esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Real-Time Experiments  
### Experiment on Control System for Charge-Amplifier Drift Compensation  
The proposed tuning method was also tested on control system for charge-amplifier drift compensation that was part of a custom-made measuring system for the automated low-frequency and high-temperature polarization measurements of dielectric materials [66]. The measurement system consisted of a temperature-regulated furnace (heating of the measured samples), lock-in amplifier Stanford Research Systems SR830, and commercial charge-amplifier Kistler 5018A. This dielectric-material characterisation concept is presented in [67; 68].  
The control process (charge amplifier) had an integrating character. Process input and output were voltage signals limited from -10 to 10 V. The 2-DOF PI controller was realized with software package LabVIEW, and data acquisition was realized with an NI USB-6001 card. More information about this particular control system for drift compensation can be found in [66].  
First, a step-change at the process input (change of input voltage signal from 0 to -2 V) was applied. The process response (voltage) is shown in Figure 17. From the measured open-loop responses, the following characteristic areas were calculated using Expressions (17)-(19):  
\[A_{0}=0.1119,\ A_{1}=0.009,\ A_{2}=5.538\times 10^{-4}. \tag{44}\]  
Using Expressions (12) and (14), the PI controller parameters for different values of reference weighting factor \(b\) (0, 0.5, and 0.7) were calculated. The calculated parameters are presented in Table 10.  
Figure 17: Open-loop response of control system for charge-amplifier drift compensation.  
Figure 18 shows the process closed-loop response on a reference change, and artificially added step-disturbance (\(d=1\)) at the process input (\(d\)). According to the responses, it is evident that the proposed method results in quite efficient control of the drift compensation of the charge amplifier. Reference following performance increases by increasing factor \(b\), and the best disturbance-rejection performance is achieved with \(b=0\). A good compromise between disturbance-rejection and reference following performance is \(b=0.5\).
###### Abstract  
The control of soft surrounding rock stability has always been a hot academic issue. Soft rock has poor stability and low strength, and the deformation of a soft rock tunnel becomes more serious after it is affected by water for a long time. In this paper, the Jintong Coal Mine is taken as the research object, and nondestructive immersion experiments are used to study the change in mechanical properties of rock after being affected by water. The FLAC numerical model is used to analyze the stress evolution characteristics of the surrounding rock after being affected by water, and the results of the study show that the water absorption of siltstone is always higher than that of coarse-grained sandstone, and the uniaxial compressive strength of siltstone and coarse-grained sandstone decreases by 54.59% and 67.99%, respectively, under a state of saturated water compared with that under a state of dryness. Influenced by a T-shaped surface, the maximum principal stress concentration area occurs in the rock layer below the T-shaped surface and outside the joint. Concentrations of maximum shear stress occur within the \"T\" channel. Vertical stress concentration zones occur at the higher ground level and the bottom of the slope. The maximum shear stress of the roof fluctuates before the face reaches the surface of the \"1\" section, and continues to increase with and continues to increase with the distance of the face. After entering below the surface of the \"1\" section, the maximum shear stress of the roof increases rapidly, and the influence range is about 24 m. The maximum shear stress distribution plays a dominant role in the stability of the surrounding rocks of the two roadways. We analyze the principle of high-strength economic support, propose a \"four-in-one\" surrounding rock control technology based on \"controlled hydrophobicity, structural adjustment, district management, and gradient control\", and propose a surrounding rock control scheme of district management. The measured data on site show that the roadway surrounding the rock is reasonably controlled. This provides a reference for the stable control of the surrounding rock of the roadway under similar conditions.  
nodestructive immersion test; mechanical properties; multistage bearing structure; anchor cable truss; zoning control +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Capacity of SIF in Monitoring GPP under Drought Conditions  
Because of the lack of direct large-scale GPP observation data, it is challenging to accurately monitor the spatiotemporal variations of GPP caused by drought. FLUXCOM-GPP, which is based on process-based modeling, can successfully represent the spatiotemporal variations of GPP [57]. GPP is dependent on the input of historical data. However, these data are difficult to access resulting in GPP data not being available in a timely manner. So, there is an urgent need for a simple, direct, and accurate method to estimate GPP. SIF is considered as a good indicator for estimating GPP due to its close correlation with vegetation photosynthesis. Therefore, it offers a new opportunity for assessing GPP losses caused by drought. Our research demonstrates that SIF can accurately track the temporal and spatial variations in GPP due to drought (Figure 5). Furthermore, the strong linear relationship of SIF with GPP enables SIF to be an applicable new indicator for evaluating drought-induced GPP losses (Figure 7a,d).  
Previous studies have shown strong correlations of SIF with GPP in different ecosystems. [PERSON] et al. (2015) comprehensively evaluated the relationship of SIF with GPP in different ecologically distinct systems (cropland and temperate mixed forest) using SIF and EC flux data [14]. The results revealed a strong correlation between SIF and GPP across different ecosystems. The high correlation of SIF with GPP makes SIF a promising indicator for assessing GPP losses due to drought (Figure 9). Traditional drought indices are calculated using meteorological data or coupled remote sensing data and primarily evaluate the effect of drought on vegetation by analyzing the statistical relationship between drought indices and GPP. Compared to traditional drought indices, there are notable benefits for SIF to assess drought losses because it can be directly used for evaluating GPP losses without the need for additional auxiliary data.  
Our results also revealed that the relationship of SIF with GPP (R\({}^{2}\) = 0.95) is more significant than the relationship of NDVI with GPP (R\({}^{2}\) = 0.78) (Figure 9). The traditional, green-based vegetation index NDVI has limitations in drought monitoring because it only interprets potential photosynthesis and lacks a direct connection to the actual photosynthetic process. Additionally, NDVI is significantly influenced by factors such as soil background and shadows. In contrast, the positive relationship of SIF with GPP indicates that SIF is more sensitive to photosynthesis and does not exhibit saturation effects. This advantage of using SIF to estimate vegetation GPP demonstrates that SIF serves as a reliable indicator of vegetation productivity, consistent with previous research [46].
## Appendix A Square Discretization  
For square discretisation we denote a square at \((i,j)\) with ordered quaternion \(((i,j),(i+1,j),(i+1,j+1),(i,j+1))\) by \(\square_{j}^{i}\), and the set of all such squares by \(X_{\square}\); see Figure 11. The discrete jet bundle is defined as  
\[J_{\square}^{1}Y:=\{(u_{i}^{j},u_{i+1}^{j},u_{i+1}^{j+1},u_{i}^{j+1})\in\mathbb{ R}^{4}:((i,j),(i+1,j),(i+1,j+1),(i,j+1))\in X_{\square}\}, \tag{11}\]  
which is equal to \(X_{\square}\times\mathbb{R}^{4}\). For more details, see [11, 16] and references therein.  
By averaging the fields over all vertices of the square, the field \(u\) can be obtained as (see Figure 11)  
\[u\to\frac{u_{i}^{j}+u_{i+1}^{j}+u_{i}^{j+1}+u_{i+1}^{j+1}}{4}. \tag{12}\]  
As above, the expressions for the derivatives can be taken from [16] for the discrete Lagrangian, which now depends on the edges of the square; i.e.,  
\[L_{d}\Big{(}u_{i^{\prime}}^{j},u_{i+1}^{j},u_{i+1}^{j+1},u_{i}^{j+1}\Big{)}. \tag{13}\]  
As a result, the discrete Euler-Lagrange field equations are  
\[D_{1}L_{d}\Big{(}u_{i^{\prime}}^{j},u_{i}^{j+1},u_{i+1}^{j+1},u_{ i+1}^{j}\Big{)} + D_{2}L_{d}\Big{(}u_{i}^{j-1},u_{i^{\prime}}^{j},u_{i+1}^{j},u_{ i+1}^{j-1}\Big{)}\] \[+D_{3}L_{d}\Big{(}u_{i-1}^{j-1},u_{i-1}^{j},u_{i}^{j},u_{i}^{j-1} \Big{)} + D_{4}L_{d}\Big{(}u_{i-1}^{j},u_{i-1}^{j+1},u_{i}^{j+1},u_{i}^{j} \Big{)}=0; \tag{14}\]  
see Figure 11 (right).
# Topology Optimisation Using MPBILs and Multi-Grid Ground Element
###### Abstract  
This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the multi and adaptive learning rate, respectively. Four classic multi-objective structural topology optimization problems are used for testing the performance. Furthermore, these topology optimization problems are improved by the method of multiple resolutions of ground elements, which is called a multi-grid approach (MG). Multi-objective design problems with MG design variables are then posed and tackled by the traditional MPBIL and its improved variants. The results show that using MPBIL with opposite-based concept and MG approach can outperform other MPBIL versions.  
topology optimization; multi-objective optimization; opposite-based evolutionary algorithm; population-based incremental learning; adaptive learning rate 2018 8 271 doi:10.3390/app8020271
# Optical Helicity and Chirality: Conservation and Sources
## 2 Review and Motivation  
In many areas of physics, it is useful to characterise the \"twist\" of a vector field by a pseudoscalar quantity of the form \(\mathbf{F}\cdot(\mathbf{\
abla}\times\mathbf{F})\). In fluid mechanics, for example, the quantity \(\mathbf{\
abla}\times\mathbf{v}\) is called the \"vorticity\" [20; 21], where \(\mathbf{v}\) is the fluid flow velocity, and the further quantity \(\mathbf{v}\cdot(\mathbf{\
abla}\times\mathbf{v})\) describes the knottedness of the vortex lines [22].  
In plasma physics, the conserved quantity \(\mathbf{A}\cdot(\mathbf{\
abla}\times\mathbf{A})\) has been used since the late 1950s to characterise the topology of magnetic field lines [23; 24], where \(\mathbf{A}\) is the magnetic vector potential. More recently, this quantity has been applied to the study of optical fields. It can be generalised in order to include both electric and magnetic contributions by including an additional vector potential \(\mathbf{C}\), defined such that \(\mathbf{\
abla}\times\mathbf{C}=-\mathbf{D}^{\mathrm{T}}\), where \(\mathbf{D}^{\mathrm{T}}\) is the transverse part of the displacement field [25]. For the free field, \(\mathbf{D}^{\mathrm{T}}=\epsilon_{0}\mathbf{E}\), and the helicity density is often expressed in terms of \(\mathbf{E}\) rather than \(\mathbf{D}\). In the presence of a medium, however, it is preferable to use the form given here. This leads to the symmetrical definition:  
\[h=\frac{1}{2}\Bigg{[}\sqrt{\frac{\epsilon_{0}}{\mu_{0}}}\mathbf{A}\cdot(\mathbf{ \
abla}\times\mathbf{A})+\sqrt{\frac{\mu_{0}}{\epsilon_{0}}}\mathbf{C}\cdot( \mathbf{\
abla}\times\mathbf{C})\Bigg{]}. \tag{1}\]  
Figure 1: **(a)** The molecule bromochlorofluoromethane is chiral, as the molecule and its mirror image cannot be superimposed, even after rotation. **(b)** Dichlorofluoromethane, on the other hand, is achiral, as the molecule can be superimposed upon its mirror image after rotation.  
This is the quantity we refer to as the \"helicity\" (or, more accurately, \"helicity density\") throughout this article. The helicity is a Lorentz pseudoscalar and is a conserved quantity of the free electromagnetic field. It is closely connected to the spin angular momentum of light, and has attracted attention as a way of describing the interactions of light with chiral matter [16; 17; 18; 26].
# A Deep Reinforcement Learning Approach for Active SLAM
## 3 Experimental Setup
### Slam  
From previous formulation it is clear that the covariance matrix recovery is a key requirement. In this sense, a slightly modified _gmapping_[2] algorithm runs on background of the training, re-initializing its map every episode and communicating the covariance estimate to the learning module via the ROS architecture (see Algorithm 2). Despite other SLAM algorithms (e.g., [4, 38]) would able to compute more accurate state and covariance estimates, _gmapping_ has been chosen because of its simplicity and low computational load, which were requirements that have been considered essential for the stage the study is at.  
Every time the algorithm processes a laser scan, it recovers the distribution of the particles' state as a Gaussian with mean \(\mu\in\mathbb{R}^{3}\) and covariance \(\Sigma\in\mathbb{R}^{3\times 3}\):  
\[\mu=\sum_{i=1}^{n_{p}}\bar{w}_{i}x_{i} \tag{13}\]  
\[\Sigma=\sum_{i=1}^{n_{p}}\bar{w}_{i}(x_{i}-\mu)(x_{i}-\mu)^{T} \tag{14}\]  
where \(n_{p}\) is the total number of particles, index \(i\) represents a single particle, \(\bar{w}_{i}=w_{i}/\sum_{j=1}^{n_{p}}w_{j}\) its normalized weight and \(x_{i}=(x_{i},y_{i},\theta_{i})^{T}\) its state vector. After its retrieval, the covariance matrix is referenced to the previous pose following the absolute formulation available in [9], and published in ROS.  
The main parameters of the algorithm have been tuned to achieve a balance between performance and low computational load. Since each particle has to carry its own map, only five particles have been used. The minimum effective number of particles, a key parameter to detect loop closures, has been set to one-fourth of the total number of particles.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Cross-Dataset Evaluation  
To evaluate the generalization capability of our approach, we conducted cross-dataset experiments by training on the FSVH dataset and testing on the MVTec-AD [47] and Food-5K [51] datasets. Table 8 presents the results of these experiments.  
Our method demonstrates strong cross-dataset generalization, achieving 54.2% mAP on MVTec-AD and 43.8% mAP on Food-5K. This superior performance can be attributed to the knowledge-enhanced feature synthesis approach, which leverages domain knowledge to generate more transferable features for unseen hazard classes.
# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 3 Robust ENF Estimation Techniques
### Non-Parametric Spectral Estimation Techniques  
Non-parametric approaches do not assume that a model generated the data. The typical approach in this method is to use Fourier analysis, which can result in some higher computational cost. We utilized the Short-Time Fourier-Transform (STFT)-based approach in this work. The ENF signal fluctuations are represented as \(f_{ENF}=f_{o}+f_{\Delta}\), where \(f_{o}\) is the nominal frequency and \(f_{\delta}\) is the instantaneous signal fluctuation. With the Fourier transform of the input signal \(x(n)\), the Power Spectral Density (PSD) is calculated from the spectrogram to estimate the spectral band from the harmonic frequency band (\(B\)) of interest \(f\in k[f_{o}\pm\frac{B}{2}]\).  
From the spectral band, the instantaneous frequencies in each frame window are estimated by the maximum value in the corresponding power density vector for that time instant. To improve the frequency estimation accuracy, quadratic interpolation can be used where the index of the frequency bin numbers is used to obtain the spectral peak. The peak location is given as  
\[\Delta=\frac{1}{2}\cdot\frac{\alpha-\gamma}{\alpha-2\cdot\beta+\gamma}\]  
where \(\alpha\) is the previous bin of the max spectral bin, \(\beta\) is the max spectral peak, and \(\gamma\) is the next bin. If \(k^{*}\) is the bin number of the highest spectral sample, then the resulting frequency estimate bin is adjusted by \(\Delta\), and the final interpolated frequency estimate is  
\[f_{ENF}=f_{o}+(k^{*}+\Delta)\frac{f_{s}}{N}\]  
Here, \(f_{s}\) is the sampling frequency of the input signal and \(N\) is the number of FFT bins used. Although the input signal data length is not limited in a continuous input stream, such as a surveillance system audio/video feed, the number \(j\) of fast Fourier transforms \(FFT_{j}\) where \(j=1,\ldots,J\) points can be increased for higher accuracy at the cost of increased computational resources. With known nominal frequency bounds, the ENF estimate from this technique can be accurate, but at the same time, if the energy peak is not in the bounds, then it is susceptible to outliers.
# Understanding the Light Detection in an Integrated Novel Electrophotonic Wavesensor Photodetector
## 1 Introduction
In [19] a variety of integrated photodetector architectures within the frame of electrophotonics are proposed. The photosensors are directly integrated with the waveguide and cannot be used separately as discrete photonic or electronic sensors. The light is directly transmitted to the depletion layer, resulting in lower electron-hole recombination and a higher contribution to photocurrent by the photons in the range of the LEC emission. In addition, the novel approach of seamless relation between guide and detector enables new advantages, such as the possibility of increased photoresponse due to current gain, in which lower light intensities result in higher photocurrents. In this work, the concept and analysis of a novel waveguide-detector binomial, called wavesensor (WS) for application in electrophotonic integrated circuits is proposed. The photodetector is designed to be integrated with an SRO LEC and with a silicon nitride waveguide. It does not function as a discrete photonic or electronic sensor, but only makes sense as part of an electrophotonic system, in which electrons and photons are intimately interrelated. The light transmitted by the waveguide is injected directly to the active depletion layer. The core of the waveguide is seamlessly integrated as the gate dielectric to a MOS-like structure. The WS biased in the Punch-Through regime allows for light detection in the visible range in the bulk of the device. Amplification of the detected photocurrent is observed in the drain current. The analyses of the physics and simulation results show electric gain for the detected photocurrent in the substrate, meaning the device is not controlled by surface phenomena as regular MOS transistors are. The results show the viability of a unique monolithically integrated capable of detecting powers in the range of nanowatts, meaning a step forward in the integration of SRO light emitters for development of electrophotonics.
## 3 Results and Discussion
### Following the A. niger Metabolites Biomarkers Pattern over 7 Days
#### 3.2.2 By Direct Analysis of Contaminated Table Grapes--In Situ SPME
niger_ contamination are 3-octanol, 2-phenylethanol, 3-methyl-butanol, and 1-octen-3 ol (only for Dominga). Based on previously reported studies, all of these compounds can be associated to volatile organic compounds produced by microorganism species [15; 27; 72], even _A. niger_[13; 14]. Previous studies have demonstrated the pathway for the production of 1-octanol is from glucose and fatty acids in _E. coli_ and this compound is mainly accumulated in the culture media fraction [15]. Furthermore, 2-phenylethanol and 3-methyl-butanol compounds are very common as microorganism biomarkers. Production of these metabolites are associated with the isoleucine/leucine and phenylalanine pathway metabolism, respectively [27; 72; 73]. Figure 5: PLS-DA biplot of samples (LV1\(\times\)LV2) using the set of 42 metabolites listed in Table 2, showing clear distinction between both contaminated grape varieties over time. Results obtained over 7 days (Table S2) for White (Dominga—Do) and Red (Red Globe—RG) grapes contaminated with _A. niger_. Independent assays were performed for red and white varieties. Non-invasive methods such as HS-SPME are very useful in the detection and monitoring of food contamination, including the level of contamination. Recent works have demonstrated the utilization of metabolomic strategies in the detection of several microorganisms in grapes and grape juices [4; 13; 14]. In general, the approaches utilized involve a non-target metabolic exploration step in order to verify a pattern of compounds responsible for the contamination and from this point on, only these target compounds are monitored to verify different levels of contamination [13; 74]. This was also the approach initially used during this work, and therefore justifies the choice of the set of metabolites investigated. The data obtained so far demonstrate that it is possible to detect contamination by _A. niger_ directly in grapes using a non-invasive and rapid method. From this initial information, new statistical tools and approaches will be employed in order to explore these data in more depth and obtain important information regarding the profile of volatile metabolites and their relationship with the level of fungal contamination [69; 75].
## 4 Experiments and Discussion
### Ablation Study  
To evaluate the contribution of each component in the model, this study designs seven variant experiments and tests them on the Amazon Review music dataset. The results are shown in Table 6 and clearly demonstrate that removing any single component leads to a performance drop, highlighting the importance of each part in the model.  
Here, Variant 1, which removes both POS tags and dependency edges from the graph structures, exhibits significant performance degradation. This indicates that POS tags and dependency edges play a crucial role in providing syntactic information, which enhancing the construction of the graph. Moreover, Variant 2, which retains POS information, demonstrates a measurable performance improvement over Variant 1, particularly when applied to language pairs with significant linguistic divergence. Variant 4 demonstrates the importance of the similarity edge in the model, since it facilitates more effective semantic representation learning. Variant 3, which removes both the translation and multi-view contrastive learning modules, achieves the lowest performance, while adding the translation module yields better results, as shown in Variant 5. However, Variant 5, which lacks multi-view contrastive learning, remains the second-worst-performing; this indicates that the contrastive learning module is the most crucial among all added components. Furthermore, the results of Variant 6 and 7, which remove translation-level and label-label contrastive learning, respectively, show a decline in performance. This further emphasizes the effectiveness of the contrastive learning modules from different views.  
In conclusion, the results of the ablation study strongly demonstrate the effectiveness of the XCLHG framework. The components work synergistically to enhance the model's performance in multilingual environments.