esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
## 3 Results and Discussion
### Model Calibration  
#### 3.3.1 Single Band  
The values for the Pearson correlation coefficient (Figure 6) between the reflectance values and the P, K, Ca and Zn content in the leaves (Figure 5) show the wavelengths with the highest correlation and that were later selected to construct the empirical models for estimating the nutrient content in the leaves of _Vigna unguiculata_ (L.[PERSON] (Figure 6).  
Figure 6: Correlogram between reflectance and the P, K, Ca and Zn content in leaves of _Vigna unguiculata_ (L.) [PERSON].  
It can be seen that the values of the correlation coefficients for Zn were not sufficient for any wavelength on the spectrum. It was, therefore, not possible to create an empirical model for estimating this nutrient using a single band.  
An analysis of Figure 6 shows the wavelengths for which there was a greater correlation between the leaf content of each element and the reflectance factors. Table 2 shows the selected wavelengths together with the calibration and validation equations and R\({}^{2}\) values.  
As discussed in Section 3.2 above, the visible region is strongly influenced by the pigments present in the leaf, especially chlorophyll. Near 700 nm, there is a point known as the red edge, which is the limit of the chlorophyll absorption process, and where scattering begins in the internal structures of the leaves [68]. Chu [69] show that the band from 650 to 750 nm (red edge) is essential for understanding reflectance based on biochemical changes in the plant. In creating the models, at least one wavelength was selected from each of these two regions (visible and red edge), as they are somehow related to chlorophyll.  
Researchers studying the estimation of the nutrient content of leaves using single-band models had good results when selecting wavelengths located in the visible region [70; 71]. On the other hand, [56] found high negative correlations (R\({}^{2}=-0.7\)) between the phosphorus content and spectral reflectance, especially from 1400 to 1900 nm, which contradicts the result found in this study.
## 3 Results and Discussion
### Basic Composition of Raspberry Leaves
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Basic Composition**} & \multirow{2}{*}{**Culttivation**} & \multicolumn{2}{c}{**Conventional**} & \multicolumn{2}{c}{**Organic**} & \multicolumn{2}{c}{**Wild Crop**} \\ \cline{2-7}  & & **2020** & **2021** & **2020** & **2021** & **2020** & **2021** \\ \hline Water & 66.06 \(\pm\) 0.21 \({}^{\text{b}}\) & 66.05 \(\pm\) 0.01 \({}^{\text{b}}\) & 67.63 \(\pm\) 0.41 \({}^{\text{a}}\) & 68.37 \(\pm\) 0.10 \({}^{\text{a}}\) & 66.49 \(\pm\) 0.97 \({}^{\text{a}}\) & 60.16 \(\pm\) 0.24 \({}^{\text{c}}\) \\ \hline Total protein & 18.18 \(\pm\) 0.24 \({}^{\text{a}}\) & 12.85 \(\pm\) 0.25 \({}^{\text{b}}\) & 18.08 \(\pm\) 0.33 \({}^{\text{a}}\) & 14.98\(\pm\) 0.06 \({}^{\text{d}}\) & 17.90 \(\pm\) 0.17 \({}^{\text{a}}\) & 14.28 \(\pm\) 0.09 \({}^{\text{c}}\) \\ \hline Crude fat & 4.19 \(\pm\) 0.16 \({}^{\text{c}}\) & 4.22 \(\pm\) 0.18 \({}^{\text{c}}\) & 3.14 \(\pm\) 0.11 \({}^{\text{b}}\) & 3.32 \(\pm\) 0.03 \({}^{\text{b}}\) & 2.49 \(\pm\) 0.18 \({}^{\text{a}}\) & 2.30 \(\pm\) 0.12 \({}^{\text{a}}\) \\ \hline Ash & 9.05 \(\pm\) 0.32 \({}^{\text{e}}\) & 7.36 \(\pm\) 0.06 \({}^{\text{c}}\)d & 7.05 \(\pm\) 0.40 \({}^{\text{b},c}\) & 6.59 \(\pm\) 0.07 \({}^{\text{a},b}\) & 7.87 \(\pm\) 0.05 \({}^{\text{d}}\) & 6.07 \(\pm\) 0.23 \({}^{\text{a}}\) \\ \hline Dietary fibre & 21.39 \(\pm\) 0.34 \({}^{\text{b}}\) & 26.28 \(\pm\) 0.02 \({}^{\text{c}}\) & 20.09 \(\pm\) 0.33 \({}^{\text{a}}\) & 27.18 \(\pm\) 0.34 \({}^{\text{d}}\) & 28.84 \(\pm\) 0.41 \({}^{\text{e}}\) & 29.78 \(\pm\) 0.26 \({}^{\text{f}}\) \\ \hline Digestible carbohydrates & 68.57 \(\pm\) 0.08 \({}^{\text{b}}\) & 75.58 \(\pm\) 0.12 \({}^{\text{d}}\) & 71.74 \(\pm\) 0.04 \({}^{\text{a}}\) & 75.11 \(\pm\) 0.02 \({}^{\text{c}}\) & 71.74 \(\pm\) 0.30 \({}^{\text{a}}\) & 77.35 \(\pm\) 0.26 \({}^{\text{e}}\) \\ \hline \hline \end{tabular}  
* Results (mean \(\pm\) SD) obtained from the analysis of three individual samples (n \(\geq\) 3). Values within lines with different letters are significantly different (Duncan test \(p\leq 0.05\)).  
\end{table} 
Table 5: Basic composition in leaves of raspberries from conventional and organic practices as well as those wild grown (g 100 g\({}^{-1}\) DW). as opposed to the results obtained by [PERSON] and [PERSON] (2019) who estimated its amount at 58%, which was twice as high. The fat and ash content in the raspberry leaves of the cultivar _Clen Ample_, reported by [PERSON] and [PERSON] (2019), was close to our results.
# Salient Semantic Segmentation Based on RGB-D Camera for Robot Semantic Mapping
## 3 Salient Semantic Segmentation Methodology
### Semantic Segmentation of Salient Objects in RGB-D Images  
Pixel-level salient object segmentation is more precise than the semantic annotation bounding box's fuzzy location, which can accurately determine whether each pixel in an RGB-D image belongs to the salient object.  
(1) Firstly, we placed the salient object grayscale image and its corresponding RGB image in the same coordinate system.  
(2) Secondly, we projected the semantic information obtained from the grayscale image object recognition to the same place in the corresponding RGB image.  
(3) We manually adjusted the RGB three-channel pixel values into any consistent color where the pixel values' coordinates were consistent with the grayscale image's coordinates at the place where the salient object(s)' pixel values are greater than 200.  
The proposed salient object pixel-level semantic segmentation based on object recognition and segmented contour was simple and could be further converted to a point cloud semantic map.
## 3 Results and Discussion
### Effect of Contact Time  
The adsorption kinetics curve of chloride ion on the CAL-60 sample was shown in Figure 4. The influence of reaction time on the Cl\({}^{-}\) uptake on CAL-60 and nonlinear fits achieved using Pseudo-first-order model and Pseudo-second-order model (Temperature = 25 \({}^{\circ}\)C; CAL sample/NaCl solution = 10 g/L; initial chloride ion concentration and pH was set as 80 mmol/L and 7, respectively). Two adsorption kinetics models (Pseudo-first-order and Pseudo-second-order model) were used to fit the experimental results as follows:  
\[\ln(Q_{e}-Q_{t})=\ln Q_{e}-k_{1}t \tag{2}\]  
\[\frac{t}{Q_{t}}=\frac{1}{k_{2}Q_{e}^{2}}+\frac{t}{Q_{e}} \tag{3}\]  
where Q\({}_{t}\) (mg\(\cdot\)g\({}^{-1}\)) and Q\({}_{e}\) (mg\(\cdot\)g\({}^{-1}\)) are the Cl\({}^{-}\) uptake on CAL at t time and equilibrium, respectively; k\({}_{1}\) and k\({}_{2}\) are the kinetics correlation of Pseudo-first-order constant and rate correlation constant of Pseudo-second-order, respectively.  
It was observed that the absorbance of chloride ion by CAL rapidly increased at an initial 240 min, and then increased slowly at 500-3000 min reaching to equilibrium, with the maximum of 93.548 mg/g. This rapid adsorption (\(<\)240 min) involving LDHs was attributed with anion exchange reaction [31] (NO\({}_{3}\)\({}^{-}\) is replaced by Cl\({}^{-}\)) and the abundant adsorption sites of LDHs [32], while the adsorption capacity changed little with contact time during the equilibrium period (500-3000 min) due to the limited adsorption sites of LDHs.  
Table 1 showed a series of kinetics parameters calculated by the above two adsorption kinetics models. By comparing the correlation coefficients (R\({}^{2}\)) of above two kinetics models, it can be seen that the Pseudo-second-order model with R\({}^{2}\) of 0.948 described the experimental data of CAL-60 better, implying the occurrence of chemisorption, which was ascribed to a rate-limiting step [32; 33]. The adsorption capacity of Cl\({}^{-}\) on CAL-60 at equilibrium achieved by Pseudo-second-order kinetics model was 91.844 mg\(\cdot\)g\({}^{-1}\), which was close to the experimental data (93.548 mg\(\cdot\)g\({}^{-1}\)).  
Figure 4: The influence of reaction time on the Cl\({}^{-}\) uptake on CAL-60 and nonlinear fits achieved using Pseudo-first-order model and Pseudo-second-order model (Temperature = 25 \({}^{\circ}\)C; CAL sample/NaCl solution = 10 g/L; initial chloride ion concentration and pH was set as 80 mmol/L and 7, respectively).
# Influence of Degree of Saturation on Soil-Pile Interactions for Piles in Expansive Soils
## 4 Evaluation of Pile Uplift Force and Parameters
### Calculation of a Value  
Equation (10) was used to determine the shear stress at the interface between soil and pile in expansive soil. It takes into account the effects of matric suction and swelling pressure. The back-calculation of the \(\alpha\) value from unsaturated conditions can be expressed as follows:  
\[\alpha=\frac{\frac{p_{cy}}{\pi dl}-(u_{af}-u_{uf})tan\left[\phi^{\prime}\left( \frac{\theta-\theta_{c}}{\theta-\theta_{r}}\right)\right]}{\left(\sigma_{cy}^ {\prime\prime}\right)_{v}} \tag{15}\]  
The uplift coefficient (\(\alpha\)) is a key parameter to compute the pile uplift force in pile design for expansive soils. The back-calculated \(\alpha\) values for the samples tested at various degrees of saturation were summarized in Table 4. The back-calculated \(\alpha\) values vs. the initial degrees of saturation are plotted in Figure 11. Table 4 and Figure 11 indicate that the average back-calculated \(\alpha\) values range from 0.115 to 0.125, and the \(\alpha\) value increases as the initial degree of saturation increases. The \(\alpha\) value increased by approximately 9% within the degree of saturation values tested.  
Figure 11: Relationship between the \(\alpha\) value and initial degree of saturation.4.3. Calculation of \(\beta\) Value.  
Figure 10: Ultimate uplift shear stress versus the initial degree of saturation and initial degree of saturation.  
As discussed in Section 2, the swelling pressure ratio value, \(\beta\), can be determined as \(\beta=\alpha/tan(\phi^{\prime})\). Figure 12 plots the calculated \(\beta\) values vs. the initial degrees of saturation. In the calculations, the interface friction angle (\(\phi^{\prime}\)) was assumed to be a constant value of 11 degrees. Figure 12 indicates that the calculated average values range from 0.591 to 0.640, and the \(\beta\) value increases as the initial degree of saturation increases. The \(\beta\) value increased by approximately 8% within the degree of saturation values tested. The results of the \(\beta\) values align with findings by [PERSON] and [PERSON] (1994) [42]. This is attributed to a greater reduction in vertical swelling pressure than lateral swelling pressure as saturation increases, leading to a higher \(\beta\) value.  
For practical design applications, the use of constant \(\alpha\) and \(\beta\) values is justified within the tested range. However, further investigation into the effects of pile geometry and soil properties is recommended. Previously, [PERSON] et al. (2025) [43] investigated the effect of pile length on pile-soil interaction, whereas [PERSON] et al. (2022) [44] also considered factors such as pile diameter, soil type, and group pile orientation. Future research could incorporate all these parameters to study their combined effects on expansive soils.
## 6 High Frequency Characterization and Modelling  
Since monolithic integrated optoelectronic circuits are targeted to operate at terahertz frequencies in beyond 5G communication systems, it is crucial to analyze the performances of the electronic and photonic devices at a very high frequency of operation. The difficulties of this approach lie with the conventional test-structures and on-wafer characterization setup that often suffer from characterization technique, calibration and de-embedding techniques adapted to (sub) millimeter-wave frequency bands. The extrapolation of measurements to higher frequencies can often lead to errors and even the models might lack additional effects that can only be understood from actual measurements beyond 200 GHz. Hence, this section sheds light on this aspect through test structure design strategies, in order to correctly \"de-embed\" the real intrinsic device characteristics, as well as to demonstrate model validity in comparison with the on-wafer measurements performed at very high frequencies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dimensions.** & **Parameters** & **Model** & **Standard Deviation** & \(R_{TH}\) **(K/W)** \\ \hline \multirow{3}{*}{\(5\times 15\) μm\({}^{2}\)} & \(R(\Omega)\) & 60.58 & \(\pm 2\) & \\  & \(I_{s}\)(A) & \(4.6\times 10^{-12}\) & \(\pm 0.4\times 10^{-12}\) & 1013 \\  & \(N\) & 1.365 & \(\pm 0\) & \\ \hline \multirow{3}{*}{\(5\times 30\) μm\({}^{2}\)} & \(R(\Omega)\) & 36.31 & \(\pm 5\) & \\  & \(I_{s}\)(A) & \(8.77\times 10^{-12}\) & \(\pm 0.07\times 10^{-12}\) & 844 \\  & \(N\) & 1.363 & \(\pm 0\) & \\ \hline \multirow{3}{*}{\(7\times 25\) μm\({}^{2}\)} & \(R(\Omega)\) & 27.7 & \(\pm 2\) & \\  & \(I_{s}\)(A) & \(6.6\times 10^{-12}\) & \(\pm 0.1\times 10^{-12}\) & 612 \\ \cline{1-1}  & \(N\) & 1.360 & \(\pm 0.03\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermal parameter values according to the UTC-PD dimensions.
## 4 Experiments
### The Defensive Effect of the Poisoning Suppression Model against Backdoor Attacks  
We evaluate the performance of the poisoning suppression model in the first step of our proposed defense method. Table 6 shows the defensive effect of the poisoning suppression model. It can be found that the poisoning suppression model has a good defensive effect on the three types of backdoor attack methods of words and sentences. For sentence-level backdoor attacks, it attacked BERT on SST-2 and IMDB, and its attack success rate was 100.00%/100.00%. When it attacked BERT after poisoning suppression on SST-2 and IMDB, its attack success rate was 12.80%/3.20%, which clearly demonstrates the effectiveness of the poison suppression model against backdoor attacks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Defense Method**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{3-4}  & & **SST-2** & **IMDB** \\ \hline \multirow{3}{*}{BiLSTM} & No defense & 94.00\% & 96.00\% \\  & Our method & **0\%** & **2.00\%** \\  & ONION & 14.60\% & 20.80\% \\ \hline \multirow{3}{*}{BERT} & No defense & 100.00\% & 100.00\% \\  & Our method & **2.60\%** & **0\%** \\ \cline{1-1}  & ONION & 31.80\% & 29.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Defense performance against word-level backdoor attack (%).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Defense Method**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{3-4}  & & **SST-2** & **IMDB** \\ \hline \multirow{3}{*}{BiLSTM} & No defense & 100.00\% & 91.00\% \\  & Our method & 8.60\% & 0.80\% \\ \hline \multirow{3}{*}{BERT} & No defense & 100.00\% & 100.00\% \\  & Our method & 4.80\% & 0.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Defense performance against sentence-level backdoor attack (%).  
Table 7 shows the test accuracy of the poisoning suppression model and the original model. Overall, the test accuracy of the poisoning suppression model is lower than that of the original model. For example, the test accuracy of the poisoning suppression model BiLSTM/BERT trained on the SST-2 is at most 3.35%/2.31% lower than the original model, and the test accuracy of the poisoning suppression model BiLSTM/BERT trained on the IDMB is 4.84%/2.33% lower than the original model at most. Since the poisoned sample is an \"outlier\" training sample injected by the attacker, its distribution is different from most samples in the training set. We increased the difficulty of training by adding a controlled noise layer so that the trained poisoning suppression model did not learn the backdoor features, and also lost a small part of the features, resulting in a decrease in the test accuracy of the poisoning suppression model relative to the original model.  
Figure 3: Proportion of poisoned samples in suspect samples. (**a–d**) Character-level backdoor attack setting. (**e–h**) Word-level backdoor attack setting. (**i–l**) Sentence-level backdoor attack setting.
## 3 Results
### Ascending/Descending Orbits  
According to Table 1, SAOCOM-1A images with both ascending and descending orbits are available at the Corrientes site, comprising the period from September to December 2021. Nevertheless, the descending orbit presents a lower number of images as some images were not acquired due to conflicts with higher priority acquisitions. It is important to highlight that the ascending orbit had an incidence angle of 32\({}^{\circ}\), whereas the incidence angle of  
Figure 5: Forested-area, Corrientes site.  
Figure 6: Bare-soil area, Corrientes site.  
the descending pass was 19\({}^{\circ}\). The approximate footprint of this pass can be observed in Figure 7. As performed previously, we computed the temporal coherence matrices for the descending pass over region C, and compared them to the matrices of the ascending pass, see Figure 7(a),b.
# Privacy-Aware Access Protocols for MEC Applications in 5G +
## 3 Statement of the Problem
### Adversary Model  
We now define types of adversaries that are relevant to the system model described in Section 3.1. These adversary types are Dolev-Yao and honest-but-curious. The choice of these adversary types is justified in Section 3.2.1.  
A Dolev-Yao adversary can listen to the traffic on the network and interact with the other entities of the network [42]. This adversary has the ability to _\"overhear, intercept, and synthesize any message and is only limited by the constraints of the cryptographic methods used\"_[43]. Nevertheless, the Dolev-Yao adversary cannot crack encrypted messages but can only decrypt them if this adversary has the right key [44].  
An honest-but-curious adversary is defined as _\"a legitimate participant in a communication protocol who will not deviate from the defined protocol but will attempt to learn all possible information from legitimately received messages\"_[22]. The local information, e.g., messages in the network, is used to deduce private data without interfering with the traffic [45].  
The parties in our system model, MNO, MEC, and APPIFY, are assumed to be honest-but-curious adversaries. They follow the protocol, but still, they are trying to learn as much as possible about the user. Moreover, apart from being honest but curious, MNO, MEC, and APPIFY are passive Dolev-Yao adversaries: they may see all messages sent by other parties, but they do not actively interfere with these messages.  
Elements that are not visible in our system model may be Dolev-Yao adversaries. They can see, delete, add, replay, modify, reroute, and reorder the messages [46] since they do not need to be honest. MNO subscribers other than Alice may be malicious, having Dolev-Yao potential. We assume that MEC applications other than APPIFY are honest-but-curious,  
Figure 2: Elements of the system model.  
passive Dolev-Yao adversaries. All these three types of entities are included in the category of outsiders.
## 4 Conclusions  
In this study, we report on the important changes in the structure of FEP films resulting from the different treatment procedures that mimic extreme environmental conditions. Macromolecular orientation texture analysis of FEP films revealed changes in the crystalline component fraction and degree of anisotropy after exposure to heat, UV, and heat-UV treatments. The heat treatment of FEP at 200 \({}^{\circ}\)C affected its structural properties by rearranging the crystallites and resulting in the highest degree of anisotropy, with a \(D_{\mathrm{f}}\) value of 2.64. The UV treatment of FEP resulted in a polymer structure with the lowest degree of anisotropy (\(D_{\mathrm{f}}\) = 2.71). The increase in anisotropy of the FEP films also affected their mechanical properties, as well as differences in Young's modulus and yield stress. The stress concentration in less cohesive amorphous regions significantly decreased, as quantified by uniaxial microtensile testing results. Young's modulus and yield stress for UV light-treated FEP films were found to be highest with the obtained values of 651.88-665.52 MPa and 12.89-13.31 MPa, respectively. The structural changes that occurred were further quantified via laser ablation tests. The laser ablation threshold values were found to be lower for the heat-treated FEP polymer by a factor of \(\sim\)10.  
Conceptualization, A.L. and V.G.; methodology, B.A., A.L. and D.J.; validation, B.A. and D.J.; formal analysis, D.J.; investigation, B.A., A.L., V.G. and D.J.; resources, V.G.; data curation, B.A. and A.L.; writing--original draft preparation, B.A., A.L., V.G. and D.J.; writing--review and editing, D.J.; visualization, B.A. and A.L.; supervision, V.G.; project administration, V.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & & \(E\) **(MPa)** & _\(\sigma_{y0.2}\) **(MPa)** \\ \hline \multirow{3}{*}{Parallel to the FEP roll direction} & FEP-1 & 583.92 \(\pm\) 0.23 & 11.60 \(\pm\) 0.10 \\  & FEP-2 & 555.57 \(\pm\) 0.32 & 10.92 \(\pm\) 0.12 \\  & FEP-3 & 665.52 \(\pm\) 0.13 & 13.31 \(\pm\) 0.08 \\  & FEP-4 & 499.11 \(\pm\) 0.43 & 9.69 \(\pm\) 0.12 \\ \hline \multirow{3}{*}{Perpendicular to the FEP roll direction} & FEP-1 & 589.46 \(\pm\) 0.21 & 12.17 \(\pm\) 0.07 \\  & FEP-2 & 511.19 \(\pm\) 0.35 & 10.36 \(\pm\) 0.13 \\ \cline{1-1}  & FEP-3 & 651.88 \(\pm\) 0.12 & 12.89 \(\pm\) 0.07 \\ \cline{1-1}  & FEP-4 & 408.62 \(\pm\) 0.50 & 8.39 \(\pm\) 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Young’s modulus (_E_) and yield stress (\(\sigma_{y0.2}\)) values. The notation represents the “\(\pm\)” standard error of the mean.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 The Building, Temporary Instrumentation and Methodology
### Parametric Analysis Procedure
#### 2.3.5 Eigenvalues (\(\mathbf{\delta}\)) and Eigenvectors (\(\mathbf{\phi}\))  
The eigenvalues and eigenvectors are calculated from the discrete system matrices \(\mathbf{A}_{d}\) and \(\mathbf{C}_{d}\). The vector \(\mathbf{\delta}\) is used to calculate the identified frequencies of the system, and \(\mathbf{\phi}\) denotes the vibrational shapes corresponding to the abovementioned frequencies.