esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Experiment (2): Point-Based Absolute Accuracy and Precision for Single Strip Measurements
The results for the individual flights are shown in Figure 9 with a summary of the whole data set in the histogram on the right. The mean absolute height difference for all table heights are 3.4 cm with a RMSE of 1.3 cm. The systematic offset of about 3 cm is equal to the target-based analysis before, which in general describes a better and consistent absolute accuracy than expected. Also, the RMSE with 1.3 cm provides a good result based on the error budget assumed for the UAV-based laser scanning system. Overall the analysis compared to the TLS reference has shown an absolute accuracy defined by mean difference of <3 cm for the horizontal direction and <4 cm for the vertical direction. In addition, the precision of the system analyzed based on the repeated flights described with the standard deviation is <1.2 cm for the horizontal and vertical direction. These results should serve as a good benchmark for the transfer to other settings and primarily analyze the accuracy of the UAV's trajectory.
## 3 Quantifying Transmission Reserve Requirements in Real-Time Dispatch
### High Dimensionality and Sparse Grids  
Since GQ rule achieves high accuracy for one-dimensional approximation, it is natural to extend it to multivariate cases where there are multiple random variables. The most straightforward way is through tensor product approach. Let \(\Theta_{d}\) be a \(d\)-dimensional CP set, which can be constructed by tensor product of \(\Theta_{1}\) at each dimension as  
\[\Theta_{d}=\Theta_{1}^{m_{1}}\times\cdots\times\Theta_{1}^{m_{d}} \tag{32}\]  
where the superscript \(m_{i}\) denotes the number of CPs based on GQ rule at dimension \(i\). Assume that \(m_{1}=\cdots=m_{d}=m\), then it requires solving the real-time dispatch problem \(m^{d}\) times, which is obviously intractable for large dimensions. To tackle this issue, Smolyak sparse grid [35] is employed which is expressed as a linear combination of the tensor product  
\[\Theta_{d}=\mathop{\cup}\limits_{N-d+1\leq|\mathbf{m}|\leq N}\left(\Theta_{1}^{m_ {1}}\times\cdots\times\Theta_{1}^{m_{d}}\right) \tag{33}\]  
where \(|\mathbf{m}|=\sum_{i=1}^{d}m_{i}\); \(N\geq d\) is an integer denoting the construction level. It is clear that the resulting grid set is a union of the collection of subsets of the full tensor grids. Hence, the curse of dimensionality, albeit still exist, is largely lessened. An example of the comparison between two-dimensional sparse grids and tensor product is shown in Figure 2, where a significant reduction in the number of CPs is observed.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Biochemical Approaches to Identify mTOR Signaling Pathway Components
#### 4.1.7 TBC1D7 as the Third TSC Complex Component Was Identified by TSC2/TSC1 IPs  
The TSC2/TSC1 functions as a heterodimer [38] to utilize its GAP activity to suppress mTOR1 activity. Flag-epitope tagged TSC2/TSC1 complexes were immunoprecipitated by Flag antibody-conjugated beads from HEK293 cells with 1% NP40. Bound proteins were eluted with 3\(\times\) FLAG peptides dissolved in nondetergent containing buffers and subjected to mass spectrometry analyses. The most abundant protein was TBC1D7 that associates with TSC1 to stabilize the TSC2/TSC1 complex towards suppressing mTOR1 [39].  
1.8 Rheb as a TSC2/TSC1 GAP Substrate Was Identified by the Biochemical Isolation of GTP/GDP Bound Rheb  
Rheb was firstly characterized as a GTPase from screening of mRNAs induced in neurons by seizures-provoking agents [40]. In fission yeast, deletion of _Rhb1_, the yeast homolog of mammalian Rheb, mimicked nitrogen starvation to cause growth arrest [41]. To identify TSC2/TSC1 GAP substrates, [PERSON] et al. utilized GST-fused GTPase activity probes composed of GTPase/GTP-binding domains [41] to pull down GTPases from serum-starved _TSC2\({}^{\prime\prime+}\)_ and _TSC2\({}^{\prime-\prime}\)_ MEF cells lysed with 1% NP40 [42], to search for increased GTPases activities upon TSC2 deletion. Due to low binding affinity of Rheb with TSC2/TSC1, this assay turned out to be unsuccessful. Instead, Rheb was immunoprecipitated from \({}^{32}\)P-labeled, serum-starved _TSC2\({}^{\prime+}\)_ and _TSC2\({}^{\prime-\prime}\)_ MEFs, respectively, and the ratio of GTP/GDP bound Rheb was determined by one-dimensional thin-layer chromatography (TLC). Using this approach, GTP-bound Rheb was enriched from _TSC2\({}^{\prime-\prime}\)_ MEFs [42], suggesting Rheb is a candidate of TSC2/TSC1 GAP substrates.
## 3 Results and Discussion
### Analysis of Apoptosis Using the Muse(r) Flow Cytometer
These compounds can influence cellular and molecular mechanisms related to carcinogenesis, such as the inhibition of factor NF-\(\kappa\beta\), induction of cell cycle arrest or apoptosis through initiation of caspase-3-dependant pathway or due to activation of p53, affecting cell proliferation and differentiation or chemical metabolism [54]. Phenolic acids and flavonoids, including anthocyanins, were found in young shoots, in mature red cabbage and in juices obtained from them (also in juices used in this study) [11]. Phenolic acids have been shown to prevent angiogenesis and inhibit the initiation and progression of cancers. Flavonoids possess antioxidative, anti-inflammatory, and also anticancer properties including the suppression of angiogenesis, induction of apoptosis and down-regulation of hormone receptor expression [55]. The synergistic effects of flavonoids on the inhibition of LNCaPP cells proliferation have also been studied [56]. The data from cell viability assay showed that anthocyanins exhibited a potent anti-proliferation effect on AGS, PC3, C4-2, and LNCaPP cell lines and this process was related to the cell cycle arrest, modulation of NF-\(\kappa\beta\) signaling, or activation of apoptosis [57; 58]. The occurrence of apoptosis induced by anthocyanins was confirmed by morphological and biochemical features, including apoptotic bodies formation, down-regulating the Bcl-2 protein, and up-regulating levels of Bax protein, caspase-3 activation, poly(ADP-ribose) polymerase proteolysis, modulating the expression of FAS and increasing the p38 kinase expression, and inhibiting ERK activity [59; 60].
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 2 Materials and Method
### Algorithmic Principle  
#### 2.2.1 Least Squares and Indirect Adjustment  
The indirect adjustment based on the least squares principle is widely used in geodetic network adjustment. It involves estimating unknown parameters from a set of observations by minimizing the sum of the squares of the residuals [25].  
For a linear model, the observation equation, namely, the mathematical model that relates the observations to the unknown parameters, can be expressed as:  
\[\mathbf{L}=\mathbf{B}\mathbf{\hat{X}}+\mathbf{d} \tag{1}\]  
where L is the vector of observations, \(\mathbf{B}\) is the design matrix, \(\mathbf{\hat{X}}\) is the vector of unknown parameters, and \(\mathbf{d}\) is the vector of residuals.  
The error equation is:  
\[\mathbf{V}=\mathbf{B}\mathbf{\hat{x}}-\mathbf{1} \tag{2}\]  
where \(\mathbf{\hat{x}}=\mathbf{\hat{X}}-\mathbf{X}^{0}\), \(\mathbf{1}=\mathbf{L}-\mathbf{L}^{0}=\mathbf{L}-(\mathbf{B}\mathbf{X}^{0}+ \mathbf{d})\), and \(\mathbf{X}^{0}\) are the initial values of \(\mathbf{\hat{X}}\).  
The goal of least squares is to find the values of \(\mathbf{\hat{x}}\) that minimize the sum of the residuals' squares, \(\mathbf{V}^{\text{T}}\mathbf{PV}\), where P is the weight matrix of the observations. This leads to the normal equations:  
\[\mathbf{N_{B}}\mathbf{\hat{x}}=\mathbf{W} \tag{3}\]where \(\mathbf{N_{BB}}=\mathbf{B^{T}PB}\) is the coefficient matrix of the normal equation, \(\mathbf{W}=\mathbf{B^{T}PI}\).  
For the case of classic free network adjustment and constraint adjustment, the coefficient matrix is full-rank, and then the solution is:  
\[\mathbf{\hat{x}}=\mathbf{N_{BB}^{-1}W} \tag{4}\]  
Once the parameters of \(\mathbf{\hat{x}}\) are determined, the residuals of \(\mathbf{V}\) can be calculated according to Equation (2). The unit weight variance can be calculated using the following formula:  
\[\sigma_{0}=\sqrt{\frac{\mathbf{V^{T}PV}}{n-t}} \tag{5}\]  
where \(n\) is the total number of observations, and \(t\) is the number of necessary observations.  
The variance-covariance matrix of the estimated parameters is given by:  
\[\mathbf{Q_{\hat{x}\hat{x}}}=\mathbf{N_{BB}^{-1}} \tag{6}\]
# Photodegradation Assessment of Calicotriol in the Presence of UV Absorbers by UHPLC/MSE
## 2 Experimental
### Method Validation
#### 2.5.5 Accuracy  
The accuracy of the method was evaluated by analyzing methanolic calcipotriol solutions at three different concentration levels: 75 \(\upmu\)g mL\({}^{-1}\), 100 \(\upmu\)g mL\({}^{-1}\), and 150 \(\upmu\)g mL\({}^{-1}\) (corresponding to 75%, 100%, and 150%, respectively). Each concentration was tested in triplicate. The recovery for each sample was determined, and the mean recovery was calculated for each concentration level to assess the method's accuracy.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 3 Materials and Methods
### Datasets Description
#### 3.1.1 Indian Pines  
Indian Pines is situated in Indiana, USA. This landscape, which includes lush, agricultural land, vegetation, and a natural woodland area, has a total resolution of \(145\times 145\times 224\). The water's ability to reflect and refract light causes the water's absorption bands to disappear. Bands 104 to 108, 150 to 163, and 200 have been eliminated. As a result, the updated image has 200 bands. As seen in Figure 2, it has 16 distinct classes with a total pixel count ranging from 20 to 2455.
# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 2 Related Work
In this architecture, a CNN model is coupled with a fully connected conditional random field (CRF) to obtain the contextual relation and the interactions between the semantics of the image and depth cues. [PERSON] et al. [33] developed joint task-recursive learning (TRL) for semantic segmentation and depth estimation and showed that TRL can recursively refine the results from both tasks using a task attention module. Further, a hybrid CNN for depth estimation and semantic segmentation (HybridNet) was developed by [PERSON] et al. [34]; this network functions by sharing the parameters that can yield mutual improvement for each task. Finally, [PERSON] et al. [35] proposed semantic object segmentation and depth estimation (SOSD)-Net for application to monocular images; in this approach, semantic objectness is used to perform image processing based on the geometric relationship between the two tasks. The aligned Xception architecture is employed. In all of the related work, the studies adopted mainly encoder-decoder approaches with some methods adopted extra attention methods or transformers. The proposed method mainly eliminates the need for a complex decoding stage which is computationally expensive, eliminates some of the image details, and propagates noise. Hence, we propose a simple and fast approach for dense prediction. We use the DTS-layer as the decoding stage to directly construct a dense map out of small feature maps extracted from an encoding stage.
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 3 Methodology
### The Main Network WLdetectNet  
In this paradigm, we specify the vision-based waterline detection as an end-to-end binary classification model, which integrates the waterline-relevant feature representation and its subsequent waterline discriminator in a customized deep convolutional neural network, i.e., WLdetectNet. To improve the accuracy of waterline detection, we deliberately devise the following two specialized schemes to construct the deep model.
# Gaussian Process Modeling of Specular Multipath Components
## 4 Results
### GPR of SMC Amplitudes
#### 4.3.2 Variance Verification  
In this section, we will validate the capability of the GPR to model the power of the DMC by means of the predicted variance, cf. Equation (17). Based on the estimated SMC of VA at \(\mathbf{a}_{2}^{(1)}\) associated with anchor 1 (east plaster board wall), the empirical mean and the empirical standard deviation are calculated over a sliding window along the trajectory positions, that is,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MPC** & **SMSE** & **MSLL** \\ \hline \multirow{4}{*}{**Variant**} & EPB—anchor 1 & 0.35 & \(-\)0.381 \\  & LOS—anchor 1 & 0.014 & \(-\)2.099 \\  & NGW—anchor 1 & 0.46 & \(-\)0.175 \\  & SW—anchor 2 & 0.762 & \(-\)0.242 \\ \hline \multirow{4}{*}{**Variant**} & EPB—anchor 1 & 0.143 & \(-\)0.834 \\  & EPB—anchor 2 & 0.058 & \(-\)1.25 \\  & NGW—anchor 1 & 0.294 & \(-\)0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction quality.  
Figure 4: GP prediction of SMC amplitude for **(a)** the LOS amplitude associated with anchor 1 at \(\mathbf{a}_{1}^{(1)}\), **(b)** the SMC amplitude of VA at \(\mathbf{a}_{2}^{(1)}\) associated with anchor 1 (east plaster board wall), **(c)** the SMC amplitude of VA at \(\mathbf{a}_{5}^{(1)}\) associated with anchor 1 (north glass window), **(d)** the SMC amplitude of VA \(\mathbf{a}_{3}^{(2)}\) associated with anchor 2 (south wall).  
\[\mu_{\text{meas},m}=\frac{1}{N_{\text{win}}}\sum_{i=m}^{N_{\text{win}}+m}|\hat{ \alpha}_{k}| \tag{23}\]  
and  
\[\sigma_{\text{meas},m}=\sqrt{\frac{1}{N_{\text{win}}}\sum_{i=m}^{N_{\text{win}}+ m}(|\hat{\alpha}_{k}|-\mu_{\text{meas},m})^{2}}, \tag{24}\]  
where \(m\) is the index of the agent position and \(N_{\text{win}}\) is the sliding window size, which is chosen to be \(N_{\text{win}}=10\).  
Figure 5a,b compare the empirical mean in (23) and the empirical standard deviation in (24) with the predicted mean (given by (16)) and the predicted standard deviation (square-root of (17)) of the GP along the agent trajectory. In Figure 5a, the blue solid line shows the estimated SMC amplitudes, the red solid line shows the empirical mean \(\mu_{\text{meas},m}\) and the red dashed lines the empirical standard deviation (\(\pm 2\sigma_{\text{meas},m}\)) of the estimated amplitudes. In Figure 5b, the blue solid line shows the estimated SMC amplitudes, the green solid line shows the predicted mean and the green dashed lines the predicted standard deviation (\(\pm 2\sigma_{\text{GP}}\)) of the SMC GP amplitude. Both, the mean and the standard deviation are in a good agreement. However, the predicted standard deviation (variance) in (17) has constant values for the DMC standard deviation (see Table 1), independent of the measurements used for prediction, and only changes deterministically with the angle via the first term of the correlation kernel in (13).  
Figure 5: Estimated alpha compared to (**a**) measurement and (**b**) GP statistics (\(\mathbf{a}_{2}^{(1)}\)).
# The Importance of Incorporating Denitrification in the Assessment of Groundwater Vulnerability
## Materials and Methods
### LOS Indices  
The LOS indices [33] are generally used to assess the intrinsic vulnerability of agricultural land to water (LOSW) and nitrogen (LOSN) losses through percolation and run-off. They are indices build via multiple regression analysis using the GLEAMS V3.0 model [37; 38]. LOS indices were developed for two main goals: (i) to avoid the complexity typical of process-based models which require a big amount of data to describe the aquifer properties and (ii) to reduce the subjectivity of the indices when using weights and ratings. Anyhow, LOS indices are limited to the agricultural soils since GLEAMS was developed specifically for these kinds of soils. They were successfully calibrated and applied in Italy and Greece with good results [39; 40]. The general forms of the LOS indices for water and nitrogen losses are the following:  
\[LOSW-P=\left\{a_{1}\sqrt{K_{si}}+a_{2}\sqrt{S_{i}}+a_{3}\sqrt{PCP_{i}}+a_{4} \sqrt{PE_{i}}+a_{5}\sqrt{IR_{i}}\right\}^{2} \tag{1}\]  
\[LOSN-P=\left\{a_{1}\sqrt{OM_{i}}+a_{2}\sqrt{T_{i}}+a_{3}\sqrt{K_{si}}+a_{4} \sqrt{S_{i}}+a_{5}\sqrt{PCP_{i}}+a_{6}\sqrt{PE_{i}}+a_{7}\sqrt{IR_{i}}\right\} ^{2} \tag{2}\]  
where: LOSW is the water losses (mm/y), LOSN is the nitrogen losses (kg/ha/y) and P indicates the percolation process, Ks is the soil hydraulic conductivity (mm/d), S is the surface slope (%), PCP is the  
Figure 1: Study areas: (**a**) Campania plain, (**b**) Garigliano River plain, (**c**) Florina basin, (**d**) Epanomi.  
precipitation (mm/y), PE is the potential evapotranspiration (mm- y-1), IR is the irrigation applied by the model (mm/y), OM is the organic matter (%) and T is the mean annual temperature (\({}^{\circ}\)C).
# Effects of Valve, Armature, and Armature Pin Guidance on Diesel Injector Performance
## 1 Introduction
### Novelty  
Existing literature on injector performance improvement primarily focuses on variables such as fuel pressure, energization timing and duration, needle opening profile, and total backflow during the injector's static and dynamic behavior. However, injection processes are fundamentally driven by the hydraulic pressure differences created by backflow through the valve, armature, and armature pin. Research exploring the causes of these pressure differences, particularly the guidance effects of the valve, armature, and armature pin, is notably scarce. This scarcity is partly attributable to the limited accessibility of these components within the injector. Additionally, conducting functional tests on assembled injector sub-parts demands highly specialized test stations, further complicating research efforts.  
Our study aims to address this gap by investigating the impact of geometric variability between the valve, armature, and armature pin on armature guidance. A controlled experimental design was utilized to manufacture injectors, which were then analyzed using computational fluid dynamics through the Ansys Fluent 2022 R1 software to evaluate the effects of dimensional variations on backflow. The results of these tests and simulations were subsequently used to develop a correlation matrix. This matrix quantifies the influence of alterations in injector geometry tolerances on injector performance. Ultimately, this research seeks to identify optimal guidance parameters that could significantly enhance injector function and efficiency.
## 9 Conclusions  
In conclusion, a dual band (C- and Ku-) perfect metamaterial absorber (PMA) based on a dumbbell-shaped tuning fork copper strip with octagonal closed ring resonator (OCRR) is proposed and investigated by numerical simulation (CST and HFSS) and equivalent circuit model in the 4 to 18 GHz frequency range. The designed absorber attains absorbance of over 99% in a wide bandwidth of a wave's incident at angle 0\({}^{\circ}\). The resonance peaks located at 6.45 GHz and 14.89 GHz with an absorption of efficiency 99.15% and 99.76%, respectively, in TE polarization mode. The proposed dual band absorber exhibits high absorption of up to 30\({}^{\circ}\) polarization insensitivity and wide oblique incidence angle of up to 60\({}^{\circ}\). In addition, the simulated results for different combinations of the array (2 \(\times\) 2, 4 \(\times\) 4, 8 \(\times\) 8) exhibit the same resonance with minor variations. Compared to the contemporary published absorber, it has the benefit of simple design and easy implementation. This work offers an exotic  
Figure 12: Comparison of CST results (**a**) S11 (**b**) Absorption.  
Figure 13: Comparison of absorbance by CST and HFSS result.  
dual band metamaterial perfect absorber, which has a widespread application in satellite communication bands, defense, security, and stealth-coating technology. This exclusive metamaterial absorber design reformed some authentication progressions that led to its uniqueness.  
Conceptualization, M.S.U.A.; M.R.I.F.; methodology, M.S.U.A.; M.B.H.; M.R.I.F. software, M.S.U.A., M.B.H. and A.M.S. validation, M.S.U.A.; A.M.S. and M.R.I.F; formal analysis, M.S.U.A.; M.B.H. and M.R.I.F.; investigation, M.S.U.A. and M.R.I.F. data curation, M.S.U.A., M.B.H. and A.M.S.; writing--original draft preparation, M.S.U.A.; writing--review and editing, M.S.U.A., M.R.I.F., M.B.H. and M.U.K.; visualization, M.S.U.A.; M.R.I.F.; M.B.H.; M.U.K.; A.A.; D.A.B. and A.M.S.; supervision, M.R.I.F.; project administration, M.R.I.F.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Research Universiti Grant, Universiti Kebangsaan Malaysia, Geran Universiti Penyelidikan (GUP), code: 2021-074.  
All the data is available within the manuscript.  
The authors declare no conflict of interest.
## 3 Results
### Reliability Evaluations  
This subsection's experiment assesses the reliability of the proposed method in different SNR and SIR situations. The SNR value is defined as  
\[SNR=10\times\log(P_{s}/P_{n}) \tag{47}\]  
where \(P_{s}\) and \(P_{n}\) are the image level powers of signals and ambient noise. Gaussian white noise is used as the ambient noise. Figure 8 shows the evaluation results, and the LTM is evaluated in the first column of the sub-figures at SNR = \(-21.65\) dB (\(\sigma=0.85\)); we can see that the targets that are being monitored have failed. However, as SNR is further dropped, as demonstrated in the second- and third-column sub-figures, Targets 1 and 4 are lost until \(-25.11\) dB (\(\sigma=1.25\)).  
Figure 6: Experiment results with jamming targets.  
Figure 7: A single snap of BTR.  
The SIR boundary of the algorithm is evaluated by using the data shown in Figure 9 in which there are three target trajectories. From the starting point, the second trajectory from the left has the weakest energy, SIR\(=-25\) dB, background SNR = 0 dB, and there is a clear intersection with the strong target. As shown in Figure 9, we successfully track it using the proposed method, i.e., reaching the performance limit SIR = \(-23\) dB.  
The results of this subsection's experiments demonstrate that the suggested approach can track an interested target stably despite jamming-target interference and has the ability to suppress noise.