esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Calculation of \(\Delta G^{\circ}\)HAT/RAF  
A conformational analysis of **1** was carried out using the semi-empirical quantum mechanical method GFN2-xTB [39; 40; 41]. Conversely, the most stable conformer of **2** was taken from [27]. For all the studied reactions of HAT and RAF, geometry optimizations of the reactants and products were performed in the gas phase without any constraint, using the M06-2X functional [42] combined with the 6-31G(d) basis set, as implemented in Gaussian 16 [43]. Spin contamination was checked for the doublet ground state species to assess the reliability of the wavefunction. Frequency calculations were carried out at the same level of theory (M06-2X/6-31G(d)) in order to confirm the stationary points (all positive frequencies) and to obtain the thermodynamic corrections at 1 atm and 298 K. Afterwards, single-point energy calculations were performed at M06-2X/6-311+G(d,p) in the gas phase in order to obtain more accurate energy values, and subsequently, at the same level of theory, in benzene and water using the continuum Solvation Model based on Density (SMD) [44; 45]. This level of theory is denoted in the text (SMD)-M06-2X/6-311+G(d,p)//M06-2X/6-31G(d,p). Benzene and water mimic an apolar and a polar environment, respectively [46]. The calculation of the HAT/RAF Gibbs free energies for all the available sites was streamlined using an in-house Python script.  
Natural spin densities and atomic charges were calculated with the natural population analysis (NPA) for C4 sites in 1 and its metabolites (Scheme 3) [47]. The electron spin density surfaces related to the localized natural bond orbitals (NBO) were also drawn for selected structures with Multiwfn [48] with a high-quality grid and the isodensity value of 0.003.
# A Dual-Band Modified Franklin mm-Wave Antenna for 5G Wireless Applications
## 5 Conclusions  
A modified Franklin antenna array with a slotted ground structure to be utilized for mmW frequency is proposed. The nine patch antennas and folded dipole-like structures arranged in a three-by-three array manner are connected to a feeding network modified into a folded dipole like structure to provide good impedance matching to the overall antenna design. To achieve the optimum dimensions and suitable results, parametric analysis was carried out by modifying the antenna's various parameters. The antenna offers dual-band resonance characteristics at 22.7 and 34.9 GHz with a total impedance bandwidth of about 2.8 and 2.1 GHz in simulation. For measurement, it shows dual-band operation at 22.8 and 35.3 GHz with impedance bandwidth of about 3 and 1.9 GHz, respectively, thus showing a good correlation between the simulated and measured results. The concept of the antenna array is deployed to enhance the gain of the antenna at a higher frequency of operation and nullify the side lobes. It also provides better impedance matching, as well as providing diversity reception. The antenna offers good gain, impedance matching, compact size, and stable radiation pattern, and thus acts as a very competitive candidate for 5G applications.  
All authors contributed equally for this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Antenna Type** & **No of** & **Resonating** & **Bandwidth (\%)** & **Gain (dBi)** & **Size (mm\({}^{2}\))** & **Advantage** \\ \hline \hline \multirow{4}{*}{[17]} & Circularly Polarized & & & & & & More than 64\% of the \(-10\) dB \\  & Franklin Microstrip & 12 & 6.9 & 5.76 & 3.1 & \(172\times 172\) &
\begin{tabular}{c} \(\mathrm{D}\mathrm{a}\mathrm{I}\mathrm{D}\mathrm{a}\mathrm{I
# Progress in Improving Photovoltaics Longevity
## 2 Methodology  
The methodology for this work was structured to ensure a comprehensive and systematic examination of the literature on PV module longevity, focusing on the key degradation mechanisms, detection methods, and mitigation strategies. The following steps outline the approach taken and are schematically depicted in Figure 1:  
1. The initial step involved defining the key terms and concepts critical to PV system longevity. These include degradation mechanisms (categorized as intrinsic, extrinsic, and specific), operational monitoring techniques, and maintenance strategies. The review was restricted to studies published over the past two decades, capturing the most recent advancements in PV technology and longevity improvements.
2. A thorough literature search was conducted using Scopus. Keywords including \"Photovoltaics\", \"PV lifetime\", \"PV degradation mechanisms\", and \"PV longevity\" were used in various combinations to identify relevant research articles, resulting in an initial dataset of 1148 articles.
3. The initial dataset was screened by title and abstract to exclude irrelevant studies, resulting in a narrowed selection of 133 articles. The search was restricted to peer-reviewed journal articles, review papers, and conference proceedings in English. Additionally, references from key articles were reviewed to ensure comprehensive topic coverage.
4. The full text of the selected articles was analyzed to extract specific information related to degradation mechanisms, monitoring methods, and mitigation strategies. The articles were then categorized into thematic groups, focusing on intrinsic, extrinsic, and specific degradation modes, along with innovative materials, technologies, and best practices in operation and maintenance. The classification was based on identifying-frequent failure patterns and stress factors impacting PV modules' thermal cycling performance.
5. The final step involved identifying research gaps and open questions within the literature. This included areas where further investigation is needed, such as the long-term effects of environmental stressors under different climatic conditions, the development of advanced materials, and the optimization of operation and maintenance (O&M) practices. The review also highlights innovative technologies and approaches that promise to improve PV systems' longevity.  
Results were summarized in tables to categorize degradation modes and their corresponding mitigation techniques, while a chart illustrated trends over time. The systematic approach employed in this review aims to provide a thorough and critical assessment of the current progress in improving the longevity of PV systems, offering valuable insights for both researchers and practitioners in the field of solar energy.
# Retained Placenta as a Potential Source of Mastitis Pathogens in Dairy Cows
## 3 Results
### Identification of Mastitis Pathogens with Bacterial Culture and Agreement between Methodologies  
In total, 28 bacterial isolates were obtained, from which 46.4% (13/28) were confirmed as _E. coli_ through conventional bacterial culture, whereas 25.0% (7/28) were identified as _Strep. uberis_, and only 3.6% (1/28) were identified as _Strep. dysgalaciae_ and _Enterococcus faecalis_. Additionally, _Strep. mutans_, _Strep. porcinus_, _Strep. pseudoporcinus_, _Hafnia alvei_, _Proteus vulgaris and E. fergusonii_ were isolated.  
Except for _E. coli_ (substantial agreement; \(p\) < 0.01), the agreement between RT-PCR and the bacterial culture was fair (_Strep. uberis_; \(p\) < 0.05) or not significant (_Enterococcus_ spp. and _Strep. dysgalaciae_; \(p\) > 0.05; Table 2).  
\begin{table}
\begin{tabular}{l c c c} \hline
**Bacteria Identified by RT-PCR** & **\% Infected Cows (x/X)** & **95\% CI** & **Parity** & **Fever** \\ \hline _Escherichia coli_ & 93.3 (14/15) & 70.2–98.9 & 0.12 & 0.28 \\ \hline _Staphylococcus_ spp. & 93.3 (14/15) & 70.2–98.9 & 0.12 & 0.28 \\ \hline Yeasts \({}^{\star}\) & 92.9 (13/14) & 68.5–98.7 & 0.34 & 0.18 \\ \hline _Trueperella pyogenes_/_Peptoniphilus indolicus_ & 80.0 (12/15) & 54.8–93.0 & 1.00 & 0.30 \\ \hline _Streptococcus uveitis_ \({}^{\star}\) & 78.6 (11/14) & 52.4–92.4 & 0.85 & 0.05 \\ \hline _Streptococcus dysplasia_ \({}^{\star}\) & 57.1 (8/14) & 32.6–78.6 & 0.62 & 0.30 \\ \hline _Enterococcus_ spp. & 46.7 (7/15) & 24.8–69.9 & 0.71 & 0.83 \\ \hline _Mycoplasma_ spp. & 40.0 (6/15) & 19.8–64.3 & 0.26 & 0.50 \\ \hline _Blaz_ gene \({}^{\star}\) & 28.6 (4/14) & 11.7–54.7 & 0.60 & 0.75 \\ \hline _Streptococcus agalaciae_ & 6.7 (1/15) & 1.2–29.8 & 0.11 & 0.14 \\ \hline \end{tabular} \({}^{\star}\)  
\end{table}
Table 1: Association between parity and fever in terms of the prevalence of microorganisms and the beta-lactamase (Blaz) gene isolated with RT-PCR from uterine fluid samples of cows with RP.  
Figure 1: Relationship between _Strep. uberis_ and presence of fever according to logistic regression (\(n=14\)).
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 6 Discussion
We believe that reducing the already reduced number of stationsin the city under study will affect the quality of the predictions; however, we hope to carry out a comparative experiment in a later work. In relation to the models, the results suggest that in the case of prediction 1 h into the future, the multilayer LSTM model is better on average, although the GRU and LSTM models produced similar results. On the other hand, in the case of the 24 h forecast, the results varied, LSTM-4 and GRU being better on average depending on whether 7 or 11 stations were processed, although their performances were very similar. Therefore, we believe that the implementation of these models requires an optimization of the neural network structure for both architectures, LSTM and GRU, according to the available data. Finally, in relation to the space-time models that are the state of the art, although they produce excellent results, most of them are not comparable, since they have been applied in areas where a large number of monitoring stations are available or simulated meteorological variables are provided for each point of the space. When considering the predictions for Santiago de Chile, temporal models are often used which do not include the space component. Although these models are particularly good for modeling PM\({}_{2.5}\) concentrations when the historical values are known, they are not adequate for forecasting pollution at new sites where data are not available. See, for example, the works of [8; 14; 26]. During the preliminary analysis of data, we considered a temporal model, where we obtained on average an \(R^{2}\) greater than 0.95 when predicting one hour ahead with seven stations' data. With the proposed space-time model, we obtained on average an \(R^{2}\) of 0.74 (Table 7) in the same hour-ahead predictions with the same stations' data. This implies that the space-time problem is a more difficult task, since the historical information of the spatial points to be predicted is not included in the training process. If we compare the proposed model with the results of a statistical space-time model proposed by [PERSON] et al. [6], in some cases, our RMSE values are lower. However, many differences characterize the two models: (i) while the RMSE in our model is evaluated for one year of prediction using one year for training the model, in the the statistical model of [6], the RMSE is evaluated for one day after using two and a half months for training the model; (ii) the statistical model uses WRF simulations for predicting the meteorological variables, whereas in our case we only use past values collected by monitoring stations located at points of interest for the forecasting of PM\({}_{2.5}\). We think that our method could improve their predictions if simulations of WRF models or/and other variables are considered as additional input.
## 4 Results
### Development Insights and Technical Achievements
#### 4.1.2 Comparison of ML Models for Fall Detection  
Using a sliding window approach with a window size of 30 s and a step size of 20 s, we extracted a total of 595 windows from the 11,916 acceleration samples collected at 1 Hz. The evaluation of the implemented machine learning models for fall detection is conducted using accuracy (2) as the primary performance metric.  
\[Accuracy=\frac{TP+FN}{FP+FN+TP+TF} \tag{2}\]  
where:  
TP (True Positives)--the number of correctly classified positive instances;  
TN (True Negatives)--the number of correctly classified negative instances;  
FP (False Positives)--the number of negative instances incorrectly classified as positive;  
FN (False Negatives)--the number of positive instances incorrectly classified as negative.  
Table 4 summarizes the accuracy results obtained for the DT, RF, SVM, and XGBoost classifiers. Among these, the RF model demonstrates the highest performance, indicating it is the most suitable algorithm for our fall detection system.  
To assess the RF model's performance, we computed the confusion matrix over the entire set of 595 windows. As shown in Figure 6, the model correctly identified 250 out of 262 fall windows and 316 out of 333 non-fall windows. The confusion matrix reveals that the model maintains a strong balance between sensitivity and specificity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ML Model** & **Accuracy** \\ \hline DT & 94.2\% \\ XGBoost & 94\% \\ RF & 95.1\% \\ SVM & 92.36\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy comparison of ML models for fall detection.  
Figure 6: Confusion matrix of the fall detection RF model.
# A Miniaturized Design for a Terahertz Tri-Mirror CATR with High QZ Characteristics
## 2 Design of a Miniaturized Tri-Mirror CATR
### Cross-Polarization Cancellation Method and Geometric Configuration
Under these parameter constraints, the resulting geometric layout of the tri-mirror system will exhibit a state of low cross-polarization, as illustrated in Figure 1.  
To determine the optical center positions of the mirrors in the low cross-polarization geometric configuration, a global coordinate system, \(XYZ\), with the optical center of mirror 2 as the origin is established. First, the relevant structural parameters of the system are substituted into Equations (1) and (2), satisfying the cross-polarization cancelation conditions. Next, based on design requirements, the parameter variables \(\theta_{0}\) and \(S_{0}\) are assumed to be the desired constant values. Here, \(\theta_{0}\) is set to \(15^{\circ}\), and \(S_{0}\) = 0.13 m is substituted into Equation (1). Then, Equations (1) and (2) are programmed and solved in the numerical computation software MATLAB R2018b. By using the values of the previously known variables, \(\alpha\), \(\sigma_{2}\), \(S_{1}\), and \(f_{3}\), the values of the remaining key unknown variables, \(S_{2}\), \(l\), \(\sigma_{1}\), and \(\sigma_{3}\), can be obtained, and the coordinates of the optical centers of each mirror can be further derived. Table 1 shows the key parameter values of the tri-mirror system with low cross-polarization. This process completes the determination of the geometric configuration and optical center positions for the low cross-polarization tri-mirror system.
## 3 Methodology
### Using Discrete Wavelet Transform to Decompose the Input Series  
In this paper, DWT is performed to decompose these time-series signals into trend and detail components utilizing low- and high-pass filters. DWT decomposes a given discrete signal into orthogonal wavelet functions. In the case of 1D signals, like a time series, results are shown in a transformed vector of equal length. The vector is first filtered with a low-, and then, a high-pass filter. We can mathematically represent a DWT as follows in Equation (8):  
\[\Psi_{l,m}(t)=2^{-\frac{1}{2}}\psi(2^{l}t-m) \tag{8}\]  
Figure 4: Schematic diagram of the model structure.  
The terms \(l\) and \(m\) in the above equation for DWT represent the scale factor and translation index, respectively. \(\psi()\) denotes the mother wavelet function. In detail, the input series \(\vec{X}\) is decomposed by convolving a low- \(L=L_{1},L_{2},\cdots,L_{K}\) and high-pass filter \(H=H_{1},H_{2},\cdots,H_{K}\), where \(K\ll s\) and \(K\) denote the lengths of the input series, as shown in Equations (9) and (10):  
\[A1_{i}=\sum_{k=1}^{K}x_{2i+K-1}\times L_{K} \tag{9}\]  
\[D1_{i}=\sum_{k=1}^{K}x_{2i+K-1}\times H_{K} \tag{10}\]  
where \(A1_{i}\) and \(D1_{i}\) are the \(ith\) elements of the trend and detail components obtained from the single-level discrete wavelet decomposition, and \(x\) denotes the element. Therefore, the DWT results of these input series can be obtained according to Equations (9) and (10), as depicted in Equations (11) and (12), as follows:  
\[X_{ED},X_{ET}=DWT(X_{E}) \tag{11}\]  
\[X_{DD},X_{DT}=DWT(X_{D}) \tag{12}\]  
where \(X_{ED}\) and \(X_{ET}\in\Re^{b\times\times(D\times K)}\), \(X_{DD}\) and \(X_{DT}\in\Re^{b\times(l+p)\times(D\times K)}\), and \(DWT\) represents the discrete wavelet transform according to Equations (8)-(10).
# Bi-Objective Function Optimization for Welding Robot Parameters to Improve Manipulability
## 2 Work Object Analysis and Trajectory Planning
### Trajectory Planning
The axis-angle representation represents a rotation in three-dimensional Euclidean space with a unit vector \(\overrightarrow{\mathrm{K}}=\left[k_{x}\,k_{y}\,k_{z}\right]^{T}\) representing the direction of the rotation axis and an angle \(\theta\) representing the magnitude of the rotation, as shown in Figure 6. Equation (1) is the rotation matrix consisting of the \(\overrightarrow{\mathrm{K}}\), which can be used to rotate an \(A\)-frame into an \(a\)-frame by rotating it by \(\theta\) about the axis \(\overrightarrow{\mathrm{K}}\)[26]. \[ \begin{split}\prescript{A}{}{R_{K}}(\theta)=\begin{bmatrix}r_{11} &r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{bmatrix}=\begin{bmatrix}k_{x}k_{x}\mathrm{v}\theta+ \cos\theta&k_{x}k_{y}\mathrm{v}\theta-k_{z}\mathrm{cos}\theta&k_{x}k_{x} \mathrm{v}\theta+k_{y}\mathrm{sin}\theta\\ k_{x}k_{y}\mathrm{v}\theta+k_{z}\mathrm{sin}\theta&k_{y}k_{y}\mathrm{v}\theta+ \cos\theta&k_{y}k_{z}\mathrm{v}\theta-k_{x}\mathrm{sin}\theta\\ k_{x}k_{z}\mathrm{v}\theta-k_{y}\mathrm{sin}\theta&k_{y}k_{z}\mathrm{v}\theta+ k_{x}\mathrm{sin}\theta&k_{z}k_{z}\mathrm{v}\theta+\mathrm{cos}\theta\end{bmatrix}\end{split}  \tag{1}\]  
\[ \begin{split}\mathrm{v}\theta=1-\cos\theta\\ \theta=\mathrm{acos}(\frac{r_{11}+r_{22}+r_{33}-1}{2})\end{split}  \tag{2}\]  
Equation (2) is the definition for \(\mathrm{v}\theta\). Using the diagonal elements in Equation (1), the angle \(\theta\) can be derived as shown in Equation (3). \[ \begin{split}\overrightarrow{\mathrm{K}}=\frac{1}{2\mathrm{sin} \theta}\begin{bmatrix}r_{32}-r_{23}\\ r_{13}-r_{31}\\ r_{21}-r_{12}\end{bmatrix}\end{split}  \tag{4}\]  
The computational process of trajectory planning is as follows:  
1. Convert the roll, pitch, and yaw angles of each row in Table 2 into a \(3\times 3\) rotation matrix. 2. Use Equations (3) and (4) to calculate axes \(\overrightarrow{\mathrm{K}}\) and \(\theta\) for consecutive rows in Table 2.
3. Interpolate \(\theta\) using a 3 rd-order polynomial. Then, input the interpolated \(\theta\) into Equation (1) to obtain the interpolated rotation matrices. 4. Utilize the \(\mathrm{n}\), \(\mathrm{o}\), and a vector of the rotation matrices, or convert the rotation matrices into roll, pitch, and yaw angles to use as the desired orientation. In this paper, roll, pitch, and yaw angles were used. From the rotation matrices, we converted them to roll, pitch, and yaw to plan the trajectory with a total of 320 steps.
# Hardware Design of a High Dynamic Range Radio Frequency (RF) Harmonic Measurement System
## 4 System Hardware Design
The spectrum analyzer has an 80-dBc dynamic range; coupling this with the 80-dB of loss provided by the Reactel diplexer yields a system dynamic range of over 200-dBc, according to Equation (5). However, the theoretical 200-dBc dynamic range is unachievable, as in practice, the system would be noise limited before 200-dBc can be achieved. The noise floor of the receiver is measured to be less than 135 dBm. With the probe signal measured to be greater than 40 dBm and the noise floor below 135 dBm, the system's dynamic range is estimated to be greater than 175 dB. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Frequency** & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline
900 MHz & \(-7.7\) & \(-8.3\) & \(-8.9\) & +40.5 & +40.2 & +40.1 \\
1800 MHz & \(-40.8\) & \(-94.7\) & \(<\)\(-130\) & +22.4 & \(-91\) & \(<\)\(-130\) \\ \hline \end{tabular}
\end{table} 
Table 2: Measured link-budget for the harmonic distortion measurement system, in dBm, at different locations marked in Figure 8.  
Figure 10: Photographs of the NI chassis with computer, signal generator, and spectrum analyzer. Figure 9: Measured \(|S_{21}|\) response for Reactel diplexers for various ports: (**a**) low-frequency port; and (**b**) high-frequency port.
# Lightweight Deep Learning for Road Environment Recognition
## 4 Experimental Results
### Experimental Result and Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Input Size** & **Backbone** & **mPA** & **Parameters (Millions)** & **FPS** \\ \hline SegNet-VGG16 & 416 \(\times\) 416 & VGG16 & 79.7 & 11.6 & 32 \\ SegNet-ResNet50 & 416 \(\times\) 416 & ResNet50 & 83.1 & 14.9 & 24 \\ \hline PSPNet-ResNet50 & 473 \(\times\) 473 & ResNet50 & 93.5 & 46.8 & 8 \\ PSPNet-MobileNetv2 & 473 \(\times\) 473 & MobileNetv2 & 90.2 & 2.4 & 150 \\ \hline DeepLabv3+ & 512 \(\times\) 512 & Xception & 92.4 & 41.3 & 9 \\ DeepLabv3+ MobileNetv2 & 512 \(\times\) 512 & MobileNetv2 & 89.9 & 2.8 & 130 \\ \hline UNet-VGG16 & 512 \(\times\) 512 & VGG16 & 91.6 & 24.9 & 15 \\ UNet-ResNet50 & 512 \(\times\) 512 & ResNet50 & 93.8 & 44 & 8 \\ \hline Our Approach-1 & 512 \(\times\) 512 & MobileNetv1 & **96.4** & 12.3 & 30 \\ Our Approach-2 & 512 \(\times\) 512 & MobileNetv2 & 94.8 & 10.5 & 35 \\ Our Approach-3 & 512 \(\times\) 512 & MobileNetv3 & 96.1 & 11.5 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the quantitative experimental results on the highway driving testing set, where bold numbers indicate the highest mPA in the column. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Lane** & **Fence** & **Construction** & **Traffic Sign** & **Car** & **Truck** & **Vegetation** & **mIoU** \\ \hline SegNet-VGG16 & 62.2 & 71.2 & 83 & 69.7 & 79 & 84.6 & 91.4 & 77.3 \\ SegNet-ResNet50 & 73.4 & 70.5 & 89.2 & 68.1 & 82.9 & 89.4 & 92.8 & 80.9 \\ \hline PSPNet-ResNet50 & 89.4 & 93.6 & 84.3 & **95.1** & 90.7 & 94.5 & 90.8 & 91.2 \\ PSPNet-MobileNetv2 & 87.2 & 93.7 & 85.6 & 90 & 89.5 & 90.4 & 82.4 & 88.4 \\ \hline DeepLabv3+ & 86.1 & 92.1 & 81.6 & 84.9 & 86.4 & 92.1 & 86.5 & 87.1 \\ DeepLabv3+ & 86.5 & 91.4 & 79.5 & 82.1 & 83.5 & 89.2 & 94.7 & 86.7 \\ MobileNetv2 & 87.3 & 88.5 & 90.7 & 88.1 & 84.4 & 89 & 94.3 & 88.9 \\ UNet-ResNet50 & 89.6 & 89.2 & **91.3** & 88.7 & 90.1 & 90.5 & 94.1 & 91.5 \\ \hline Our Approach-1 & **93.7** & **94.3** & 88.4 & 90.4 & **93.8** & **95** & 97.5 & **93.3** \\ Our Approach-2 & 91.5 & 92.8 & 87.1 & 92.7 & 91.9 & 92.6 & **98.9** & 92.5 \\ Our Approach-3 & 92.8 & 94.4 & 91 & 91.7 & 93.5 & 94.3 & 83.5 & 91.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Segmentation results of the multi-task model, where bold numbers indicate the highest IoU in each column. The visual data of different methods are shown in Figure 19. The visual display results show that our model still performs well when dealing with variable road types, complex environmental backgrounds, and variable weather, which confirms that our model is robust.