esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Adaptation to Cd Stress on Biomass of the Middle European Ecotype of S. nigrum
#### 3.3.5 Second Line Defence Antioxidant Activities
This resulted in the basically constant ratio GSH/GSSG at T10-T50, higher than at T0 in both varieties (1.0 > 0.9 in N0 and 1.40 > 1.29 in A50), and significantly lower in N0 than in A50 (1.0 vs. 1.40). This indicated an increase of defensive capacity in both varieties at Cd increase in the soil and lesser oxidative stress in the A50 variety, with a generally weak response of second line glutathione antioxidant compounds to oxidative stress. Along with moderate reduction of the first line defense antioxidant GPX in N0 at the highest Cd treatment T50 in the soil, and its higher activity in N50 at T50 than at T0 treatment, this points to the strong glutathione-based ROS resistance mechanism on the one hand,  
Figure 6: Response of the second-line defense antioxidants ascorbate: (**a**) APX, (**b**) AsA and glutathione compounds: (**c**) GR, (**d**) GSH, (**e**) GSSG activity and (**f**) MDA content in the leaves of non-adapted N0 and adapted A50 varieties of _S. nigrum_ L. to the growing Cd stress. (1) 0 to 50 means control and soil treatments with Cd (mg kg\({}^{-1}\)); (2) Data for the same treatments marked by the same capital letters over bars are not significantly different at \(p<0.05\). Data for different treatments marked by the same lowercase letters over bars are not significantly different at \(p<0.05\). and lesser involvement of glutathione compounds into Cd-induced ROS detoxification in comparison with CAT, SOD and ascorbate on the other hand. High resilience of the Middle European ecotype of _S. nigrum_ to Cd stress was confirmed by trends in the generation of the plant cell damage indicator malondialdehydedehyde MDA in the leaves of studied non-adapted N0 and adapted A50 varieties (Figure 6f). In the range from T0 to T20 treatments, MDA contents in N0 increased, reflecting growing oxidative damage. However, in the range T20-T30, MDA remained at the constant level. Formation of MDA in A50 was significantly smaller than in N0 (by 21-46%), stabilizing in the T30-T50 range of Cd treatment. Such trends in MDA generation indicate high tolerance of both varieties of the Middle European ecotype of _S. nigrum_ to abiotic stress caused by the growing contents of Cd in the soil, and significantly lesser oxidative damage in the A50 variety. Remarkable, that both varieties of the Middle European ecotype, non-adapted N0 and adapted A50, opposite to the Asian ecotypes [5] showed strong resistance to similarly high-levels of Cd stress (T30-T50) and differed mostly by the level of oxidative damage (by 46-22% lesser in A50).
## 3 Controller Design
### Neclgpc Terminal Control Law \(F(k)\)  
In this work, the terminal control law in the NEClgpc is determined online by an unconstrained NGPC Control with finite control and predictions horizons minimizing a cost function constituted by two important terms, the first one for set point tracking and the second for taking into account the economic cost that is approximated by means of its gradient.  
Next, the following notation has been introduced:  
\[\left[\prod_{i=I}^{n_{u}}\tilde{A}(k+i)\right]=\left\{\begin{array}{ll}\tilde{A }(k+n_{u})\tilde{A}(k+n_{u}-1)\dots\tilde{A}(k+l)&if\quad l\leqslant n_{u}\\ I&if\quad l\geqslant n_{u}\end{array}\right.\]  
where \(I\) denotes the identity matrix of appropriate size.  
Then (25) may represented as:  
\[\begin{split}\chi(k+i)=&[\prod_{i=0}^{i-1}\tilde{A}(k+i)] \chi(k)+[\prod_{i=1}^{i-1}\tilde{A}(k+i)]\tilde{B}(k)\Delta u(k)\\ &+[\prod_{i=2}^{i-1}\tilde{A}(k+i)]\tilde{B}(k+1)\Delta u(k+1)\\ &+\dots\\ &+[\prod_{i=n_{u}}^{i-1}\tilde{A}(k+i)]\tilde{B}(k-1+\min(i,n_{u}) \Delta u(k-1+\min(i,n_{u})))\end{split} \tag{26}\]  
Now using (26) the following equation for the future state predictions vector \(\underline{\chi}(k)\) is obtained:  
\[\underline{\chi}(k)=\Omega(k)\tilde{A}(k)\chi(k)+\Psi(k)\Delta\underline{u}(k) \tag{27}\]  
where  
\[\Omega(k)=\left[[\prod_{i=1}^{0}\tilde{A}(k+i)]^{T}\dots[\prod_{i=1}^{n_{u-1}} \tilde{A}(k+i)]^{T}\right] \tag{28}\]  
\[\Psi(k)=\left[\begin{array}{cccc}[\prod_{i=1}^{0}\tilde{A}(k+i)]\tilde{B}( k)]&0&\cdots&0\\ [\prod_{i=1}^{0}\tilde{A}(k+i)]\tilde{B}(k)]&[\prod_{i=2}^{0}\tilde{A}(k+i)] \tilde{B}(k+1)&\ddots&\cdots\\ \vdots&\vdots&\ddots&\cdots\\ [\prod_{i=1}^{n_{u}-1}\tilde{A}(k+i)]\tilde{B}(k)&-&-&[\prod_{i=n_{u}}^{n_{u}- 1}\tilde{A}(k+i)]\tilde{B}(k+n_{u}-1)\end{array}\right]\]  
From the output Equation (21) it is clear that  
\[y(k+i)=C(k+i)\chi(k+i) \tag{29}\]  
Combining the outputs in (29) and (27) the following relationship between vectors \(\underline{x}(k)\) and \(\underline{y}(k)\) is obtained:  
\[\underline{y}(k)=\theta(k)\underline{\chi}(k) \tag{30}\]  
where \(\theta(k)=diag(C(k+1),C(k+2),\dots,C(k+n_{y}))\)  
Finally substituting in (30) \(\underline{\chi}(k)\) by (27) the following equation for output prediction is obtained:  
\[\underline{y}(k)=\phi(k)\tilde{A}(k)\chi(k)+S(k)\Delta\underline{u}(k) \tag{31}\]  
where  
\[\phi(k)=\theta(k)\Omega(k),\quad S(k)=\theta(k)\Psi(k)\]  
Substituting \(\underline{y}(k)\) in the cost function (24) by the Equation (31) and performing the analytical minimization, \(\Delta\underline{u}\) is obtained by deriving the cost function:  
\[\Delta\underline{u}=(S^{T}(k)w_{1}S(k)+w_{2}+G^{T}w_{3}G)^{-1}[S(k)w_{1}( \underline{r}(k)-\phi(k)\tilde{A}(k)\chi(k))-G^{T}w_{3}D] \tag{32}\]  
By denoting:\[F(\chi(k)) =(S^{T}(k)w_{1}S(k)+w_{2}+G^{T}w_{3}G)^{-1}S(k)w_{1}\Phi(k)\tilde{A}(k) \tag{33}\] \[d(k) =(S^{T}(k)w_{1}S(k)+w_{2}+G^{T}w_{3}G)^{-1}[S(k)w_{1}\underline{ \Gamma}(k)-G^{T}w_{3}D]\]  
The Equation (32) becomes:  
\[\Delta\underline{u}(k)=-F(\chi(k))\chi(k)+d(k) \tag{34}\]
## 9 Discussion on Controllability Modeling of PNDs Particle Synthesis Using CDP Method  
PNDs have the potential to enhance the stability and solubility of encapsulated cargo, facilitate membrane transport, and extend circulation times for increased safety and efficacy. Nanoparticle size and shape could be a significant factor influencing particulate drug delivery systems [42; 43]. Hence, ensuring precise control over the nanoscale dimensions and aspect ratio of PNDs is of paramount significance. [PERSON] and co-workers [44] fabricated cylindrical pore-shaped models and then produced PDMS micro-pillar arrays through processes involving spin-coating, curing, and peeling. Utilizing initiated chemical vapor deposition (iCVD), they synthesized nanoscale pPFDA structures on the PDMS micro-pillar  
Figure 10: The evolution of PNDs’ size over a duration of 2 min and 20 s during the stage of removing unreacted monomers and initiators, with a scale bar of 100 nm.  
Figure 9: Extract unreacted monomers and initiators after polymerization. The yellow spheres in the diagram represent initiators, the blue spheres represent monomers, and the red spheres represent polymer chains.  
arrays. This strategy of creating a micro/nanostructured PDMS-pPFDA superhydrophobic architecture offers valuable insights into the size control of PNDs.  
As shown in Figure 11, after PDMS (poly(dimethylsiloxane)) was subjected to spin-coating, curing, and peeling off processes, it was placed onto the substrate inside the reaction chamber of iCVD. A single-step iCVD process was then utilized to synthesize the pPFDA film. The synthesis of pPFDA is not our primary focus; rather, it is the protruding micro-scale pillar arrays that we have identified as having the potential for the size-level control of PNDs. The presence of micro-structured PDMS constrains the dimensions of the pPFDA film to smaller levels. Consequently, this limitation extends to the size of the PNDs synthesized on the superhydrophobic pPFDA film. To achieve specific sizes for the PNDs, the employment of the protruding pillar arrays becomes advantageous. In a uniform protruding model, the process of nanodroplet coalescence on the nanodomes may be influenced. Smaller droplet nanodomes could potentially be lost due to the absence of neighboring nanodomes at the edges available for merging.
## 1 Introduction
As a result of the combination of MWCNTs and a VNR matrix, conductive nanocomposites with unique and highly desirable electrical properties can be produced. Various applications of these conductive nanocomposites can be found, including flexible electronics, advanced sensors, and energy storage devices such as supercapacitors [45; 46; 47; 48; 49]. Recent studies indicate that incorporating MWCNTs into NR significantly improves the latter's mechanical and thermal properties. The effectiveness of this reinforcement is influenced by factors such as the surface modification of MWCNTs, a process that enhances their adhesion to the polymer matrix, leading to better performance in terms of tensile strength and thermal stability. For instance, research has shown that MWCNT reinforces composites can exhibit substantial improvements in tensile strength and thermal stability depending on the concentration and dispersion of nanotubes within the matrix. A novel aspect of this research is the application of the Lorenz-Park methodology to investigate the interactions between MWCNTs and vulcanized natural rubber (VNR). This approach allows for a theoretical-experimental adjustment that provides deeper insights into the reinforcement mechanisms at play. Using a two-roll mill for preparation, we systematically examined how dispersion and synergy between MWCNTs and VNR affect their rheological, mechanical, thermal, and electrical properties.
## 4 Experimental Section
### Findings and Limitations in This Experiment
#### 4.4.3 Effectiveness of Image Masking in Addressing Sparse Rewards  
Experimental results show that the image masking approach significantly improves learning efficiency in addressing the sparse rewards problem. In the scenario without masking, the agent requires more iterations to achieve a sufficient success rate, since the reward is received only at certain times when the task is successfully completed. In contrast, when image masking is used, the agent receives rewards more frequently, which accelerates convergence and reduces the training time required.  
A performance comparison between models with and without image masking shows a clear improvement in convergence and task success metrics. For example, the agent with image masking achieves a 30% higher success rate in fewer iterations compared to the model without masking. This indicates that image masking not only helps in focusing exploration on more relevant areas but also effectively addresses the sparse rewards problem by providing more meaningful training signals.  
In addition, the use of image masking is proven to provide an improvement in model efficiency. This is evidenced by the decrease in the number of steps required to complete the task and the increase in the learning speed of the agent. Thus, the results of this study support the claim that the image masking approach can overcome the challenge of sparse rewards, accelerate the learning process, and improve overall performance in robotic manipulation tasks. The simulations and experiment models developed can be seen at the following video link [[https://feji.us/tv3](https://feji.us/tv3) sbq]([https://feji.us/tv3](https://feji.us/tv3) sbq) (accessed on 11 September 2024).
# Dissimilar Rotary Friction Welding of Inconel 718 to F22 Using Inconel 625 Interlayer
## 3 Results and Discussion  
### Microstructure: Base Metal  
Figure 2a,b shows optical and scanning electron micrographs of the alloy F22, respectively. Optical micrograph of the F22 showed tempered martensitic microstructure without the apparent grain boundaries and phase morphology due to the fine nature of the temperate martensite. However, the higher magnification SEM micrograph of the F22 (Figure 2b) clearly showed tempered martensite colonies outlined by the prior austenitic grain boundaries. Inconel 718 and Inconel 625 base metal optical and SEM micrographs showed a grain structure, respectively, as seen in in Figure 2c-f. The average grain size values of the Inconel 718 and Inconel 625 were 23 \(\pm\) 2 \(\upmu\)m and 85 \(\pm\) 3 \(\upmu\)m, respectively. Twins were observed in the microstructure of the Inconel 625. X-ray diffraction patterns of base metals were indexed and are shown in Figure 3. Alloy F22 base metal showed diffraction peaks corresponding to the martensite, while 718 and 625 showed diffraction peaks corresponding to the austenitic phase.  
Figure 2: \(Cont\).  
Figure 3: XRD diffraction patterns of the alloy F22, 718, and 625 base metals.  
Figure 2: Optical micrograph and secondary electron micrographs of the F22 base metal (**a**,**b**), 718 base metal (**c**,**d**), and 625 base metal (**e**,**f**).  
EDS analysis of the precipitates observed in the Inconel 718 electron micrographs were found to be rich in Ti and C, with a relatively smaller quantity of Nb (Figure 4a). These precipitates were identified as the MC type of carbides rich in Ti (M=Ti, Nb), which precipitate during cooling from higher temperatures. These MC types precipitate control grain growth during the ageing treatment and during the high-temperature service, imparting higher creep resistance to the Inconel 718 [23]. Similar MC types of carbides rich in Nb were observed from the EDS analysis of the Inconel 625 (Figure 4b). Mo and Cr were found to distribute in the \(\gamma\) phase because of the good solid solubility in Ni. It was reported that the preferential partitioning of the Mo and Cr into \(\gamma\) phase would promote carbide formation by reducing the solubility of strong carbide forming elements such as Nb and Ti [21]. Further, Mo and Cr also reduced the stacking fault energy of the \(\gamma\) phase and contributed to the strength by retarding the cross slip during the plastic deformation.
# Content and Privacy Protection in JPEG Images by Reversible Visual Transformation
## 3 Proposed Algorithm for Content Protection in JPEG Images
### Generating the Visually Transformed JPEG Images
#### 3.1.1 Key Generation  
For security concern, a secret key is used in the proposed visual transformation algorithm. The secret key is shared between the sender and receiver, or can be transmitted by using public-key cryptography. To generate different keys for different images to be protected, the secret key can be concatenated to the mega information of an individual image to generate a hash value of 128 bits by MD5 [21]. Then the hash value is used as the seed of a pseudo-random number generator (PRG) to generate random sequences in the flowing steps. In this way, the user can use the same key to obtain different seeds for different JPEG images to resist brute force attack [22].
## 3 Methods
### Framework Architecture
```
0: Image \(X_{0}\), Model \(f(X)\), layer \(l\)
0:\(L_{MS-CAM}\)  Initialization;  // get activation of two layers; \(A_{l}\), \(A_{l-1}\), logit \(\gets f(X_{0})\) \(predict_{class}=sigmoid(logit)\) \(Cloud_{mask}=(predict_{class}\geq 0.5)\) \(Sky_{mask}=(predict_{class}<0.5)\) // up sample \(A_{l-1}\) to match \(A_{l}\) spatial dimensions; \(A_{l-1}^{up}\gets Upsample(A_{l-1})\) //concatenate features along channel dimension; \(A_{concat}\gets Concat\left(A_{l},A_{l-1}^{up}\right)\) \(M\leftarrow[]\) \(C\leftarrow\) the number of channels in \(A_{concat}\) for\(k\)in \([0,\dots,C-1]\)do \(M_{l}^{k}\gets A_{concat}^{k}\) //normalize the activation map; \(M_{l}^{k}\gets s\left(M_{l}^{k}\right)\) //Hadamard product; \(M\).append (\(M_{l}^{k}\circ X_{0}\)) end for \(M\gets Batchify(M)\) //\(f(\cdot)\) as the logit of class; \(S\gets f^{c}(M)\) \(\alpha_{k}\gets Sigmoid(S_{k})\circ Cloud_{mask}\) \(\beta_{k}\gets Sigmoid(S_{k})\circ Sky_{mask}\) \(L_{Cloud-CAM}\gets ReLU(\sum_{k}\alpha_{k}A_{concat}^{k})\) \(L_{Sky-CAM}\gets ReLU(\sum_{k}\beta_{k}A_{concat}^{k})\)
```  
**Algorithm 1** Multi-Scale CAM algorithm
## 3 Proposed Method
### Message Extraction Algorithm  
This section describes the message extraction, consisting of two phases, as shown in Figure 2. The message extraction is in a reverse order. In Phase-1, the recipient inputs dual stego images, \(I^{\prime\prime}_{s1}\) and \(I^{\prime\prime}_{s2}\), the weight vector, \(\mathbf{W_{b}}\) = [\(w_{1}\), \(w_{2}\)], and the authorized secret key, \(key_{3}\), to extract the latest \(k\)-ary secret message vector, \(\mathbf{d_{du}}\) = (\(d^{1}_{k}\), \(d^{2}_{k}\), \(d^{3}_{k}\)), from every  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\mathbf{W_{b}}\) = [1, 6] & \(\mathbf{W_{b}}\) = [2, 5] & \(\mathbf{W_{b}}\) = [3, 4] \\ \hline d\({}_{7}\) & \(V_{\text{d}}\), & \(V_{\text{d}}\), & \(V_{\text{d}}\), \\ \hline
0 & \(\mathbf{V_{0}}\) = [0, 0] & \(\mathbf{V_{0}}\) = [0, 0] & \(\mathbf{V_{0}}\) = [0, 0] \\
1 & \(\mathbf{V_{1}}\) = [1, 0] & \(\mathbf{V_{1}}\) = [\(-\)1, 2] & \(\mathbf{V_{1}}\) = [\(-\)1, 1] \\
2 & \(\mathbf{V_{3}}\) = [2, \(-\)1] & \(\mathbf{V_{3}}\) = [\(-\)1, 2] & \(\mathbf{V_{3}}\) = [\(-\)2, \(-\)1] \\
4 & \(\mathbf{V_{4}}\) = [2, \(-\)2] & \(\mathbf{V_{4}}\) = [1, \(-\)1] & \(\mathbf{V_{4}}\) = [1, 0] \\
5 & \(\mathbf{V_{5}}\) = [\(-\)2, 2] & \(\mathbf{V_{5}}\) = [\(-\)1, 1] & \(\mathbf{V_{5}}\) = [0, 1] \\
6 & \(\mathbf{V_{6}}\) = [\(-\)1, 2] & \(\mathbf{V_{6}}\) = [\(-\)1, 1] & \(\mathbf{V_{5}}\) = [\(-\)1, 2] \\ \hline \end{tabular}
\end{table}
Table 4: The distortion vector table for concealing 7-ary secret messages.  
\begin{table}
\begin{tabular}{c c c} \hline  & \(\mathbf{W_{b}}\) = [1, 8] & \(\mathbf{W_{b}}\) = [2, 7] & \(\mathbf{W_{b}}\) = [4,5] \\ \hline d\({}_{9}\) & \(V_{\text{d}}\), & \(V_{\text{d}}\), & \(V_{\text{d}}\), \\
1 & \(\mathbf{V_{1}}\) = [1, 0] & \(\mathbf{V_{1}}\) = [\(-\)2, 2] & \(dual-pixel. Note that it is not necessary to restore the secret image in order to extract the message vector, \(\mathbf{d_{du}}\).  
Next, in Phase-2, the pixels in dual stego images are employed to restore the stego pixel, \(\mathbf{RP^{\prime\prime}_{ouect}}\), using a simple, yet efficient addition and floor function, as shown in Equation (12). Next, the recipient who holds the secret key, \(key_{2}\), can independently extract the \(m\)-ary secret message, \(d_{um}\), using a 3-tuple weight vector, \(\mathbf{W_{a}}=[w_{1},\,w_{2},\,w_{3}]\). Finally, the \(n\)-ary secret message vector, \(\mathbf{d_{sb}}=(d_{u}^{1},\,d_{u}^{2},\,d_{u}^{3})\), is extracted by the legitimate recipient who holds the secret key, \(key_{1}\). We now detail each phase as the following.
# On Noise Modeling of Capacitive Feedback Transimpedance Amplifiers in CMOS
###### Abstract  
The work reports on the development of a detailed noise current model for a low-noise capacitive feedback transimpedance amplifier (TIA) in CMOS. The proposed TIA circuit implements the programmable-gain using an array of discretely controlled feedback capacitors and resistances in the biasing circuit and is originally designed bearing in mind low-noise requirements for optical time-domain reflectometer (OTDR) applications with the base gain of 10 k\(\Omega\) at 1 GHz bandwidth and noise levels below 5.0 pA\(/\sqrt{\mathrm{Hz}}\). The newly developed model for input-referred noise current spectral density complements the previously suggested transimpedance gain model and takes into account both the primary and secondary noise sources so far ignored in the models known in the literature. The proposed noise model consists of five terms and includes the effects caused by biasing components of the input stage and the noise shaping from the source follower. The performance of the developed noise model is evaluated using the post-layout simulation in 0.18 \(\upmu\)m CMOS and 0.25 \(\upmu\)m BiCMOS technologies, and a close match of the proposed model is demonstrated in the results of the post-layout simulation with the noise level below 1.8 pA\(/\sqrt{\mathrm{Hz}}\) for the base gain configuration in CMOS. A comparison to available noise models from the literature confirms that previously known noise models for this promising TIA architecture omitted important noise components present in practical and physically realizable circuits and, therefore, resulted in underestimating the base noise level by a factor of two to three, while completely ignoring the flicker noise mapping in the low-frequency range.  
capative feedback; CMOS integrated circuits; low-noise amplifiers; noise modeling; optical time-domain reflectometry; transimpedance amplifiers +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://doi.org/10.3390/appl21910186](https://doi.org/10.3390/appl21910186)]([https://doi.org/10.3390/appl21910186](https://doi.org/10.3390/appl21910186))
# Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement
## 4 Experimental Results and Analysis
### Experimental Setup  
To analyze the performance on different image data, the following four databases are used.  
(1) The BioID Face Database consists of 1521 gray level images with a resolution of 384 \(\times\) 286 pixels. Each of the 23 different tested persons shows the frontal view of the face. All of images are extracted from face video with different backgrounds.  
(2) The IMM Face Database comprises of 240 still images with resolutions of 640 \(\times\) 480 pixels. Each of the 40 individuals presents variations of head pose and expression. The background variation is not significant and the illumination in the whole image is not even.  
These two databases are conducted under controlled conditions and with a simple background. The former is more useful to test the robustness of the algorithm to illumination variations and expressions, whereas the latter is more suitable to see the influence of image resolution in face alignment. In this paper, the images from these two databases are divided into two parts: 1/4 images comprised the training set and 3/4 comprised the test set.  
(3) The LFPW Face Database consists of 1035 face images, 811 images comprised the training set and 224 comprised the test set.  
(4) The HELEN Face Database consists of 2330 face images with a higher resolution, 2000 images comprised the training set and 330 comprised the test set.  
The images of these two databases are from the internet, with no control and great variations in illumination, occlusion and expressions.  
It is easy to encounter overfitting problems with a small amount of training data for the CNN networks used in this paper. Therefore, a widely used data augmentation technique is performed to obtain more training images. The additional images are artificially created from the existing images by using image translation and cropping. Finally the training image volume is significantly increased.  
As a preparation task, the landmark positions in all sample images are obtained, and a set of landmark coordinates on each image form a face shape vector. The following quantitative evaluation indicators were used to measure the alignment accuracy on the test samples.  
(1) Normalized error: The error distribution of all images can be obtained from mean and max error of all landmarks in each image. To remove the influence of the image size, the normalized error is defined as \((p_{i}-g_{i})/d\), where \(i\) indicates the index of landmark, \(p_{i}\) and \(g_{i}\) respectively indicates landmark position automatically aligned and manually labeled, \(d\) indicates the distance between eyes in front face image or the size of bounding box.  
(2) Success rate: In the alignment process, if the algorithms cannot converge when the iterations reach the max or the error between the convergence results and the labeled results is greater than a certain threshold (like 5%), it can be concluded that the alignment on this landmark failed. The proportion of the landmarks which were successfully aligned reflects the algorithm performance.