esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Validation of Coverage Classification  
The validation of the land-cover classification was carried out by means of the statistical measures of producer accuracy (PA), user accuracy (UA), overall accuracy (OA) and the Kappa index. The PA allowed establishing references by means of the predictions of the classes included in the study. Based on the initial predictions, the proportion of correct and incorrect classifications was determined [46]. The UA made it possible to recognize the probability that a classified category effectively represents the class determined in the field. The OA contributed to the development of a base of successfully classified categories. The Kappa index made it possible to evaluate the relationship between the data observed in the image and the data identified by means of the classifier [47].  
After the land-cover classification, a multitemporal analysis was developed by means of IDRISI's Crosstab module through the TerrSet software [48], generating change matrices between the different categories analyzed. For the case study, the changes were established in two periods (2000, 2010; 2010, 2020). The transition matrices generated by the changes in coverage made it possible to estimate losses, gains and permanence for each category. In addition, it was possible to establish the types of coverage transitions, which can be systematic or random. The equations for validating the classification and evaluating the temporal changes of the hedges are detailed in Table 1[49].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measure** & **Formula** \\ \hline \({}^{\text{``Producer accuracy''}}\) & \(\mathrm{PA}=\frac{\mathrm{D}_{\mathrm{i}}}{\mathrm{R}_{\mathrm{i}}}\) \\ (PA) & \(\mathrm{PA}=\frac{\mathrm{D}_{\mathrm{i}}}{\mathrm{R}_{\mathrm{i}}}\) \\ \hline \({}^{\text{``User accuracy''}}\) & \(\mathrm{UA}=\frac{\mathrm{D}_{\mathrm{i}}}{\mathrm{C}_{\mathrm{i}}}\) \\ (UA) & \(\mathrm{UA}=\frac{\mathrm{D}_{\mathrm{i}}}{\mathrm{C}_{\mathrm{i}}}\) \\ \hline \({}^{\text{``Overall accuracy''}}\) & \(\mathrm{UA}=\frac{\mathrm{\Sigma}\mathrm{D}_{\mathrm{i}}}{\mathrm{N}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical measures used to analyze the performance of classification models and temporal analysis of changes in coverages.
## 3 Results  
### GLIDER Environmental Data Recordings  
The three gliders effectively recorded data across planned transects in the LoVe region. All data were uploaded into the GLIDER data management platform where scientists could access and download the data for further analysis. For instance, a subset of environmental data recorded by the Wave Glider showed temporal variation and trends in surface fluorescence, temperature, salinity and oxygen during the summer (June-July) (Figure 5). These data were further employed to explore relations between environmental parameters and the spatiotemporal distribution of zooplankton and cetaceans, as well as to improve regional ocean models (see below).  
Figure 4: Example plots from the GLIDER web portal showing down-sampled data collected by the Seaglider® in the 2018 survey. In all plots, color on data points corresponds to the level of chlorophyll-a (CHI-a, fluorescence). (**A**) Depth along the Seaglider® path (x-axis is time, y-axis is depth). (**B**) Correlation between oxygen (x-axis) and CHI-a (y-axis). (**C**) Path travelled by Seaglider® for which data are displayed. Bottom panel shows the depth (**D**) and CHI-a-oxygen correlation (**E**) along the Seaglider® path (**F**) after the user applied a filter highlighting high CHI-a and oxygen values.
## 4 Experiments
### Ablation Study
#### 4.4.5 \(L_{1}\) Loss and Cross-Entropy Loss  
In this section, we experimentally compare our classification-based cross-entropy loss (CE loss) with the commonly used regression-based \(L_{1}\) loss [5,6] on the DTU dataset. The experimental results are presented in Table 8, where the depth error is calculated as the average absolute difference between the predicted depth and the ground truth. Lower error values indicate better performance. It can be observed that replacing the depth regression approach with the multi-depth classification method reduces the depth error from 8.53 to 6.79, and consequently, the overall reconstruction error is further reduced. This validates the effectiveness of the proposed module.  
In summary, the excellent reconstruction performance of our method is primarily attributed to the appropriate number of input views, the cascaded U-Net module, the epipolar transformer module, the dynamic depth sampling module, and the multi-stage joint learning approach.
## 3 Results and Discussion
### Oxidation of Higher Alcohols  
The first pathway studied is the oxidation of higher alcohols. As already introduced, these compounds can be oxidized in the presence of reactive oxygen species, forming their corresponding aldehydes [2]. The degradation is an alternative pathway for Strecker aldehyde formation. Figure S1 shows the concentrations of 2-methylpropanol (2 MP precursor), 2-methylbutanol (2 MB precursor), and 3-methylbutanol (3 MB precursor) during natural and forced beer aging at two barley varieties (B1 and B2), each with three different malt modification levels (P1 < P2 < P3).  
There was no consistent trend in the behavior of natural beer aging at all variations. Therefore, the most constant trend did not indicate an oxidation reaction. The oxygen level was below 0.1 mg/L in fresh beer samples at all variations [1]. Thus, the oxidation reaction was inhibited and no contribution to the aging aldehyde formation could be observed. However, only the P1 levels of 2-methylbutanol and 3-methylbutanol at B2 showed a significant reduction during aging. The degradation could indicate the oxidative formation of 3 MB or alternative degradation pathways of 3-methylbutanol favored such as esterification, acetal formation, or elimination reaction. Further, the aging of B2P1 is more influenced by oxygen because it showed the lowest precursor levels (Figure 2), as well as the lowest SO2 levels [1], but oxygen concentrations were low, compared with the other variations in this study. Regarding the effect of malt modification, there was also no consistent trend observable. Surprisingly, the medium malt modification level (P2) showed the highest concentrations for 2-methylpropanol and 2-methylbutanol. The higher alcohol concentration measured in fresh beer is mainly formed during fermentation from their corresponding amino acids via the Ehrlich pathway [28]. Although the highest amino acid concentrations of valine, leucine, and isoleucine were found in the highest malt modification level (P3) after boiling [19], a differentiation of the corresponding higher alcohols due to the order in boiled word (P1 < P1 < P3) was not detected. That confirms that the Ehrlich pathway is affected by various parameters such as yeast strain, pH value, aeration, or additional glucose content, which was not monitored in this study, in boiled word [28; 29]. Exemplarily, 3-methylbutanol showed no trend within the different malt modification levels.  
Generally, there was no consistent indication for oxidative Strecker aldehyde formation during beer aging for the observed analytes in this study, which confirms the literature study on wine [11], as already elucidated in the Introduction Section.
# Hybrid Deep Learning Approach for Automated Sleep Cycle Analysis
## 3 Methodology
### Model Training
#### 3.4.2 Loss Function  
The loss function serves to evaluate the discrepancy between the predicted value \(\hat{y}\) and the corresponding ground truth label \(y\). For regression tasks, the mean squared error (MSE) is typically used [45]:  
\[J_{\text{MSE}}=\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}, \tag{11}\]  
where \(m\) is the number of training examples. In classification tasks, the cross-entropy (CE) loss is often utilized to compare the predicted probability distribution with the actual class labels. In binary classification, the CE loss is defined as [45]  
\[J_{\text{CE}}=-\frac{1}{m}\sum_{i=1}^{m}[y_{i}\log(\hat{y}_{i})+(1-y_{i})\log( 1-\hat{y}_{i})]. \tag{12}\]  
Figure 6: Structure of a neural network with an input layer, hidden layers, and an output layer.  
When dealing with multiclass classification involving \(C\) distinct categories, the CE loss generalizes to  
\[J_{\text{CE}}=-\frac{1}{m}\sum_{i=1}^{m}\sum_{j=1}^{C}y_{ij}\log(\hat{y}_{ij}), \tag{13}\]  
where \(y_{ij}\) is a binary value indicating whether instance \(i\) belongs to class \(j\) (1 if true, 0 otherwise), and \(\hat{y}_{ij}\) denotes the model's estimated probability that instance \(i\) is assigned to class \(j\).
# A Power-Line Communication System Governed by Loop Resonance for Photovoltaic Plant Monitoring
## 2 Materials and Methods
### Electronics Designed
Anti-saturation coils have been also connected as AC blocking devices in the combiner box avoiding communication signals to enter the DC path outside the string. These coils also block high frequency harmonics from inverters or other devices in the DC output line to enter the communications loop. The DC power current path is colored blue, and the communications AC signal is colored red. The AC voltage generator (Vac) within each \"A\" block is the circuit equivalent to the power push-pull stage for transmission. Note that the negative side of the communication electronics is connected to the negative lead of the solar module and is a sort of local groundfor this module circuitry. As stated in [6] no common reference voltage exists for the whole string due to its configuration, and to avoid confusion the \"ground\" symbol and name are not used in our schematics, since the local ground of one module (negative) is the positive lead of the previous module. This circumstance allows us to regard the circular communications path as a serial association of voltage generators, where only one of them is driving the signal into the loop while others show an input impedance that leads to a voltage drop for communications signals, enough for reception to work. The system is intended to work in an answer-response fashion in such a way that the transmitter in the combiner box initiates communication with a request sent to one of the receivers that listens for everyone along the loop. The first byte sent is the unique identification address of the receiver requested, that after receiving its address and subsequent command, will respond with the data requested. All parameters of the circuitry and firmware have been carefully adjusted to optimize the communication even in the presence of relatively high levels of noise. When the combiner box is transmitting, the power sent to the loop must be divided equally for the rest of transceivers along the loop. Therefore, all of them must show the same (relatively low) impedance, but for the inverse communication, the reception level can be improved since only the combiner box electronics must receive the response signal, and it can be programmed to show high impedance during reception. This way, most of the signal transmitted back by the requested module will reach the combiner box and only a small fraction of it will be lost along the rest of the loop. Considering that the most volume of information is expected to flow from PV modules toward the combiner box, this last strategy will minimize considerably the bit error probability (BER). Figure 2: General setup for field measurements. The loop resonance frequency of the closed signal path established will depend on the length of the path and on the series resistance, inductance and capacitance found along the AC loop as stated in [16], and our loop is supposed to always fulfill the resonance condition, including, if necessary, an automatic tuning circuitry as the one designed in [16].
## 2 Materials and Methods
### Comparison of LAI Estimates  
Three sets of LAI estimates (S2; S2, using the 10 m processor; and L8) were stacked in large multidimensional datacubes, after applying an image co-registration step [11] to manage the weak spatial coherence of Sentinel-2 time series processed using processing baselines 01.xx and 02.xx [12]. Copernicus Land Monitoring Service datasets were used to generate a vegetation land cover map, used for subsequent analysis.  
A linear regression model was created for each combination of unique pairs from the three LAI sets, using the entire datacubes as dependent and independent variables. A model was generated for both spatial and temporal dimensions, the latter without the use of an intercept value. Additionally, separate models were created for the following land cover classes: all vegetated pixels; croplands; grasslands; broadleaved forest; and needleleaved forest. Regression coefficients and the coefficient of determination (R2) were computed for each model, together with the root mean square error (RMSE).  
In order to perform the comparison between S2 and L8 LAI estimates, S2 datacubes were resampled to fit a 30 m spatial resolution, using average resampling statistics, and only synchronous acquisitions were used.  
All the procedures used to analyze the data were performed using GDAL libraries, R, GRASS GIS, and SNAP software.
# Effect of Stiff Hangers on the Longitudinal Structural Behavior of Tied-Arch Bridges
## 3 Stiff Hangers
### Effect of Hanger and Deck Stiffness  
In an arch bridge, the relative stiffness between the arch and the deck, (i.e., the contribution of the arch or the deck to the global flexural stiffness) is a factor of major importance when determining the structural response. In conceptual design of arch bridges, the Equations (1)-(3) (that can found, for example at Menn [21] and Manterola [22]) are considered to be accurate enough when estimating the bending moments at the arch, and at the deck, respectively \(M_{A}\) and \(M_{D}\), in a bridge similar to the reference bridge:  
\[C_{A}\equiv\frac{EI_{A}}{EI_{A}+EI_{D}} \tag{1}\]  
\[M_{A}=M\cdot C_{A} \tag{2}\]  
\[M_{D}=M\cdot(1-C_{A}) \tag{3}\]  
where \(M\) represents the global bending moment and the ratio \(C_{A}\) represents the contribution of the arch to global flexural stiffness of the arch-deck structural system.  
Therefore, the flexural stiffness of the hangers must be carefully studied, according to the global stiffness of the arch-deck structural system and their relative stiffness, in order to maximize its efficiency. Figure 13 shows how the bending moments distributions are modified by the effect of stiff hangers for different values of \(C_{A}\) = 0.20, 0.33 (reference bridge) and 0.50.  
Figure 12: Pinned and stiff hangers comparison: bending moment distribution at a hanger.
# Classification of Shoulder X-ray Images with Deep Learning Ensemble Models
## 4 Experiments
### Data Augmentation for Shoulder Bone X-ray Images  
Since the results obtained in deep learning, particularly in classification studies, are very sensitive to the dataset, and since an increase in the amount of data in the training stage of the network has a positive effect on the training of the network, the quantity of shoulder bone X-ray images in the MURA dataset used in this study was increased by data augmentation. For this procedure, new images were obtained by rotating the images to the right or to the left to a maximum of 10 degrees.
# AR Long-Term Tracking Combining Multi-Attention and Template Updating
## 2 Related Work  
Since MOSSE [5], a variety of excellent target-tracking algorithms with correlation filtering have come out one after another. However, because the shape and size of the target are constantly changing, the correlation filtering algorithm cannot cope with the tracking requirements in complex scenes. Deep learning can learn richer representations of information.  
At present, the most popular method is target tracking based on the Siamese network. [PERSON] et al. proposed the SiamFC [12] tracker in 2016, which locates the target by calculating the similarity between the template and the search branch, setting off a research boom in target tracking based on Siamese networks. In 2018, [PERSON] et al. proposed SiamRPN [13], which introduced a regional proposal network based on SiamFC to improve tracking accuracy. However, the anchor frame operation introduced a large number of hyperparameters, which was not effective in the face of large deformation and scale change. DaSiamRPN [18] introduces template-based data enhancements based on SiamRPN [13] to improve model robustness and generalization ability. As the above methods all use a shallow backbone network (AlexNet) for feature extraction, the overall performance cannot be greatly improved. SiamRPN++ [17] uses a modified ResNet-50 network for feature extraction on the basis of SiamRPN and obtains good results.  
In order to make the network pay more attention to useful information, the attention mechanism [23] has been widely used in the fields of natural language processing and computer vision in recent years, bringing new ideas for target tracking. [PERSON] et al.  
proposed that SENet [24] could strengthen important channel features and enhance network representation ability by adaptively learning the weight of each channel. In 2018, [PERSON] et al. proposed a Non-Local [25] network, which can capture the global information of all positions in the input feature map, so that the network has stronger modeling ability. [PERSON] et al. proposed CBAM [26] to improve model performance by integrating channel attention and spatial attention. [PERSON] et al. added three attention mechanisms into the appearance branch, which enhanced the ability of feature representation, but limited the ability of network representation because only one of the branches was used. The trackers based on the attention mechanism above either act on only one of the branches or rely on correlation operations for feature fusion in the end. In this paper, inspired by Transformer [23], we design a dual self-attention and mutual attention module to merge the feature information of the two branches of the template and search branch without any correlation operation.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## Materials and Methods  
### Study Area  
The study was conducted at the Jones Center at Ichiuway (JCI) in southwestern Georgia, USA, using data from three sites collected during 2009-2017. The 11,000-lectare JCI property (31.22\({}^{\circ}\) N, 84.47\({}^{\circ}\) W; Figure 1) has a relatively flat terrain with long-term average annual precipitation of 1310 mm; air temperature ranges from \(-3\)\({}^{\circ}\)C to 23 \({}^{\circ}\)C in winter, and 16 \({}^{\circ}\)C to 31 \({}^{\circ}\)C in summer [37]. The regional climate is classified as subtropical. All three sites are maintained by frequent low intensity fire on a two-year return interval (odd-numbered years; Table 1) [36]. Due to the differences in soil water holding capacity and land use legacy, the sites have different understory vegetation composition (Table 1) [38]. The mesic site lies on somewhat poorly drained sandy loam over sandy clay loam and clay textured soils [39]. Soils at the intermediate site have a depth to the argillic horizon of approximately 165 cm and are well-drained [39]. The xeric site lies on well-drained deep sandy soils with no argillic horizon [39]. All sites are situated within 10 km of each other (Figure 1), with heights above sea level of 65 m for the mesic site, 55 m for the intermediate site and 60 m for the xeric site. Inside the flux footprint area of the three EC flux towers, long-leaf pine trees (_Pinus palustris_ Mill.) are the dominant woody plants, averaging 100 years of age, and understory vegetation includes shrubs such as _Diospyros virginiana_ L and _Aristida stricta_ Michx [40].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mesic** & **Intermediate** & **Xeric** \\ \hline DBH (cm) & 25.9 & 42.5 & 22.5 \\ Water holding capacity & 40.3 & 27.6 & 18.5 \\ LAI (m\({}^{2}\)/m\({}^{2}\)) & 2.34 & - & 1.87 \\ NDVI & 0.7 & 0.7 & 0.65 \\ EVI & 0.37 & 0.35 & 0.34 \\ \hline \hline \end{tabular} DBH: diameter at breast height, LAI: leaf area index (Source: MODIS-MOD15 AH2 vegetation product; [[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/))), NDVI: normalized difference vegetation index, EVI: enhanced vegetation index (Source: [40]). Water holding capacity: cm water per cm soil, measured in upper 3 m of soil.  
\end{table}
Table 1: Description of the three eddy covariance (EC) flux sites at the Jones Center at Ichiaway (JCI), Newton, GA, USA.
# Electromagnetic Field Distribution and Data Characteristics of SUTEM of Multilayer Aquifers
## 3 Forward Modeling Mathematical Method of SUTEM
### TEM Field Excited by Rectangular Transmitting Loop
#### 3.2.2 Frequency Domain Electromagnetic Field Excited by Horizontal Rectangular Transmitting Loop  
The emission loop laid in the layered medium can be regarded as the superposition of numerous vertical magnetic dipole sources, as shown in Figure 4. Therein, the above magnetic dipole moment \(m\) is replaced by \(\mathrm{d}m=I\mathrm{d}x^{\prime}\mathrm{d}y^{\prime}\), and then the loop is integrated through its area to obtain the resulting electromagnetic field. In Figure 4, the origin of the coordinate is located in the center of the rectangular transmitting loop, the \(z\)-axis points vertically downward, the coordinate of the receiving point is \((x,y,z)\), the coordinate of the vertical magnetic dipole source is \((x^{\prime},y^{\prime},z^{\prime})\), and the length and width of the rectangular transmitting loop are \(2L\) and \(2W\), respectively [25]. * The secondary field of the plane where the horizontal rectangular transmitting loop is located  
The magnetic dipole moment \(m\) in the first expression of Equation (21) is replaced by \(\mathrm{d}m=I\mathrm{d}x^{\prime}\mathrm{d}y^{\prime}\) and \(\rho=\big{[}(x-x^{\prime})^{2}+(y-y^{\prime})^{2}\big{]}^{1/2}\), the rectangular emission loop in Figure 4 is integrated through its area, and the secondary electric field \(x\) component \(E_{x}\) at the point (\(x,y,z\)) is obtained:  
\[E_{x}\ =\ \frac{\hat{z}_{0}I}{4\pi}\int_{-W}^{W}\int_{-L}^{L}\int_{0}^{\infty} \frac{\lambda^{2}}{u_{0}}(r_{TE_{M}}+r_{TE_{N}})\frac{y-y^{\prime}}{\rho}J_{1}( \lambda\rho)\mathrm{d}\lambda\mathrm{d}y^{\prime}\mathrm{d}x^{\prime} \tag{22}\]  
Since the integration operations are independent of each other, the order of integration is exchanged. First, the following relationship is used:  
\[\frac{\partial J_{0}(\lambda\rho)}{\partial y^{\prime}}\ =\ \lambda\frac{y-y^{ \prime}}{\rho}J_{1}(\lambda\rho)\]  
The secondary electric field \(E_{x}\) can then be rewritten as  
\[E_{x}\ =\ \frac{\hat{z}_{0}I}{4\pi}\int_{-W}^{W}\int_{0}^{\infty}\frac{ \lambda}{u_{0}}(r_{TE_{M}}+r_{TE_{N}})(J_{0}(\lambda\rho_{L})-J_{0}(\lambda \rho_{-L}))\mathrm{d}\lambda\mathrm{d}x^{\prime} \tag{23}\]  
where \(\rho_{L}\ =\ \left[(x-x^{\prime})^{2}+(L-y)^{2}\right]^{1/2}\), \(\rho_{-L}\ =\ \left[(x-x^{\prime})^{2}+(-L-y)^{2}\right]^{1/2}\). Similarly, we can obtain the remaining secondary field components of the same plane of the rectangular loop:  
\[E_{y} =\ -\frac{\hat{z}_{0}I}{4\pi}\int_{-L}^{L}\int_{0}^{\infty}\frac{ \lambda}{u_{0}}(r_{TE_{M}}+r_{TE_{N}})(J_{0}(\lambda\rho_{W})-J_{0}(\lambda \rho_{-W}))\mathrm{d}\lambda\mathrm{d}y^{\prime} \tag{24}\] \[H_{z} =\ \frac{I}{4\pi}\int_{-W}^{W}\int_{-L}^{L}\int_{0}^{\infty}\frac{ \lambda^{2}}{u_{0}}(r_{TE_{M}}+r_{TE_{N}})J_{0}(\lambda\rho)\mathrm{d}\lambda \mathrm{d}y^{\prime}\mathrm{d}x^{\prime}\]  
where \(\rho_{W}\ =\ \left[(W-x)^{2}+(y^{\prime}-y)^{2}\right]^{1/2}\), \(\rho_{-W}\ =\ \left[(-W-x)^{2}+(y^{\prime}-y)^{2}\right]^{1/2}\).