esa-sceva/satcom-chunk-collection huggingface dataset:
# A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
## 3 Application to Authenticate Turmeric Powder  
Turmeric (_Curcuma long_ L.), which is a yellow colored herbaceous root from ginger family, is commonly used for food seasoning, for medicinal purpose and as dietary supplements [32]. Anti-inflammatory, anticarcinogenic, antioxidant, wound-healing effects are some of the medicinal value that is attributed to turmeric [47; 48; 49; 50]. The medicinal value of turmeric is associated with the yellow color pigment \"curcumin\" (diferuloyol methane) content in it. Curcumin content varies in the range of 0.3% to 8.6% [47; 51; 52; 53; 54], due to nutrients and acidity content in soil [55; 56], fertilizer, soil type and cultivar [57; 58; 59]. Due to the isolation of curcumin from turmeric for medicinal and cosmetic purposes, it is adulterated with color dyes as a substitute for curcumin [60]. Metanil yellow (C\({}_{18}\)H\({}_{14}\)N\({}_{3}\)NaO\({}_{3}\)S), a yellow color toxic azo dye, is added to turmeric for brighter color and appealing appearance [32]. Toxicologically, metanil yellow is classified as a CII category substance by the Joint Food and Agricultural Organization of the United Nations (FAO) and World Health Organization (WHO) expert committee on Food Additives [61]. Study on rats shows that the consumption of metanil yellow causes neurotoxicity [62], hepatocellular carcinoma [63], tumor development [64], deleterious effect on gastric mucin [65], and lymphocytic leukemia [66]. This study demonstrates the application of the 1064 nm dispersive Raman spectral imaging system for the detection of metanil yellow contamination in turmeric powder and subsurface detection and identification of gelatin-encapsulated pure and mixed samples of metanil yellow and turmeric powder. For each sample, a dark current spectrum was acquired with the laser off and a cap covering the probe and  
Figure 3: Image of a resolution test chart acquired by 1064 nm Raman spectral imaging system.  
subtracted from the Raman spectrum at each pixel during the spectral acquisition. Five-point moving average filter was used to smooth the spectra.
# A Twitter-Based Comparative Analysis of Emotions and
## 3 Methodology
### Translation into English  
Because the collected tweets were in Arabic and Spanish, they needed to be translated into English as an intermediary language so that we could compare Arabic and Spanish tweets in emotion and sentiment dimensions. To this end, we developed a Python script that interacted with Google Translate. The software received Arabic and Spanish tweets as inputs and delivered them translated into English. This allowed us to compare the tweets using English-based ML algorithms for sentiment analysis and emotion recognition that are already available.  
It is known that when using translated data, translated emotions and sentiment data are likely to contain samples that are not indicative of their assigned sentiment or emotion in their source language [16]. Although the level of preservation may be deemed sufficient for the present cross-national emotional comparison of messages on Twitter, more research was required.
## 2 Methodology for Determination of Beam Pointing Dynamics  
Consider a tropo link between stations A and B, as shown in Figure 1. A is considered a transmitter, and B is considered a receiver for the purpose of explanation. Both the transmitter and receiver are capable of steering their antenna beams to N different elevation angles. The lowest transmit and receive antenna beams just clear their respective horizons, with elevation angles of \(\alpha_{1}\) and \(\beta_{1}\) radians, with respect to straight-line AB. As the transmit and receive beams are steered upward simultaneously, through N different elevation angles, they form common volumes of 1, 2, 3, and so on, till common volume N. Successive common volumes may be contiguous or partially overlapping, depending on the difference between the successive elevation angles. A case of contiguous and vertically stacked common volumes is shown in Figure 1. The heights of the uppermost and lowermost points of a common volume from the Earth's surface determine its vertical height range. For example, points 1\({}^{\prime}\) and 1\({}^{\prime\prime}\) in Figure 1 represent the lowermost and uppermost points of the first common volume, and the difference between their heights determines the vertical range of the first common volume. The power received for a given set of corresponding transmit and receive beam elevation angles depends on the propagation conditions in the height range occupied by the corresponding common volume. The received power is calculated using a channel model at different times of the day in different seasons for each of the N sets. A comparison of N-received power values obtained for a given time helps determine the elevation angle set for which the received power is maximum at that time. The height range of the corresponding common volume is most favorable for signal propagation at that particular time. Antenna beams, at any point in time, should be steered such that they have elevation angles offering the maximum received power and their common volume occupies the most favorable height range. Received power for elevation angle sets higher than a certain value and their respective common volume height ranges will be comparatively low at all times. The useful steering range should be restricted to elevation angles below these low-power delivery angles.
# Settlement Prediction and Differential Settlement Criterion for Heightening and Thickening Leveee
## 2 Methodology
### Generalized Model  
Figure 2 shows the generalized model of the HTL. In fact, there are three ways to increase the height and thickness. However, heightening and thickening along the downstream slope is  
Figure 1: Pavement cracking.  
more common. Hence, the heightening and thickening along the downstream slope is taken as the generalized model.  
For the HTL, pavement cracks mainly occur on the downstream slope, as shown in Figure 1a. In fact, there are different kinds of pavement failure. Hence, there are different mechanisms accounting for pavement failure, such as leakage, flood scour, and differential settlement (see Figure 1a,b). In essence, all the pavement failure mentioned above can result from large settlements. In this paper, pavement cracking is the main research question. The main characteristic of this kind of crack is located at the junction of the backfill and the old levee. The cracking is due to the differential settlement.  
The HTL is mainly composed of the old levee, backfill, and pavement. The differential settlement of the HTL may cause early cracking of the pavement, which shortens the life of the pavement and reduces the stability of the HTL. The main reasons for differential settlement are as follow: 1. The compressibility between the old levee and backfill soil is different; 2. The foundation is uneven.  
In general, the compressibility of the backfill is higher than that of the old levee because the compactness of the old levee is high under long-term loading. In turn, the backfill is not compacted and has rheological characteristics. Moreover, the consolidation of the backfill is very small, according to the author's settlement monitoring. The settlement of the backfill mainly comes from rheology. However, the consolidation of the new foundation still exists and is not universal. Hence, this study focuses on the rheology of the backfill.  
In this paper, a generalized settlement prediction method is described so that the pavement laying time can be determined. On this basis, the relationship between the horizontal stress of the pavement and differential settlement will be revealed based on finite element analysis.
## 3 Results
### Hydrological Evaluation of Discharge Simulation Using Two SPPs
#### 3.3.2 Event of 21 March 2016  
This event represents the typical characteristics of a freshet caused by the melting of snowfall upstream of the Zat watershed. With the progressive increase of temperatures, the snow cover at the summit of the Atlas Mountains starts melting and feeding the streams of the mountainous basins including the study basin. This usually causes significant flooding during the occurrence of moderate rainfall episodes.  
The hydrograph of Figure 4 is well calibrated, the simulated flow curves were differently reproduced for both products. Concerning 3B42V7 SPPs, the rise and the recession curves were well reproduced, but the peak flow was underestimated due to the fact that this satellite product is not able to reproduce the low precipitation. However, IMERG V05 hydrograph is well reproduced at the rise, the recession, and the peak flow.  
Figure 3: Validation of the episode of 20 November 2014, of 3B42V7 and IMERG V05 rainfall products and observed runoff data as input using the means calibration parameters of the gauge precipitation calibration.  
The evaluation criteria are good with an RMSE of 0.3 and 0.3 and a Nash of 66.5% and 83.7% for 3B42V7 and IMRGV5 products.  
On the other hand, the hydrographs of validation are also differently reproduced for the two SPPs (Figure 5).  
The 3B42V7 underestimates the precipitation; as noticed this episode is generated by the effect of snowmelt, with the occurrence of light precipitation, and for this reason, the following product estimates poorly the slight precipitation as previously indicated, and consequently underestimates the value of precipitation during the event.  
Figure 4: Calibration of the episode 21 March 2016, using 3B42V7 and IMRG V05 rainfall products and observed runoff data as input.  
Figure 5: Validation of the episode of 21 March 2016, of 3B42V7 and IMRG V05 rainfall products and observed runoff data as input using the means calibration parameters of the gauge precipitation calibration.  
However, IMERGY5 has shown good performance in detecting small precipitation events; on the validation hydrograph the simulated curve is well reproduced at all levels. This is due to the fact that this product is capable of estimating small precipitation events with short time steps. This resolution has been well confirmed once more at this event.  
The evaluation criteria are good with an RMSE of 0.7 and 0.6 for the product 3B42 V7 and IMERG V05, and a Nash of 56.20% and 74.20. 9%, respectively.
## 3 Results
### Numerical Results: Point Scatterer
#### 3.1.2 Transmit Aperture  
So far, we used a full-aperture transmission. In this section, we evaluate the effects of the transmit aperture size (\(\mathrm{Nt}\)) on the PSFs obtained with the selected reception parameters (\(\mathrm{Nr=5}\) and \(\mathrm{Ns=3}\)). Figure 7a-c shows the PSFs obtained with different \(\mathrm{Nt}\) while a scatterer is positioned at the depths of 10, 15, and 20 mm, respectively. A small \(\mathrm{Nt}\) leads to strong artifacts caused by the grating lobes. This is due to the omnidirectional pressure field in transmission which causes excitation in the direction of the grating lobes with the same energy as that of main lobe. As the \(\mathrm{Nt}\) gets larger, the transmission pressure field gets more directional toward the front side of the array and becomes like what is shown in Figure 2a. In higher depths, the effects of grating lobes are lower since a lower pressure is transmitted in the direction of the grating lobes (see Figure 2a).  
The effects of grating lobes are lower when \(\mathrm{Nt}\) is larger than 20 (see Figure 7a-c). From \(\mathrm{Nt}\) of 1 to 50 the FWHM decreases, and reaches an asymptotic minimum for \(\mathrm{Nt}\) larger than 50. However, around a \(\mathrm{Nt}\) of 50, the effects of sidelobes are still more powerful than larger \(\mathrm{Nt}\) (compare the regions shown by the red and green dashed rectangles in Figure 7a-c). Since the larger the \(\mathrm{Nt}\), the lower the effects of sidelobes, we proceed with a full-aperture transmission (\(\mathrm{Nt=80}\)).
### Motivation  
The benefits of advanced deep-learning-based models and the associated limitations on the need of large-sized datasets and classification costs are the prime motivation for the proposed work. The main focus is on developing a low-cost recognition model with the advantage of excellent feature-extraction capabilities of advanced pre-trained DCNN models for the recognition of handwritten Hindi characters. The main motivations for the proposed work are as follows:  
1. To introduce the novel approach of handwritten Hindi character recognition with the benefits of features received from pre-trained DCNN models, accompanied by the reduced computational loads of the classifiers.
2. The majority of previously reported works have been solely based on either handcrafted features or CNN-based features. No work has been reported yet in which both types of features are used in a single feature-vector for the recognition of handwritten Hindi characters. CNN-based features and handcrafted features have their own advantages -- the former are auto-generative and the latter are rich in customization.
3. The model performance for each character-class of Hindi script has also not been covered in detail, such as character-wise correct and incorrect predictions; the amount of false-positive and false-negative predictions out of the total incorrect predictions and development of a confusion-matrix for all the 36 classes of Hindi consonants, etc.
4. The examination of the effectiveness of individual feature-types and their all-possible combinations is also a novel approach in relation to handwritten Hindi characters.
## 2 QTL Mapping Problem Description
### Statistical PLS Methods  
Like every linear statistical method, PLS has some advantages and disadvantages; its main advantage is that it can be accurate when the sample size \(n\) and number of predictor variables is small, and there are few irrelevant markers. Another reason that makes PLS regression a good option for small QTL mapping problems is its robustness against environmental variation in the data and missing data; also it can handle multi-collinearity among the genes [16].  
However, this regression method is unsuitable for larger problems when the number of predictor variables grows, particularly in the epistatic model, where the number grows with the square of the number of markers. The problems are two fold: first the solution will over-fit the data due to the excess of parameters and secondly, the method becomes unable to accurately discriminate those genes that control the trait from irrelevant genes. While hybrid methods have been introduced to ameliorate these problems [17; 18; 19], the application of the PLS method in QTL mapping is limited [20]. This paper presents a new hybrid method that overcomes both these limitations of PLS while retaining the advantages. The method finds accurate and compact QTL epistatic PLS models with large \(k\) and \(n\).
## 2 Methods
### Model Based Methods
#### 2.1.2 Sen2 res  
Sen2 res [9] models the mix of elements within a pixel (e.g., 20% road and 80% vegetation) as a physical property of that pixel, exploiting the geometric consistency of subpixel elements across the MS bands in a similar manner to hyperspectral unmixing [34]. The reflectance of each element is modelled as dependent on the spectral band at which it is observed, relying on local consistency between neighbouring pixels. High resolution bands are used to separate band-independent information from band-dependent reflectance, and the geometric information is used to unmix low-resolution pixels while preserving their overall reflectance [9].  
Shared values, \(\mathbf{S}\), span over multiple pixels and represent the band dependent spectral reflectance of elements that are common to the spanned pixels. They are initialized as the average of the high-resolution pixels that partially cover them. Weights, \(\mathbf{W}\), are internal to each pixel and represent the proportion of elements within the pixel and are initialized as \(\frac{1}{r^{2}}\), where \(r\) is the scale difference between the coarse and fine bands. Weights are independent of the spectral band, but \(\mathbf{S}\) is not. The mixing equation for shared values \(\mathbf{S}\) and weights \(\mathbf{W}\) is given as  
\[\hat{\mathbf{\Upsilon}}_{20m_{l}}^{10m}(x,y) =\hat{\mathbf{W}}_{0}(x,y)\hat{\mathbf{S}}_{l}(x,y)+\hat{\mathbf{W}}_{1}(x,y) \hat{\mathbf{S}}_{l}(x+1,y) \tag{2}\] \[+\hat{\mathbf{W}}_{2}(x,y)\hat{\mathbf{S}}_{l}(x,y+1)\] \[+\hat{\mathbf{W}}_{3}(x,y)\hat{\mathbf{S}}_{l}(x+1,y+1),\;l\in\{1,\dots,6\}.\]  
The weights are non-negative and sum-to-one. \(\hat{\mathbf{W}}\) and \(\hat{\mathbf{S}}\) are estimated using an iterative method. The shared values are processed through various normalizations, and geometric means to produce a set of corrected shared values \(\hat{\mathbf{S}}_{l}\) for each coarse band as detailed in [9] and they are then combined with the band independent weights, \(\hat{\mathbf{W}}\), to produce high-resolution estimates using (2). The general procedure for the \(\mathbf{Y}_{60m}\) bands is similar.
# Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks
## 1 Introduction
In order to lessen the impact of DoS attacks, [PERSON] [20] suggests a hierarchical resilient learning technique and creates a virtual reference signal for every agent to estimate the time-varying reference signal. Additionally, ref. [21] presents an attack detection mechanism and buffer-based compensation strategies to address the adverse effects of DoS attacks. Designing more effective compensation strategies to minimize or eliminate the impact of DoS attacks remains an important research challenge. The bipartite formation trajectory tracking problem for unknown non-affine nonlinear MASs that are subject to recurring DoS attacks is discussed in this study. The following is a summary of this work's primary contributions:  
1. For MIMO heterogeneous multi-agent bipartite formation systems with unknown dynamics, a controller is designed based on the MFAILC method. 2. To address the impact of periodic DoS attacks on MIMO heterogeneous MASs, a DoS attack compensation mechanism is introduced within the MFAILC framework to mitigate the effects of such attacks. 3. An improved dynamic event-triggered mechanism is proposed, which incorporates both consensus error and tracking error into the dynamic triggered condition. Compared to the event-triggered conditions in ref. [22], the proposed mechanism significantly reduces controller updates, alleviates communication and computational pressure, and further enhances the system's resilience against DoS attacks. This is how the remainder of the paper is structured: Graph theory and modeling of periodic DoS attacks are introduced in Section 2. The controller design is detailed in Section 3. The system stability analysis and dynamic event-triggered mechanism are discussed in Section 4. Section 5 reports the simulation experiments. Finally, the conclusions are summarized in Section 6.
## 3. Results and Discussion
### Mechanical Considerations Pertaining to Sensor Embedment  
As presented in previous research works [1; 2; 3], three key sinusoidal placement parameters (Figure 8 and Equations (1) and (2)) need to be carefully optimized. For example, larger amplitudes (V3) are desirable for larger monitoring coverage but smaller edge gaps (V1) are more likely to initiate delamination/debonding at lower stress levels than those in specimens without embedded FOSs [3]. When monitoring principal strains under uniaxial loadings, FOSs are required to be placed as straight as possible following the loading direction [1], while smaller period sizes (V4) are expected to help monitor shear strains when under torsion. In this article, the set of parameters (V1 = 0.375, V3 = 0.625, and V4 = 2.500, as illustrated in Figure 3) are chosen based on previous studies [1; 2; 3], assuming that they have been optimized preliminarily, and the goal is to further identify the resulting strain patterns from them in various loading cases.  
\[b=2\times 1+2\times\text{V3}. \tag{1}\]  
\[L=\text{n}\times\text{V4}. \tag{2}\]  
In Figure 8 and Equations (1) and (2), V1, V3, and V4 denote the edge gap, amplitude, and period size of a sinusoidal placement pattern, respectively, while \(b\) and \(L\) represent the width and length of the host material, respectively, in which \(L\) is equal to the product of n (the number of full periods) and V4 [3]. Each sinusoidal FOS is modeled with four full periods (n = 4) to predict the strain variations internal to specimens along the X-axis under three loading conditions. The mid-line is a construction line at half the width (0.5 \(b\)).  
The intrusive impact of embedded FOSs (discussed in Section 1) can be alleviated by embedding FOSs into structural adhesive joints, as no wavy reinforcing fiber or resin pocket is created [30; 31]. By monitoring the stress/strain fields inside adhesive joints, in-service structural mechanical behavior, as well as manufacturing quality, can also be evaluated by capturing any strain gradient developed [30; 31]. Therefore, in this study, distributed FOSs are modeled as being embedded into an adhesive joint between two CFRP laminates, which is also consistent with previous studies [1; 2; 3].  
A configuration of 3-ply laminate/adhesive joint/8-ply laminate (discussed in Section 2.1) is designed for the following two reasons: (1) to simulate a more realistic bonded assembly used in wind turbine blades [12], where the thickness and LSS of one laminate on one side of the adhesive joint is usually different from that on the other side--for example, a bonded assembly of spar and skin; (2) to monitor the resulting strains from various loading cases, FOSs should be away from the neutral plane.  
Figure 8: Three key placement parameters of a sinusoidal placement pattern [3].  
In addition, to alleviate the effect of warping, a symmetric quasi-isotropic LSS is designed for the 8-ply laminate ([45/0/\(-\)45/90]s), while symmetric LSS for the 3-ply laminate ([0/90/0]).
## 2 Materials and Methods
### GC-MS Analysis  
The individual volatile compounds were analyzed using gas chromatography coupled with a mass spectrometer (Model 7890A, Agilent Technologies, Santa Clara, CA, USA), equipped with a 30 m \(\times\) 250 \(\upmu\)m i.d. \(\times\) 0.25 \(\upmu\)m film thickness HP-5 MS column and a mass spectrometer (MS) detector (Model 5977A, Agilent Technologies, Santa Clara, CA, USA). The analysis was performed in splitless mode, and helium was used as the carrier gas (1 mL min\({}^{-1}\) flow rate). The solvent delay was also set at 3 min. The column temperature program was as follows: initially, the column was maintained at 40 \({}^{\circ}\)C for 3 min, then programmed at 5 \({}^{\circ}\)C min\({}^{-1}\) to 175 \({}^{\circ}\)C and held for 1 min, followed by 10 \({}^{\circ}\)C min\({}^{-1}\) to 275 \({}^{\circ}\)C, then held for 3 min with a total run time of 44 min. The mass spectrometer detector was operated in electron impact ionization mode (70 eV) by scanning a mass-to-charge ratio (m z\({}^{-1}\)) from 50 Da to 400 Da with a scan speed of 1.562 \(\upmu\)s\({}^{-1}\).