esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Parameter Settings  
1. Frame Count: We evaluate the impact of different F values on algorithm performance by varying F from 1 to 10 in increments of 1. We use ROC curves to compare the results of different parameter settings.
2. The trade-off parameter \(\lambda\) between the target and background is related to the image dimensions and is typically calculated using the H parameter in combination with the image size and frame count. We perform parameter analysis using different H values, adjusting H from 1 to 10 in increments of 1 during the experiments.
3. Rank \(r\) primarily describes the low-rank property of infrared images, and it determines the size of U so it affects the computational complexity. We analyze the impact of \(r\) on algorithm performance by varying \(r\) in increments of 1, with F set to 10 frames.
4. \(\alpha\): \(\alpha\) is used to balance the target and background. Typically, a lower \(\alpha\)-value enables the reconstruction of more detailed background information, but the target may also be retained in the background, leading to missed detections. A higher \(\alpha\)-value results in a coarser background, with many background details potentially preserved in the target image, causing false alarms.  
Based on Figure 7, we set the parameters as follows: \(\text{F}=10;\text{H}=4;\text{r}=6;\text{a}=0.1\).
# Laboratory Evaluation of Wear Particle Emissions and
## 3 Results
### Development of the TRWP Emission Estimation Equation Based on Suspended Dust Concentration  
#### 3.2.1 Selection of Variables  
As shown in Figure 9, TSP and TRWP emissions tend to increase according to the NMASs of the PSMA mixtures. Based on this relationship, an analysis was conducted to estimate the TRWP emissions accumulated at the bottom of the chamber, without directly collecting them, by utilizing the suspended dust concentration measured in real time through sensors.  
Figure 12 illustrates the relationship between the concentration of suspended dust by PM size and TRWP emissions in order to select independent variables for regression analysis. It can be seen that for PM\({}_{5}\) and PM\({}_{10}\), the TRWP emissions tend to increase when the suspended dust concentration increases. However, no specific tendency was found in the analysis results for the remaining PM sizes.  
The suspended dust concentrations for PM\({}_{0.3}\), PM\({}_{0.5}\), and PM\({}_{1}\) exhibited coefficients of determination (R\({}^{2}\)) of 0.5 or lower with the TRWP emissions (Table 4). As suspended dust particles with smaller particle sizes are greatly affected by turbulence, it can be deemed that the cycle of the suspension of dust particles generated inside the chamber and the settling of these dust particles is not stabilized, similar to the interpretation of Figure 9. On the other hand, the coefficients of determination for the relationship between the suspended dust concentrations for PM\({}_{2}\), PM\({}_{5}\), and PM\({}_{10}\) and TRWP emissions were 0.7 or higher, exhibiting a high explanatory power for interpreting TRWP emissions based on these concentrations.  
Figure 11: Relationship of PM\({}_{5}\) and PM\({}_{10}\) suspended dust concentrations in PSMA mixtures with maximum aggregate sizes.
## 3 Materials and Methods
To enhance interpretability, brief textual descriptions are provided for each stage within Figure 2, as follows:  
* **Feature aggregation:** Integrates multi-source inputs from environmental, economic, and social dimensions. * **SE-based recalibration:** Applies channel-wise re-weighting to emphasize relevant features dynamically. * **Multi-task output layer:** Predicts tourist arrivals, revenue, and accommodation demand simultaneously. * **Feedback loop:** Evaluates model outputs and triggers real-time adjustment when the accuracy drops. To complement the visual summary provided in Figure 2, the core architecture of SE-TFF consists of the following four interconnected components:  
1. **Subsystem feature extraction:** Each of the environmental, economic, and social subsystems undergoes a global information aggregation phase using the Squeeze [38] operation, which captures the overall state of each subsystem. Figure 2: The improved SE module framework. 2. **Adaptive recalibration with excitation:** The Excitation operation is applied to adaptively adjust the importance of each subsystem's features, recalibrating the channel weights based on their relevance to sustainable development. 3. **Dynamic multi-objective optimization:** A multi-objective optimization algorithm is integrated to optimize the interaction between the subsystems, considering the environmental, economic, and social objectives while respecting the system constraints. 4. **Model evaluation and feedback mechanism:** A dynamic monitoring and feedback mechanism is incorporated to evaluate the model's performance, allowing for real-time adjustments based on new data and ongoing model parameter optimization. To improve the transparency and reproducibility, we present the pseudo-code of the SE-Net component in Algorithm 1. This simplified representation outlines the channel-wise recalibration process used in the Squeeze-and-Excitation mechanism, highlighting how global feature aggregation and adaptive Weighting are jointly achieved across subsystems. ```
Input: 4D feature tensor set \(X\) Output: Feature map with channel-wise attention weights 01: function SE_Block(X, r):
02: \(\text{P}(t)\leftarrow\text{Aggregate}(X(t))\) # Combine subsystem features 03: \(S\leftarrow\text{GlobalAveragePooling}(P(t))\) # Channel-wise global stats 04: \(Z\leftarrow\text{ReLU}(W_{1}S+b_{1})\) # First excitation layer 05: \(A\leftarrow\text{sigmoid}(W_{2}Z+b_{2})\) # Two-layer gating network 06: \(X^{\prime}(t)\gets A\odot P(t)\) # Reweight original features 07: Return \(X^{\prime}(t)\) # Output recalibrated features
```  
**Algorithm 1**SE-Net computation pseudo-code.
## 2 Material and Methods
### Basic Mathematical Formulation Part
#### 2.3.3 Evaluation Metrics  
The primary energy demand in the heat pump case is found below [28]:  
\[\rm{PE_{HP}=2.9\cdot\int_{t=0}^{t=8760}P_{el}\;dt,\;in\;[kWh]} \tag{12}\]  
The primary energy demand in the boiler case is found below [28]:  
\[\rm{PE_{b}=1.1\cdot\int_{t=0}^{t=8760}Q_{b}\;dt,\;in\;[kWh]} \tag{13}\]  
The yearly CO2 emissions in the heat pump case are calculated below [31]:  
\[\rm{Y_{CO2,HP}=0.467\cdot\int_{t=0}^{t=8760}P_{el}\;dt,\;in\;[kg_{CO2}]} \tag{14}\]  
The yearly CO2 emissions in the boiler case are calculated below [28]:  
\[\rm{Y_{CO2,HP}=0.264\cdot\int_{t=0}^{t=8760}Q_{b}\;dt,\;in\;[kg_{CO2}]} \tag{15}\]  
The initial cost for the heat pump investment (C0) was estimated at EUR 4500 for Athens and EUR 6000 for Thessaloniki, which took into consideration a specific cost of about EUR 750 / kW\({}_{th}\)[32]. The electricity cost was estimated at \(\rm{k_{el}=EUR\;0.22/kWh}\) which is a reasonable value for the present situation in Greece. The lifetime of the project was estimated at (N = 25 years), and the discount factor was estimated at (r = 2%). The life cycle cost of the heat pump system can be calculated below:  
\[\rm{(LCC)}_{HP}=C_{0}+\sum_{j=1}^{N}\frac{k_{el}\cdot\int_{t=0}^{t=8760}P_{el} \;dt}{(1+r)^{j}},\;in\;[EUR] \tag{16}\]  
The cost of the oil was chosen at (EUR 1.4 /L), the oil's density at 0.86 kg/m\({}^{3}\), and the oil's lower heating value at 42,000 kJ/kg. Thus, the oil cost per energy (\(\rm{k_{oil}}\)) was calculated at EUR 0.1395 / kWh. The life cycle cost of the oil boiler system can be calculated below:  
\[\rm{(LCC)}_{b}=\sum_{j=1}^{N}\frac{k_{oil}\cdot\int_{t=0}^{t=8760}Q_{b}\;dt}{ (1+r)^{j}},\;in\;[EUR] \tag{17}\]  
The net savings on the present time (NS) are calculated as below:  
\[\rm{NS=(LCC)}_{b}-(LCC)_{HP},\;in\;[EUR] \tag{18}\]  
The simple payback period (SPP) of the investment of the heat pump system can be calculated as below:  
\[\rm{SPP=\frac{C_{0}}{k_{oil}\cdot\int_{t=0}^{t=8760}Q_{b}\;dt-k_{el}\cdot\int _{t=0}^{t=8760}P_{el}\;dt},\;in\;[years]} \tag{19}\]  
The mean yearly COP or seasonal COP of the heat pumps system (SCOP) is calculated below:  
\[\rm{SCOP=\frac{\int_{t=0}^{t=8760}Q_{heat}\;dt}{\int_{t=0}^{t=8760}P_{el}\;dt}} \tag{20}\]
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 4 Applications of Machine Learning in XRD Data Analysis
### Pattern Matching and Classification Algorithms  
[PERSON] et al. [91] applied support vector machine and deep learning methods (convolutional neural network) to extract image features from synchrotron data streams and compared the accuracy of their algorithm against synthetic and real datasets. Image features to be identified and classified belong to several groups (experiments, instrumentation, imaging, scattering features, samples, materials, and specific substances), as depicted in Table 1. The experimental dataset consisted of 2832 grayscale X-ray images and the dataset used to train the CNN consisted of 100,000 synthetic (simulated) images. In what concerns the deep learning algorithm, the basic units were a convolutional layer, a subsampling/pooling layer, and an activation layer based on ReLU (rectified linear unit) activation function. The mean average precision determined for the SVM algorithm on the synthetic dataset was  
Figure 2: Machine learning algorithms used in X-ray diffraction data analysis. 0.6705, compared with 0.771 for the CNN algorithm, which showed superior performance in this instance. The detection of synchrotron image features was also studied by [PERSON] et al. [92], which aimed to identify seven types of flaws: ice rings, diffuse scattering, background rings, nonuniform detector responses, loop scattering, strong background, and digital artefacts. The group compared several algorithms (SVM; naive Bayes--NB; k-nearest neighbors--KNNs; random forest--RF) with CNN, in which they used different inputs (cartesian coordinates and polar coordinates with different interpolation methods: _min_ and _max_). The dataset used in the study comprised 6311 diffraction images from the Integrated Resource for Reproducibility in Macromolecular Crystallography, from which 5048 were used as a training set, 631 were used as a validation set, and, subsequently, 632 were used in the testing set. The accuracy of class-specific predictive performance for the different classifier algorithms is shown in Table 2. Their results clearly show that CNN performance was consistently better than any other of the tested classifiers. Moreover, it is worth mentioning that the differences among the CNNs were generally approximately 0.01-0.02.
# An Updated Review: _Opuntia focus indica_ (OFI) Chemistry and Its Diverse Applications
## 3 Applications
### Uses of Opuntia in the Bioremediation of Wastewaters  
Due to the increase in global population and pollution problems, providing safe drinking water is becoming an increasingly important issue. For this reason, many methods have been studied for removing contaminants from wastewater, including irradiation, biosorption, deionization, flotation, coagulation, microfiltration, membrane filtration, oxidation, ion exchange, ozonation, and electrochemical treatment. The elimination of organic and inorganic pollutants from wastewater by biosorption using OFI has advantages due to its low cost, its great ability for pollutant binding, its quick elimination of pollutants, and the easy accessibility of material [75]. The seeds of OFI and Moringa Oleifera were used as bio-coagulants in comparison to Alum, removing 100% water turbidity at pH 7.5. The OFI coagulation-flocculation active molecules, especially polysaccharides, work as inter particles binding in polluted H\({}_{2}\)O treatment [76]. Kinetic and thermodynamic studies and equilibrium models are utilized to determine the interfaces between pollutants and biosorbents. OFI pads, both in their raw and chemically and physically treated forms, are useful for removing chemical oxygen demand (COD) dyes, pesticides, turbidness, negative-ions, and metal species from wastewater [75; 76; 77]. Except for the skin, various parts of OFI show coagulation action with Moringa Oleifera, which is useful for the elimination of turbidity in synthetic clay solutions, as reported by [PERSON] [77]. The turbidity is reduced by 92-99 percent with a natural coagulant. The presence of galacturonic acid and other active molecules in OFI makes it efficient for treating wastewater turbidity. Galactose, arabinose, and rhamne combined with galacturonic acid account for fifty percent of all coagulation processes, indicating that other components of OFI also contribute to the coagulation process [74; 77]. The biocoagulation-flocculation process was utilized to remove heavy metals from contaminated natural samples collected from the Mukuvisi River in Zimbabwe under standard conditions with high OFI powder activity. The optimal conditions for the process were 35 \({}^{\circ}\)C, 5 pH, and a 180 min contact time. At standard conditions, even in the presence of other ions, Pb(II) is easily eliminated with OFI powder [78].
# Orthogonal Neural Network: An Analytical Model for Deep Learning
## 3 ONN Model Applied on Convolutional Neural Networks
### Extended CNN in ONN Model  
In orthogonal modulation of the input layer, a vector \(\boldsymbol{\alpha}\in\mathcal{A}\) is assigned to each pixel according to a certain rule. The grayscale image in this example is actually a two-dimensional matrix of \(\mathbb{R}^{28\times 28}\), so a total of \(28\times 28=784\) vectors \(\boldsymbol{\alpha}\in\mathcal{A}\) need to be assigned one by one for correspondence to the image pixels, and for the pixel point \(x_{i,j}\), the vector assigned is \(\boldsymbol{\alpha}_{i\times 28+j}\in\mathcal{A}\). Thus, the input layer after modulation is an \(E^{1\times 28\times 28}\) matrix. The details of the extended CNN in the ONN model are as follows.  
Input layer: \(1\times 28\times 28\), stored as a matrix of \(E^{1\times 28\times 28}\).  
Layer 1: convolution layer, convolution kernel size is \(5\times 5\), the output is six feature maps, sliding step \(\mathit{stride}=1\), so the convolution kernel is an \(E^{6\times 1\times 5\times 5}\) matrix and the output is an \(E^{6\times 24\times 24}\) matrix, and activation is by the ReLU activation function.  
Layer 2: pooling layer, using maximum pooling, the pooling window size is \(2\times 2\), sliding step \(\mathit{stride}=2\), and the output is an \(E^{6\times 12\times 12}\) matrix.  
Layer 3: convolution layer, convolution kernel size is \(5\times 5\), the output is 12 feature maps, sliding step \(\mathit{stride}=1\), so the convolution kernel is an \(E^{12\times 6\times 5\times 5}\) matrix and the output is an \(E^{12\times 8\times 8}\) matrix, and activation is by the ReLU activation function.  
Layer 4: pooling layer, using maximum pooling, the pooling window size is \(2\times 2\), sliding step \(\mathit{stride}=2\), and output is an \(E^{12\times 4\times 4}\) matrix.  
The last three layers are fully connected layers, spreading the \(E^{12\times 4\times 4}\) output of the fourth layer into a vector of \(E^{192}\). Each layer consists of 120, 60, and 10 neurons. The ReLU activation function is applied to the output of the first two layers. The first layer neuron output is a vector of \(E^{120}\), the second layer neuron output is a vector of \(E^{60}\), and the third layer output is a vector of \(E^{10}\), which is the basis for the final classification discriminations and the index of the largest value in the vector corresponds to the classification, i.e., the classification of the image with the input layer of \(1\times 28\times 28\).
# Fingerprint Based Codebook for RIS Passive Beamforming Training
###### Abstract  
In this article, we propose a new RIS passive beamforming scheme in two main stages. First, a fingerprint-based codebook (FP-CB) design phase occurs, where the area of interest is divided into a number of points and the optimal reflection patterns (RPs) corresponding to these points are determined and stored alongside the coordinates of these points in the codebook database (DB). Second, there is the searching and learning online stage, in which, based on the receiver (RX) and FP points' locations, the system determines a group of candidate RPs. Then, it just searches through them instead of examining the entire CB RPs to select the best RP that can be used for configuring RIS during the data transmission period. The proposed mechanism proves that designing a positioning information-based CB can highly reduce the system overhead computational complexity and enhance performance comparable to the conventional CB-based scheme and the full channel estimation (CE)-based scheme. For example, selecting only 10 candidate RPs from the FP-CB can obtain a better effective achievable rate than a CE-based scheme in a rapidly changing channel.  
reconfigurable intelligent surface; passive beamforming; fingerprint-based codebook; location information +
Footnote †: journal:  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)]([https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)) 4.0/).
# Epoxy/Glass Fiber Nanostructured p- and n-Type Thermoelectric Enabled Model Composite Interphases
## 3 Results
### Thermoelectric Response of the Model Composites  
In Table 1 the TE properties of both p-type and n-type bucky paper films are presented. The conductivity and the TE response introduced from the coating can be attributed to the nanoparticle-based networks deposited onto the fiber surfaces creating the electron transport pathways. In fact, the electrical conductivity and the Seebeck coefficient values can be explained more precisely if we consider the existence of a continuous self-assembled layer with thickness (t) onto the fiber surface with electrical conductivity (\(\sigma\)) stemming from the electrostatic interactions [34].  
More analytically, when the anionic surfactant (SDBS) is added to distilled water it tends to ionize and give a negative charge to the created micelles which include the dispersed de-aggregated SWCNTs. Conversely, the positively charged free silanol particles from the glass fibers surface interact with the ambient oxygen groups result in non-covalent bonding of the CNTs [35]. The resistivity (\(\rho\)) of an epoxy coated GF tow along the longitudinal direction is given by the formula: \(\rho=A\times R/L\) (\(A\): circular cross section area). Executing this equation, the coated GF tows with SWCNTs (p and n-type) consist of a highly conductive continuous network and could be assumed approximately as a tubular-like geometry resistor (\(V=\pi\times r2\times L\)) with electrical conductivity of \(\sigma=(4L)/\pi\times d2\times R\)[23]. The average diameter (\(d=r/2\), \(r\): radius) was \(\sim\)1654 \(\upmu\)m for p GF tows within the epoxy resin and \(\sim\)1600 \(\upmu\)m for the n-type GF tows within the epoxy resin, measured via a calibrated optical microscope. The electrical resistance (R) was measured along the length (L) via a two-probe method. It should be mentioned that the nanoparticle-based layers for both p-type and n-type SWCNTs coatings onto GFs possess lower electrical conductivity values in comparison to the aggregated and highly dense structure of the corresponding bucky paper films form, as presented in Table 1. Furthermore, the bulk thermoelectric properties of both p- and n-type bucky paper films were studied. The positive measured values of the Seebeck coefficient correspond to the p-type semi-conducting behavior and the negative to the n-type, respectively.  
Figure 6: (**a**) Experimental set-up used to study the Raman spectra of p-type SWCNTs coated GFs and n-type SWCNTs coated GFs at room temperature, as well as at \(\Delta T\) of 50 \({}^{\circ}\)C and 100 \({}^{\circ}\)C and (**b**,**c**) Raman spectra of the p-type and n-type SWCNTs coated GFs at ambient and at temperature gradients of 50 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, respectively.  
The hierarchical GF tows model composites were evaluated in terms of their thermoelectric response as presented in Figure 7.
# Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR
## 3 Low Frequency Expansion
### Well Constrained Low-Frequency Expansion Method  
In the framework of CS theory, low-frequency information can be recovered in the frequency domain by using the sparsity of reflection coefficients in the time domain and frequency domain. This method is greatly affected by noise; that is, the reflection coefficient is more accurate when noise is weak. Otherwise, the accuracy of the reflection coefficient will be greatly reduced. In this section, well data are introduced into the cost function to constrain the inversion process so as to improve the accuracy of the reflection coefficient inversion.  
For the noisy seismic data \(\mathbf{G}\), the cost function with well constraint is constructed as follows:  
\[\begin{split}\min_{r}\frac{1}{2}\|\mathbf{G}-\mathbf{F}\mathbf{W}\mathbf{r}\|_{2}^ {2}&+\lambda\|\mathbf{r}_{w}-\hat{\mathbf{r}}_{w}\|_{2}^{2}\\ s.t.\|\mathbf{r}\|_{1}^{1}&\leq\sigma\end{split} \tag{12}\]  
Figure 2: Low-frequency expansion process based on CS [21].  
where \(\sigma\) is the noise standard deviation estimation of reflection coefficient, \(\mathbf{r}_{w}\) is the reflection coefficient at the well location, and \(\hat{\mathbf{r}}_{w}\) is the reflection coefficient estimated from the well information.  
The cost function can also be solved by a greedy algorithm, base pursuit algorithm or iterative threshold algorithm. The iterative threshold method has a simple structure, less calculation and good stability, so this method is adopted to solve the cost function. The iterative formula is as follows:  
\[\mathbf{r}^{j+1}=T_{\lambda_{k}}(\mathbf{r}^{j}+\frac{1}{\alpha}\big{[}\big{(}\mathbf{W} \mathbf{F}\big{)}^{\mathrm{T}}\Big{(}\mathbf{G}-\mathbf{W}\mathbf{F}\mathbf{r}^{j}\Big{)}+\lambda \mathbf{R}_{w}^{\mathrm{T}}\Big{(}\mathbf{R}_{w}\mathbf{r}^{j}-\hat{\mathbf{r}}_{w}\Big{)} \big{]},\frac{\lambda}{\alpha}\Big{)} \tag{13}\]  
where \(\mathbf{W}\mathbf{F}\) is the measurement matrix, which is composed of wavelet operator and Fourier transform operator. \((\mathbf{W}\mathbf{F})^{\mathrm{T}}\) is the conjugate transpose of \(\mathbf{W}\mathbf{F}\). \(\mathbf{R}_{w}\) is the sampling operator to extract the location information of the corresponding well. \(\mathbf{R}_{w}^{\mathrm{T}}\) is the conjugate transpose of \(\mathbf{R}_{w}\).  
The reflection coefficient of anti-noise interference can be obtained by iterating Formula (13), and then the expanded seismic data can be obtained according to Equations (10) and (11).