esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 5 Experimental Results and Discussion
### Experimental Results
#### 5.1.3 Effects of the SF  
We present in Figure 15 the \(PDR\) for a 17 bytes LoRa transmitted packet at different \(SFs\) in different positions for a fixed bandwidth of 125 KHz and \(CR=4/5\) operating in the 433 MHz and 868 MHz bands. As seen from the figure, the higher the SF for a given CR and BW value, the higher the \(PDR\). It is well known that the choice of SF provides a compromise between range and data rate. Higher SF values provide a better range at the cost of a lower achievable data rate, as demonstrated in Figure 15. As expected, 433 MHz propagation is better in the same conditions. This is due to its lower radio propagation losses in comparison to the 868 MHz frequency.  
The worst connectivity was achieved with POS 3 and POS F at a distance of 860 m and 2050 m from their respective locations to the gateway. Even though POS 3 has a shorter distance compared to POS F, POS 3 is placed at a very dense forest vegetation. Whilst these experiments suggest that optimistically a range of 860 m was achievable, in practice, few packets were successfully delivered. Similar performance was observed at POS 4, where no packets were successfully delivered to the gateway from this location, regardless of the \(SF\) and \(CR\) values. The best connectivity was achieved with POS 1, POS A and POS B, where 100% \(PDR\) was achieved. From the figures presented in Figure 15, and using POS C and POS 3 as an example. POS C is placed at a distance of 1080 m to the gateway, while POS 3 is at a distance of 860 m. The PDR achieved in POS 3 surpasses the \(PDR\) achieved in POS 3, despite the fact that POS 3 is closer to the gateway. This shows the position differences, which indicated that POS C is placed in a not so dense forest, while POS 3 is placed in a very dense forest environment.  
Figure 14: RSSI registered between the GW and POS 2 for different SF and CR values.  
Figure 16 shows the average \(SNR\) in function of the SF for POS 1-POS 3 in the 433 MHz and 868 MHz band. We can draw up the following remarks: the experiments performed in POS 1 benefit from a high quality of reception due to their distance to the gateway. There is a significant drop in the \(SNR\) in POS 2 and POS 3, mainly due to the presence of vegetation as well as distance. In the case of POS 3 in the 868 MHz, connectivity was only achieved with \(SF11\) and \(SF12\). As expected, we obtained a higher \(SNR\) in the 433 MHz band, due to the lower radio propagation losses even in the worst gateway location.  
Figure 15: PDR achieved for transmissions from all positions for different SF values, with CR = 4/5, BW = 125 KHz in the 433 MHz and 868 MHz frequency bands.
# Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification
## 3 Materials and Methods
### Feature Extraction
#### 3.3.3 Fast Fourier Transform  
The FFT is used to transform a fingerprint image into the frequency domain. The transformed vector contains most of the information in the spatial domain and is used for gender classification. It is presented in Equation (3), where M and N represent the height and width of the fingerprint image, k and 1 represent frequency variables, respectively. 0 \(\leq\) m, k \(\leq\) M\(-\)1, 0 \(\leq\) n, 1 \(\leq\) N\(-\)1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Network** & **Details** \\ \hline
**DWT** & Goes through 8 levels of decomposition \\
**SVD** & Image vector in size 256 \\
**FFT** & Two-dimensional FFT used. \\
**VGG** & 8 blocks applied and each block contains 2 convolutional layers and a batch of normalization layers \\
**ResNet** & Each block contains a residual block \\
**DDC-ResNet** & Replacing the normal convolutional layers with Dilated Convolutional layers in ResNet \\ \hline \hline \end{tabular}
\end{table}
Table 1: Encoder details.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Network** & **Details** \\ \hline
**Adaboost** & \(n\_estimator=100\), \(learning\ rate=0.01\), \(max\_depth=7\), and \(subsample=1.0\) \\
**SVM 1** & The linear kernel is utilized with the function LinearSVC in the default configuration \\
**SVM 2** & \(C=100\) is set when applying RBF kernel \\
**SVM 3** & \(gamma=0.5\) is set when applying polynomial kernel \\
**kNN** & \(k=1\) \\
**J48** & conducted in WEKA software with the default settings [20] \\
**CNN** & The CNN classifier is comprised of 3 fully connected layers with the leaky relu activation \\
**ID3** & \(criterion=\textit{`entropy'}\) and \(splitter=\textit{`best'}\) \\
**LDA** & \(solver=\textit{`sud'}\) and \(tol=1.0e-4\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classifier details.  
\[F[k,l]=\frac{1}{\sqrt{MN}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}f[m,n]e^{-2\pi(\frac{m}{M}+ \frac{m}{N})} \tag{3}\]
## 4 Discussion
### Study Limitations and Future Research  
The methodology applied in this study contributes to the analysis of landslide susceptibility in the study area (although it could also be evaluated in other areas), since, from the early stages of the research, it has been sought to find the ML model with the best predictive results, as well as the best combination of conditioning factors that affect such predictive capacity; however, some limitations must still be considered, such as the scarcity of quality data, especially based on official information. In this context, the models run do not consider important parameters such as precipitation levels in the area, deep vegetation aspects or exhaustive land cover details, precisely because of the scarcity of reliable data. Precipitation and meteorological data could be resolved using freely available datasets, such as TRMM or CHIRPS (for precipitation), considering their characteristics to make them useful in the study. Vegetation or land cover data could be modeled using satellite image processing analysis to calculate vegetation or land cover indices. Moreover, although validation metrics reflect a good predictive capability of the model, uncertainty should always be taken into account as part of this type of analysis.  
As for future research, the model could be implemented with XGBoost but considering additional hyperparameters, as well as emphasizing the application of hyperparameter optimization algorithms to obtain a new approach that improves the values obtained with the trial-and-error method and, therefore, increases the accuracy of the model. It is also essential to apply new models focused on deep learning, such as convolutional neural networks (CNNs), since deep learning increases the degree of automation and recognition of complex entities in large datasets [78] and, therefore, improves predictive ability. In addition, models could be improved by assessing model sensitivity and robustness by applying extended cross-validation to ensure generalizability of results; investigating interactions between demographic variables and other predictors; evaluating the practical effectiveness of models through case studies; or developing hybrid models by integrating additional ML techniques.  
It is also important to consider the practical implications of the research conducted in the study area, especially in high and very high susceptibility zones, as they would be useful for generating preventive and emergency protocols, territorial planning and habitability regulation plans for these sectors. All this is with the objective of reducing the risk associated to landslides, generating a prevention vision and increasing the resilience of people, property and infrastructures.
# Quadratic Curve Fitting-Based Image Edge Line Segment Detection: A Novel Methodology
## 2 Derivation and Parameter Solving of the Edge Line Segment Model
### Solution of Edge Line Segment Model Parameters  
From the derivation of Section 2.1, it is known that due to the distortion, each edge segment to be extracted in the actual image can be regarded as a quadratic curve, and the coefficients of the quadratic curve, i.e., the parameters of the image edge segment model, are calculated in this section.  
Assuming that the extracted edge line segment consists of n pixel points, denoted as \(\left\{(x_{d,j},y_{d,j})|j=1,2,3,...,n\right\}\), then it satisfies Equation (5):  
\[x_{d,j}^{2}+y_{d,j}^{2}+ex_{d,j}+fy_{d,j}+g=0(j=1,2,3,...,n) \tag{5}\]  
In order to obtain the exact coefficients e, f, and g of the quadratic curve, the error function is defined by (6):  
\[F=\sum_{n}^{j=1}(x_{d,j}^{2}+y_{d,j}^{2}+ex_{d,j}+fy_{d,j}+g)^{2} \tag{6}\]  
By solving the first-order partial derivative equation of Equation (6), i.e.,  
\[\frac{\partial F}{\partial e}=0;\frac{\partial F}{\partial f}=0;\frac{\partial F }{\partial g}=0 \tag{7}\]  
we can obtain the optimal solution of Equation (6). Then, by expanding and combining the like terms of Equation (7), we can have a matrix form as follows:  
\[\underbrace{\left[\begin{array}{cc}\sum_{j=1}^{n}x_{d,j}^{2}&\sum_{j=1}^{n} x_{d,j}y_{d,j}&\sum_{j=1}^{n}x_{d,j}\\ \sum_{j=1}^{n}x_{d,j}y_{d,j}&\sum_{j=1}^{n}y_{d,j}^{2}&\sum_{j=1}^{n}y_{d,j} \\ \sum_{j=1}^{n}x_{d,j}&\sum_{j=1}^{n}y_{d,j}&n\end{array}\right]}_{A}\underbrace{ \left[\begin{array}{c}e\\ f\\ g\end{array}\right]}_{X}= \tag{8}\] \[\underbrace{\left[\begin{array}{c}-\sum_{j=1}^{n}(x_{d,j}^{2}+ y_{d,j}^{2})x_{d,j}\\ -\sum_{j=1}^{n}(x_{d,j}^{2}+y_{d,j}^{2})y_{d,j}\\ -\sum_{j=1}^{n}(x_{d,j}^{2}+y_{d,j}^{2})\end{array}\right]}_{B}\Rightarrow AX =B\]  
The matrix containing the quadratic curve coefficients can be obtained by solving Equation (8) using the least squares method:  
\[X=(A^{T}A)^{-1}A^{T}B \tag{9}\]
## 3 Results and Discussion
### Performance Test
#### 3.2.3 Oil Rejection Efficiency  
From Figure 8, the oily wastewater rejection rate of both membranes with 1.0 phr and 1.5 phr sulfur increases from 91.67% to 92.23% and 89.2% to 91.46% respectively, as the operating pressure increases. As the pressure increases from 0.5 to 1 bar, oil droplets start to accumulate on the membrane surface and act as a barrier against passage of oil droplets. Increase in oil rejection upon application of pressure was also reported in previous works but the relationship is not proportional [60].  
Figure 7: Permeation flux of the membrane.  
The membrane with higher sulfur loading exhibits lower oil rejection efficiency at all pressures. This is due to the cracks found on the surface morphology of the membrane that allows oil particles to seep through the permeate stream to decrease the oil rejection.  
In conclusion, oil rejection efficiency of the membrane with 1.0 phr of sulfur is more preferable as compared to the membranes with 0.5 and 1.5 phr of sulfur. Global oil discharge limits in wastewater after treatment have been well-established and the membrane that is expected to be developed needs to achieve the requirement of the global oil discharge limits. According to the environmental regulations that administer the discharge of oily wastewater, the limit on the maximum oil concentration falls generally within 5-100 mg/L [4]. According to the Environmental Protection Agency (EPA) from the United States, the maximum oil concentration of 72 mg/L for any 24 h period is acceptable [60]. Hence, both membranes from this research fulfill the requirement as the discharged oily wastewater through both membranes falls only in the range of 49.57 to 59.90 mg/L in 24 h.
## 7 Conclusions  
Transmitting power consumption levels of individual customers to an electricity supplier or utility over short intervals has advantages for supplier planning and management purposes in smart grids. However, it threatens user privacy by disclosing fine-grained consumption data and usage behavior to the supplier. In this study, we proposed a novel smart metering scheme that features a mechanism that (1) preserves user privacy using symmetric homomorphic encryption so that only the data collection unit can decrypt the aggregated metering data without knowing individual meter measurements; and (2) enables the collection unit to verify the integrity of aggregated data and authenticate the identities of the smart meters participating in the aggregation by exploiting the aggregate signature. Thus, the proposed scheme provides a secure metering solution for smart grids against passive and active attacks. The proposed scheme is also efficient in terms of computation due to symmetric encryption. Because the aggregate signature is constant in size regardless of the number of smart meters in a neighborhood, the additional communication overhead for the signature is also alleviated.
## 2 Materials and Methods
### Machine Learning Methods
#### 2.3.2 Support Vector Machine (SVM)  
The SVM algorithm is normally introduced as a supervised learning model, and it has been widely used to deal with classification and regression problems [48; 49]. It has also been utilized for prediction [50]. In general, the SVM model aims to generate the best separation hyperplane that can linearly divide classes (Figure S2a) [51]. This modeling process contains the definition of a certain linear function that works to determine a decision boundary for the highest margin, which is illustrated as \(2/\left\|\omega\right\|\) in Figure S2a. However, certain minor noisy data must be accepted (Figure S2b) because disregarding them can oversimplify the modeling process and induce limited influence on model performance. The current study adopted SVM to estimate \(\text{ET}_{a}\) because of its high efficiency and reliable output. Further details regarding SVM can be found in the literature [52]. The commonly used radial basis function, a nonlinear kernel function, was adopted in this study because it exhibits better performance in evapotranspiration estimation than other kernel functions [53].
# 2D Dielectric Nanoimprinted PMMA Pillars on Metallo-Dielectric Films
###### Abstract  
In this work, we propose an optimized nanoimprint protocol for the fabrication of a two-dimensional (2D) array of polymethyl-methacrylate (PMMA) nano-pillars deposited on different sputtered configurations (bilayer and multi-layer) of copper (Cu) and aluminum nitride (AlN) slabs supported by a silicon dioxide (SiO\({}_{2}\)) substrate. Both the Cu/AlN bilayer and multilayer thin films were deposited by a sputtering technique. The sub-micron PMMA pillars were realized by using nanoimprint lithography (NIL). In order to optimize the NIL process, several tests were performed by varying temperature and pressure, allowing us to achieve uniform and high-resolution pillars. The fabricated periodic array enabled the phase-matching of the incident plane wave exciting optical resonances. All the fabricated devices were then optically characterized by means of an ad hoc setup, where the reflected light from the sample was analyzed. The fabricated nano-pillars are mechanically stable, and they could be fully exploited for the realization of novel metallo-dielectric core/shell structures for sensing, surface-enhanced Raman spectroscopy, and light-matter interactions.  
Keywords:NIL; thin films deposition; optical absorbers; diffraction grating; guided-mode resonance +
Footnote †: journal: Applied
# Augmented Reality for PCB Component Identification and Localization
## 2 Literature Review and Technical Framework
Research indicates that AR enhances digital twins and smart factory ecosystems by providing real-time visualization, error detection, and context-aware instructions. For example, a study by [PERSON] et al. [18] discusses the role of digital twins in Industry 4.0, emphasizing their application in various sectors to optimize production and control systems. Similarly, [PERSON] et al. [19] provide an overview of emergent trends in industrial augmented reality, highlighting its integration into manufacturing processes. In the realm of maintenance and remote assistance, [PERSON] et al. [20] conducted a systematic review of AR applications, underscoring AR's value in improving maintenance operations and remote support. These insights are particularly relevant for printed circuit board (PCB) diagnostics and repair workflows, where precision and efficiency are paramount. Despite these advantages, challenges persist. Integrating AR with existing workflows presents ergonomic constraints and issues, such as marker instability under varied conditions. Usability also varies depending on user expertise and task complexity, making empirical evaluations essential for optimizing AR deployment in real-world manufacturing contexts. Building on these studies, this research explores the performance of AR in real-world PCB component identification tasks using HoloLens(tm 2).
# Hash Based DNA Computing Algorithm for Image Encryption
## 3 Methods
### Content-Aware Encryption Module  
The content-aware encryption enhancing module is designed to reorganize the data structure by highly non-linear functions, originating from correlations between adjacent pixels and patches. It is noted that DNA operations could vary to form more diversified expressions, thus greatly improving the variability of encrypted sequences. Moreover, the inherent complexity of DNA operations can be enhanced by utilizing the relevance between neighboring pixels, borrowing non-linear equations from neighboring pixels and patches. Therefore, the proposed module is designed to naturally involve the complexity of the image, greatly boosting the difficulty of the cracking. We demonstrate the structure design of the proposed module in Figure 4, where the DNA-encoded sequence and rule selection sequence are regarded as inputs for the module. Essentially, we design a reversible permutation and diffusion algorithm to combine both forms of complexity for better unbreakability. Both algorithms could be directly applied on the input image without additional transmission cost via Internet or LAN. Furthermore, we have designed several computation operations that satisfy the commutative law, namely ADD, SUB and XOR, which not only ensures the variance of DNA sequences, but also shares the same parameters to reduce unnecessary transmission costs in LAN. Specifically, we describe the calculation process of the DNA Sequence Permutation algorithm in Algorithm 3, which is built on the following four parameters:  
\[\left\{ \begin{array}{l}\frac{dx_{1}}{dt}=x_{n+1}=a(y_{n}-x_{n})+w_{n}\\ \frac{dy_{1}}{dt}=cx_{n}-y_{n}-x_{n}z_{n}\\ \frac{dz_{1}}{dt}=x_{n}y_{n}-b_{n}z_{n}\\ \frac{dw_{1}}{dt}=-y_{n}z_{n}+\gamma w_{n}\end{array} \right. \tag{2}\]  
where \(a\), \(b\), \(c\) and \(\gamma\) are preset parameters equaling 10, 3/8, 28 and \([-1.52,-0.06]\), respectively. It is noted that such a calculation process ensures the chaos property of theproposed module. Afterwards, four parameters are sent into the Hyper Chaotic Lorenz System (HCLS) to generate the permutation control sequence \(Q_{p}\), where HCLS is a highly nonlinear dynamical system, being sensitive to the initial values for unpredictability.
# Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview
## 4 Defense
### Classification from the Content of Defense  
In the process of defense, we can process the data in two stages before and after the input to achieve the effect of protection:  
Data preprocessing: The data modification is that which modifies the training set in the training stage or the input data in the test stage through adversarial training, gradient hiding, transferability blocking, audio data compression [89], data randomization, etc. The adversarial example passes through the system after preprocessing and achieves the correct result to achieve an invalid attack of the target system.  
Model modified: Modification of the network model is performed, which adjusts the target model directly to increase its robustness. Commonly used methods are through regularization [46], audio squeezing [59], or audio turbulence [19]. By modifying the network model, the protected system can be more robust, making the system less damaged by adversarial attacks. Studies such as [19; 46] have also achieved more significant results in this regard.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Year** & **Defense Method** & **Task** & **System** & **Adversarial Example** \\ \hline
[59] & 2018 & Temporal dependency & Detecting & ASR & Genetic algorithm [13]/FGSM/ \\  & 2019 & Adversarial regularization & Defense & ASV & FGSM/LDS \\
[61] & 2019 & MVP-\(E_{ASR}\) & Detecting & ASR & FGSM \\
[62] & 2019 & Audio modification & Detecting & ASR & Carlini and Wagner Attacks [4] \\
[90] & 2020 & Adversarial training/Spatial smoothing & Defense & ASV & Projected Gradient Descent Method [91] \\
[92] & 2020 & Self-attention U-Net & Defense & ASR & FGSM/Evolutionary optimization [93] \\
[94] & 2021 & Hybrid adversarial training & Defense & SRS & FGSM \\
[95] & 2021 & Audio transformation & Detecting & ASR & Adaptive attack algorithm [95] \\
[96] & 2021 & Self-supervised learning model [97] & Defense & ASV & BIM \\ \hline \hline \end{tabular}
\end{table}
Table 2: The taxonomy of defense in speaker and speech recognition. Task’ indices whether the defense method is completely defense or detection. An adversarial example is the generating method of the attack method.  
Figure 3: The general method of defense including filtering, detect–reject, retrain.
## 3 Results
### Hemoglobin  
#### Oxyhemoglobin (HbO)  
The left HbO levels were \(-\)0.004 \(\pm\) 0.015, 0.001 \(\pm\) 0.019, 0.017 \(\pm\) 0.020, and 0.028 \(\pm\) 0.024 in C1-C4, respectively (Table 2). The values showed an increasing pattern in the order of C1 to C4. The comparisons among conditions revealed significant differences for C1 vs. C3, C1 vs. C4, C2 vs. C3, C2 vs. C4, and C3 vs. C4 (\(p\) < 0.001) but not for C1 vs. C2 (\(p\) = 0.061).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Region** & **Condition** & **Mean \(\pm\) SD** & **F** & \(p\) & **Factor 1** & **Factor 2** &
\begin{tabular}{c} **Mean Difference \(\pm\) SE** \\ **(Factor 1-Factor 2)** \\ \end{tabular} & **t** & \(p_{\text{Thkey}}\) \\ \hline \multirow{4}{*}{Left} & \#1 & 49.47 \(\pm\) 5.84 & & & \#1 & \#2 & \(-\)0.92 \(\pm\) 0.41 & \(-\)2.24 & 0.143 \\ \cline{3-10}  & \#2 & 50.39 \(\pm\) 5.85 & & & & \#1 & \#3 & \(-\)2.85 \(\pm\) 0.51 & \(-\)5.63 & \(<\)0.001 * \\ \cline{3-10}  & \#3 & 52.32 \(\pm\) 6.01 & & & & \#4 & \(-\)3.63 \(\pm\) 0.41 & \(-\)8.85 & \(<\)0.001 * \\ \cline{3-10}  & \#4 & 53.10 \(\pm\) 5.78 & & & & \#2 & \#3 & \(-\)1.93 \(\pm\) 0.32 & \(-\)5.98 & \(<\)0.001 * \\ \cline{3-10}  & & & & & & \#4 & \(-\)2.71 \(\pm\) 0.33 & \(-\)8.14 & \(<\)0.001 * \\ \cline{3-10}  & & & & & & \#3 & \(-\)44 & \(-\)0.78 \(\pm\) 0.30 & \(-\)2.59 & 0.072 \\ \hline \multirow{4}{*}{Right} & \#1 & 50.51 \(\pm\) 9.95 & & & & \#2 & \(-\)1.58 \(\pm\) 0.48 & \(-\)3.25 & 0.016 * \\ \cline{3-10}  & \#2 & 52.08 \(\pm\) 9.86 & & & & \#1 & \#3 & \(-\)4.21 \(\pm\) 0.84 & \(-\)5.02 & \(<\)0.001 * \\ \cline{3-10}  & \#3 & 54.72 \(\pm\) 8.44 & & & & \#4 & \(-\)5.14 \(\pm\) 1.04 & \(-\)4.92 & \(<\)0.001 * \\ \cline{3-10}  & \#4 & 55.64 \(\pm\) 8.20 & & & & \#3 & \(-\)2.64 \(\pm\) 0.83 & \(-\)3.18 & 0.019 * \\ \cline{3-10}  & & & & & & \#2 & \#4 & \(-\)3.56 \(\pm\) 1.03 & \(-\)3.44 & 0.010 * \\ \cline{3-10}  & & & & & & \#3 & \(-\)44 & \(-\)0.92 \(\pm\) 0.55 & \(-\)1.68 & 0.355 \\ \cline{3-10}  & & & & & & & & & \\ \hline \end{tabular} Abbreviations: COS, cerebral oxygen saturation; RM ANOVA, repeated-measures analysis of variance; F, F, statistic; SD, standard deviation; SE, standard error; t, t-statistic. * \(p\) < 0.05.  
\end{table}
Table 1: Comparison of COS in the left and right frontal lobes using RM ANOVA.  
The right HbO values increased from \(-\)0.005 \(\pm\) 0.011 in C1 to 0.001 \(\pm\) 0.013 in C2, 0.017 \(\pm\) 0.019 in C3, and 0.025 \(\pm\) 0.026 in C4 (Table 2) and showed an increasing pattern in the order of C1 to C4, with significant differences among all conditions (\(p\) < 0.05).