C. albicans_[60]. These findings could be explained considering that inflammation is a major mechanism in the development of external otitis, similarly to what occurs in periodontal disease, as described above.  
Finally, we include here a cathelicidin-like antimicrobial peptide, Cbf-K\({}_{16}\), characterized by [PERSON] and colleagues and proposed as an anti-inflammatory and antibacterial compound that can effectively kill clarithromycin- and amoxicillin-resistant _Helicobacter pylori_ SS1, both in vitro and in a gastritis mouse model [61]. Cbf-K\({}_{16}\) showed time-dependent killing kinetics, protection of _H. pylori_-infected gastric epithelial cells, and inhibition of interleukin 8 (IL-8) secretion. Indeed, Cbf-K\({}_{16}\) binds to genomic DNA, downregulating the expression of adhesion genes (_alpA_ and _alpB_) and the virulence gene (_cagA_), thus indicating its potential use of the in the development of an anti-infective therapy.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 5 Key Challenges and Future Research Directions
### Scalability Issues  
The complete integration of advanced sensing technologies with every component of the power grid is difficult to realize. This is because synchronization becomes an issue with heterogeneity among power grid components. The power grid is a huge entity with a multitude of parameters of diverse nature that needs to be monitored and controlled. There have also been deployment issues since replacing the traditional metering and measuring systems over the entire length of the power system is capital-, labor-, and knowledge-intensive in nature. Furthermore, the full integration of multiple sensing technologies is difficult, owing to the diverse nature of power sources and the surrounding environment. Thus, the development of a single sensing technology or a standard protocol is difficult if not impossible to implement, especially with the dynamic nature of the technology where advanced components, control methods, and interfaces are still under rapid development [147]. One such attractive technology is called FLEXITRANSTORE which is an Integrated Platform for Increased FLEXIbility in smart TRANSmission grids with STORage Entities and large penetration of Renewable Energy Sources [159]. Its goal is to improve the deployment of future smart grids while addressing their scalability and replicability issues.  
Thus, some of the key challenges faced by the current transmission and distribution system topologies have been described above. Researchers working on the future expansion of the power grid and its transformation into smart grids can take inspiration from the above key challenges. Furthermore, researchers interested in condition health monitoring and predictive maintenance of power systems can take their future research insights from this review. Finally, problem statements for popular research topics such as power and storage optimization, big data, and cybersecurity can be found in the key challenges described above.
## 5 Experiments and Results  
The experimental setup for the Card3 DFace system was composed of a rig for the portrait acquisition and a polycarbonate personalization machine (printer). The acquisition rig was composed of several 3D cameras (plenoptic, stereo and time-of-flight), a photographic studio with soft-boxes and a homogeneous background and the software application. Regarding the personalization of the cards, this was achieved with a specialized printer with laser technology, which is commonly used for the personalization of Portuguese ID cards at the Portuguese Mint and Official Printing Office (INCM).  
After the acquisition and modeling processes, the generated head view images could be used for printing on the lenticular cards. The calibration of the personalization machine was conducted by expert operators. This process involved the calibration of the exact position of the laser beam reaching the lenticular lenses and the exact calibration of the angle of the card holder with respect to the laser beam. This calibration guaranteed that the generated view images were personalized on the card surface below the lenticular lenses. As this calibration process depends on the specific printer and its inner design, its description does not fall within the scope of this article.  
We present some examples in Figures 19-23 of the head views generated for printing, and an imprinted card prototype example is presented in Figure 25. Figure 24 shows the same card viewed from different perspectives. Notice that the printed 3D face will commonly have small dimensions and usually occupies a reduced area of the card and travel document.  
Before evaluating the whole process, we describe some ablation studies made for the phases of the process.
## 3 Learned Separation
### Phase Prediction  
Of the tone parameters for (10), we still need the phase angles \(\varphi_{j,h}\). Canonically, we could represent them as a vector that is output by the network for each possible choice of \(\
u_{j},\eta_{j}\), but this would lead to high dimensionality. Instead, we let the network emit a single artificial spectrum \(\
u_{j}\in\mathbb{C}^{n_{\rm spec}}\) for each possible instrument choice \(\eta_{j}\). This is used as the right-hand side of a least-squares problem for determining the coefficients \(c_{j,h}\in\mathbb{C}\):  
\[\min_{(c_{j,h})}\frac{1}{2}\sum_{l}\left|\sum_{h}c_{j,h}\cdot\exp\left(-\frac {\left(\beta l-f_{j,1}^{\circ}h\sqrt{1+b_{j}h^{2}}\right)^{2}}{2\sigma_{j}^{2 }}\right)-\
u_{j}[l]\right|^{2},\qquad l=0,\ldots,n_{\rm spec}-1, \tag{17}\]  
from which we extract the phase angles as \(\varphi_{j,h}=\arg c_{j,h}\). We apply some \(\ell_{2}\) regularization to improve the condition number of the system. For \(c_{j,h}=0\), the phase would be ill-defined, but since the magnitudes of these coefficients are stabilized via the objectives introduced in Section 3.4, we do not realistically expect this case to happen.  
With this approach, the frequency dimension of the network output is used differently than for the other tone parameters. Instead of providing an output for each possible tone choice \(\
u_{j}\), the network computes a single spectrum, which then determines the phases of all harmonics. We expect the spatial structure of the U-Net architecture with respect to the frequency dimension to be beneficial for this task. While \(c_{j,h}\) is not explicitly given as a conditional value depending on \(\varpi_{\rm s}\), the computation of \(c_{j,h}\) in (17) does depend on all the stochastic parameters (even \(b_{j}\)), and since typically \(n_{\rm spec}>N_{\rm har}\), the network has some freedom to output \(v_{j}\) such that \(c_{j,h}\) take different values depending on the other parameters.  
Since \(v_{j}\) is the right-hand side of a linear least-squares system, optimizing \(y\) with respect to \(v_{j}\) is convex. Therefore, training is done deterministically via backpropagation through the pseudo-inverse. Moreover, we include the additional gradient that occurs with respect to the left-hand-side parameters that appear inside the exponential. Computation of the gradient of the solution of a least-squares system with respect to the left-hand side is usually included in automatic differentiation frameworks, but to obtain it explicitly, one can repeatedly apply the Woodbury formula.  
In the case where the positions of the peaks from the model perfectly match those from the spectrum \(Y\) without any overlap or additive noise, the choice \(v_{j}=Y\) gives the ideal phase values \(\varphi_{j,h}\) for all \(j=1,\ldots,m\) and \(h=1,\ldots,N_{\rm har}\). While this exact case is not realistic (not least since Gaussians have unbounded support), a network with skip connections can quickly learn to predict good approximate phase values.
# Screening Mechanism and Properties of a Cantilevered Vibrating Sieve for Particles Processing
## 5 Analysis of Simulation Results
### Influence of Vibration Parameters on Screening Efficiency  
In studying the influence of vibration parameters on screening efficiency, the geometric parameters were fixed to be: The sieve inclination angle \(\beta=0^{\circ}\) and the length of cantilevered sieve rod L = 380 mm. On the condition that the vibration amplitude D = 2 mm and the vibration circular frequency \(\omega=16\) Hz, this investigation also analyzed the screening efficiency when the vibration direction angle was in the range of 30\({}^{\circ}\)-65\({}^{\circ}\) (Figure 9). Obviously, when the vibration direction angle is within this range, the screening efficiency has no significant differences and lies between 56% and 68%.  
Figure 8: Screening efficiencies of a cantilevered vibrating sieve (CVS) for different sieve inclination angle.  
Figure 7: Screening efficiency of a cantilevered vibrating sieve (CVS) for different length of cantilevered sieve rod.  
The screening efficiency reaches its maximum of 68.02% at 45\({}^{\circ}\), and then begins to drop abruptly when the angle increases.  
The screening efficiency is also affected by the vibration amplitude, as shown in Figure 10. Clearly, within a small range, the increase of amplitude improves the screening efficiency with \(\beta\) = 0\({}^{\circ}\), L = 380 mm, \(\omega\) = 16 Hz, and \(\gamma\) = 40\({}^{\circ}\). When the amplitude is 2 mm, screening efficiency reaches its maximum value of 68.76%. However, a further increase in the amplitude causes screening efficiency to drop abruptly. The increase of amplitude within a small range mainly influences the bouncing of particles and increases screening efficiency to some extent. However, when the amplitude exceeds 4 mm, particles are seriously thrown out of the working area, and screening efficiency declines.  
Figure 11 shows the influence of vibration frequency on screening efficiency. For the common vibration frequencies of 14-18 Hz, screening efficiency maintains a high level of over 64%. However, when vibration frequency increases to exceed 20 Hz, the screening efficiency begins to decline. A large vibration frequency of the CVS may lead to a fast movement of the particles, which has a negative influence of the particles feeding-conveying-layering through the screen surface. It should be noted that the diameter of cantilevered sieve rod d = 6 mm, and according to Table 1 and Equation (8), one can show that the natural frequency of cantilevered rod is 29.31 Hz. As the flexible deformation of  
Figure 10: Screening efficiencies of a cantilevered vibrating sieve (CVS) for different vibration amplitude.  
Figure 9: Screening efficiencies of a cantilevered vibrating sieve (CVS) for different vibration direction angle.  
the cantilevered rod cannot be achieved during the screening simulation, we cannot demonstrate the beneficial effect of the screening process at the natural frequency.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 4 Experimental Simulation
### Testing Performance
#### 4.3.1 Model Generalization Test
This might be attributed to the significant differences between \"Area\" and \"World\" scenarios and the scenario dependence of our DRL model.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 3 Background
### IoT Devices
#### 3.2.3 PIR Sensor  
An electronic sensor that measures infrared (IR) light radiating from objects in its field of view. The characteristics of this sensor include the angle of detection (\(\upalpha\)) and the maximum detectable distance (\(d\)) with the detection range calculated as a cone with \(h\) as the diameter of the circle as the base. It is small, cheap, power-efficient, easy to use, and durable. Therefore, it is typically used for security and automatic lighting related purposes.
# A Survey on Hardware Accelerators for Large Language Models
## 5 ASIC Accelerators
### Elsa  
In 2021, [PERSON] et al. presented a hardware-software co-design approach for the acceleration of Transformer networks, called Elsa [27].  
Based on the fact that irrelevant relations can be effectively filtered out by computing approximate similarity, ELSA substantially reduces computational waste in a self-attention operation. Unlike conventional hardware such as CPUs or GPUs, which cannot benefit from approximation, ELSA proposes specialized hardware that directly translates this reduction to further improve performance and energy efficiency.  
The approximate self-attention scheme consists of three sub-operations. The first one estimates the angle between two vectors (e.g., a key and a query) with minimal computation by utilizing the concise representations of the key and the query. Then, an estimated angle is utilized to compute the approximate similarity between a query and a key, based on the fact that the dot product is directly proportional to the cosine of the angle between two vectors. Finally, the approximate similarity is compared with a certain threshold to identify whether a specific key is relevant to the query or not.  
The authors evaluated several representative self-attention-oriented NN models to demonstrate the effectiveness of ELSA. For performance evaluation, they implemented a custom simulator for ELSA, targeting a 40 nm ASIC clocked at 1 GHz. ELSA-moderate achieved up to a 157\(\times\) speedup compared to GPUs, along with improvements in energy efficiency by two orders of magnitude over the GPU for the self-attention computation.