esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 5 Thermal Sensing Integration  
The additional challenge of our framework consisted in the integration of an external thermal sensor cooperating with the scene understanding software in order to detect and signal the presence of harmful heat sources. To do so, we installed a commercial thermal camera on the helmet and calibrated it with the AR device's embedded depth sensor.  
We approached the problem of cross-calibrating the two-sensor system as a stereo calibration [34]. Since ToF sensors allow the acquisition of reflectivity information alongside with depth (Section 6) and the adopted thermal camera also embeds a standard RGB one, by treating reflectivity maps as gray-scale images, we can straightforwardly calibrate the system using a checkerboard pattern [35].  
The calibration parameters we are interested in are as follows:  
* \(\mathcal{K}_{D}\), \(\mathcal{K}_{T}\), the _intrinsic_ parameters of the two cameras;
* R, \(\mathbf{t}\), the _extrinsics_, i.e., the rototranslation of one camera with respect to the other;
* F, the _fundamental matrix_ of the two-view system.  
From these, we can write down the projective models for the two cameras as  
\[\mathbf{x}_{D}=\mathrm{P}_{D}\mathbf{X}=\mathcal{K}_{D}[\mathrm{I}\mid\mathbf{0}]\ \mathbf{X}, \tag{3}\]  
\[\mathbf{x}_{T}=\mathrm{P}_{T}\mathbf{X}=\mathcal{K}_{T}[\mathrm{R}\mid\mathbf{t}]\ \mathbf{X}, \tag{4}\]  
where \(\mathbf{X}=(X,Y,Z,1)^{\top}\) is a real-world 3D point in homogeneous coordinates; \(\mathbf{x}=(x,y,1)\top\) is the projected point on the image plane; \(\mathrm{P}_{D}\), \(\mathrm{P}_{T}\) are the respective _camera matrices_. Note, that we are adopting the _center of projection_ of the depth camera as the origin of our world coordinates.  
The two-sensor system is now described by the _epipolar geometry_[36], which models the relations between the observed 3D points in the scene and their projections onto the two camera planes, namely \(\pi_{D}\) and \(\pi_{T}\) (Figure 3a). In particular, given a generic world-point \(\mathbf{X}\), the plane identified by \(\mathbf{X}\) and the two centers of projection, \(\mathbf{C}_{D}\), \(\mathbf{C}_{T}\), is called the _epipolar plane_. The projections of \(\mathbf{X}\) onto \(\pi_{D}\) and \(\pi_{T}\), namely \(\mathbf{x}_{D}\) and \(\mathbf{x}_{T}\), lie themselves on the epipolar plane.  
These geometric relations are utilized in the following paragraph to describe the algorithm designed to calculate the 3D coordinates of a heat-point given its 2D projection on the thermal camera plane. We leave the criterion by which to select such heat-points out of the discussion, as it is usually application-dependent, and we focus instead on the procedure to be applied for a generic given pixel on the thermal image.  
Figure 3: Epipolar geometry relations between the calibrated depth and thermal cameras (**a**) and 3D coordinates reconstruction of a given point \(\mathbf{X}\) with known depth value (distance from the center of projection of the depth camera, \(\mathbf{C}_{D}\)) and optical ray \(\mathbf{r}\) (**b**).
## 5 Conclusions  
This study used statistical regression methods combined with ground observation data and MODIS remote sensing data of the same period to monitor grassland production change in Mongolia along the China-Mongolia Railway, and to reflect continuous change and differences in the spatial and temporal distribution of grassland production. The results showed that with a simulation accuracy of 78%, the exponential model based on MSAVI effectively estimated grassland production. The determination coefficient of the optimal model was 0.72, so there still existed some potential uncertainty in the estimation results. The current results of grassland production from 2006 to 2015 revealed that the spatial distribution of grassland production showed an increasing trend from southeast to northwest. Grassland production along the Zamyu Uud-Sainshand-Choir Railway in the south was the lowest, while the Ulaanbaatar-Sukhbataar Railway in the north had the highest production. Grassland production changed little in the first five years and fluctuated markedly in the latter five years, showing a slight growth trend overall. Grassland production in the surrounding area of Chinggis City--with better land resources--had increased significantly. The main areas of grassland production decline were concentrated in the surrounding regions of Ulaanbaatar, Darhan, and Sukhbataar, as well as the Ommogovic Province where a land degradation trend was apparent. Our investigation of grassland production along the China-Mongolia Railway suggests that in order to curb grassland degradation and promote the green development of this region, efforts to identify key areas of grassland degradation along the China-Mongolia Railway and its driving forces should be intensified. Meanwhile, we will continue to collect sampling data and pursue better models for grassland production through a longer time-series study.  
[PERSON] was responsible for the research design and analysis. [PERSON] drafted the manuscript and was responsible for the data preparation, experiment, and analysis. [PERSON] and [PERSON] were responsible for the data processing and archiving. [PERSON], D.D., S.C., and E.N. were responsible for results verification and article review.  
This study was funded by the Strategic Priority Research Program (Class A) of the Chinese Academy of Sciences (XDA2003020302); the Asia Research Center, Mongolia and Korea Foundation for Advanced Studies, Korea (P2018-3606); and the Construction Project of the China Knowledge Center for Engineering Sciences and Technology (CKCEST-2018-2-8).  
The authors declare no conflict of interest.
## 2 The System Model of LDPC
### Design of the Correlated Reliability  
In fact, the wtd-AlgB algorithm only considers the Hamming distance coefficient \(\theta_{\overline{d}(z_{j}^{(k)},\sigma_{i,j}^{(k)})}\) and the extrinsic information frequency \(n(\sigma_{i,j}^{(k)})\) in each iteration and ignores the channel received information \(y_{j}\). Generally speaking, \(y_{j}\) takes the value of a real number (including positive and negative decimals), while \(\sigma_{i,j}^{(k)}\) is an integer within the range of a finite field. Obviously, the calculation between an integer and a real number will add an additional floating-point operation. In order to avoid floating-point operations in the algorithm, combined with the updated method \(z_{j}^{(k)}\) of the iterative process in reference [19], the channel received information \(y_{j}\) is decided as follows:  
\[z_{j,t}^{(0)}=\left\{\begin{array}{ll}0,&y_{j,t}\geq 0\\ 1,&y_{j,t}<0\end{array}\right. \tag{7}\]  
In Equation (7), if \(y_{j,t}\geq 0\), the initial hard-decision vector \(z_{j,t}^{(0)}\) is 0; otherwise, \(z_{j,t}^{(0)}=1\). In this step, the channel received information is introduced to improve the correctness of decoding, and the received real bit information is simplified to integer bit information, which is beneficial to reduce memory consumption.  
Then, the constellation mapping rule \(\phi(\bullet)\) is processed, and the mapping processing bit information is \(w_{j,t}=\phi(z_{j,t}^{(0)})=+1\) with \(z_{j,t}^{(0)}=0\); otherwise, \(w_{j,t}=\phi(z_{j,t}^{(0)})=-1\). In reference [20], the correlated reliability between the hard decision \(z_{j}^{(k)}\) and the channel received information \(y_{j}\) is calculated. Similarly, in the \(j\)-th iteration, the correlated reliability between the extrinsic information \(\sigma_{i,j}^{(k)}\) and the mapped processing information \(w_{j}\) is computed by:  
\[\rho(\sigma_{i,j}^{(k)},w_{j})=\sum_{t=0}^{r-1}\phi\Big{(}\sigma_{i,j,t}^{(k)} \Big{)}w_{j,t} \tag{8}\]  
Here, \(0\leq j\leq n-1\), and \(\sigma_{i,j,t}^{(k)}\) is the binary expression of \(\sigma_{i,j}^{(k)}\). Obviously, the larger the \(\rho(\sigma_{i,j}^{(k)},w_{j})\), the more reliable the extrinsic information \(\sigma_{i,j}^{(k)}\) is.
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 4 Experimental Results and Performance Analysis
### Participants Data Labeling  
DEAP dataset contains the valence and arousal scores for each video watched by each participant. The videos involved in this study are grouped into two-class problems, such as stress and relaxation, using the arousal and valence scores. The valence and arousal values are obtained for each video for all 32 participants that are in the range of 0-9. The arousal and valence have maximum and minimum scores of 9 and 1, respectively. The stressed and relaxed state conditions, along with each video for each participant, are found using Equations (11) and (12) [18].  
\[\text{\emph{Stress}}=valence<3\cap(\text{\emph{arousal}}>7) \tag{11}\]  
\[\text{\emph{Relaxed}}=(valence>7)\cap\text{\emph{arousal}}<3 \tag{12}\]  
From the results of these equations, we found that some of the videos do not affect brain activity, i.e., neither do participants feel stressed nor relaxed. At the same time, we observed that if an individual is feeling stressed while watching a particular video, the other participants may not experience the same state while watching the same video. Based on valence and arousal values, we collected a total of 92 video data out of 40 (videos) \(*\) 32 (participants) = 1280, which induced stressed and relaxed conditions in participants. A total of 67 video data are labeled as stressful videos, while 25 videos are labeled as relaxing. The videos that did not induce a stressed or relaxed state were removed from the study. The number of videos that are labeled as stressful and relaxing is shown in Figure 3. In Figure 3, a bar graph depicts the number of videos that are associated with stressed and relaxed emotions. From here, we can see that there are 67 videos related to stress, which is significantly higher than the 25 videos that are associated with relaxed conditions.
## Results and Discussion  
### Composition of Basidiocarps  
The organic elemental composition of purified dried basidiocarps is summarized in Table 2. The carbon and hydrogen contents varied in the ranges of \(\sim\)39.7-41.7% and \(\sim\)6.7-6.8%, respectively. Nitrogen (\(\sim\)1.6-4.4%) as well as sulfur (\(\sim\)0.1-0.2%) found in basidiocarps originated mainly from proteins, and nitrogen, to a lesser extent, also from chitin. The chitin-\(\beta\)-p-glucan complex forms the fibrillar base of fungal cell walls, and in the case of _P. ostreatus_ basidiocarps, chitin makes up about 11.5% of the alkali-insoluble part of cell walls [29]. The protein contents calculated from elemental nitrogen were in the range of \(\sim\)6.2-17.5%. Among the samples of basidiocarps, there are two groups of low and high protein contents; i.e., samples hk35, kryos, p80, fotios and p05 were \(\sim\)6.2-9.9% protein,and samples 3009, 3029, po4 and spoppo were \(\sim\)14.8-17.5% protein; the rest could be assigned as medium protein content (\(\sim\)11.2-13.95%) samples. The neutral sugars composition of and glucan contents in purified dried basidiocarps are represented in Tables 3 and 4, respectively. Glucose predominated in all the samples and comprised \(\sim\)71.3-94.4 mol% of all neutral sugars. It means that glucans of various structures should be the main polysaccharides of _P. ostreatus_ basidiocarps. In samples 3009, 3253, p80 and spoppo, galactose, xylose, mannose and/or rhamn were found in moderate quantities up to \(\sim\)5.3-12.6 mol% that markedly decreased the contribution of glucose to 71.3-87.5 mol%. These sugars originate from glycoproteins, galactans, mannogalactans or other heteropolymersaccharides. In all samples, arabinose and fucose were present in much lower or even trace amounts. The contents of total glucans in the samples varied in the range of \(\sim\)18.8-58.2%; the contents of \(\alpha\)-glucans digestible by amylolytic enzymes was low (\(\sim\)2.5-16.7%) in comparison with the rest of the glucans (\(\sim\)15.9-51.5%), assigned as \(\beta\)-glucans. The estimation of \(\beta\)-glucans by this test is complicated by
## 3 Proposed Method
### Third Stage: RV Segmentation  
The RV segmentation is addressed in a separate stage since the experiments verified that this strategy offered a better result. The RV is a structure with variable size and shape throughout the exam volume. Therefore, it is the most complex among the structures of interest studied in the scope of this work.  
To segment the RV, the proposed method employs an additional FCN, called IRAX-Net, whose proposed architecture is based on the X-Net [16]. As input, this network receives the ROIs extracted by the ROI extraction step (Section 3.1), which are first preprocessed as described in Section 3.2.1.  
Figure 6 shows the proposed architecture of IRAX-Net. This network has two phases of feature extraction. In the first one, Inception-ResNet-v2 (IRv2) [48] is used as a backbone. This network comprises Inception blocks that incorporate residual connections instead of the traditional concatenation of filters in their output. This strategy allows Inception residual blocks to perform feature extraction with multiscale filters and a lower computational cost.  
In the second phase's encoder path, only convolutional blocks (kernel \(3\times 3\)) and max pooling operators (kernel \(2\times 2\)) are used. Upsampling operators (kernel \(2\times 2\)) are used in both decoder paths. This network also shares feature maps between the layers using skip connections. In the second phase, Attention blocks are used to assign weights to the regions of these maps that are most relevant to the learning process (similarly to EAIS-Net). The last layer consists of convolutions \(1\times 1\) and sigmoid activation function for the pixel-wise classification between the values 0 (background) and 1 (right ventricle). Finally, as a final step of the proposed method, the masks generated for MYO, LVC, and VD are combined to produce the final result.
###### Abstract  
Landslides cause significant economic losses and casualties worldwide. However, robust prediction remains challenging due to the complexity of geological factors contributing to slope stability. Advanced correlation analysis methods can improve prediction capabilities. This study aimed to develop a novel landslide prediction approach that combines numerical modeling and correlation analysis (Spearman rho and Kendall tau) to improve displacement-based failure prediction. Simulations generate multi-location displacement data sets on soil and rock slopes under incremental stability reductions. Targeted monitoring points profile local displacement responses. Statistical analyses, including mean/variance and Spearman/Kendall correlations, quantified displacement-stability relationships. For the homogeneous soil slope, monitoring point 2 of the middle section of the slope showed a mean horizontal displacement of 17.65 mm and a mean vertical displacement of 9.72 mm under stability reduction. [PERSON]'s rho correlation coefficients ranged from 0.31 to 0.76, while Kendall's tau values ranged from 0.29 to 0.64, indicating variable displacement-stability relationships. The joint rock slope model had strong positive total displacement correlations ([PERSON]'s and [PERSON]'s correlation ranges of +1.0 and -1.0 at most points. Horizontal and vertical displacements reached mean maxima of 44.13 mm and 22.17 mm, respectively, at the unstable point 2 of the center section of the slope. The advanced correlation analysis techniques provided superior identification of parameters affecting slope stability compared to standard methods. The generated predictive model dramatically improves landslide prediction capability, allowing preventive measures to be taken to mitigate future losses through this new approach.  
slope stability; numerical modeling; monitoring points; displacement monitoring; failure prediction; correlation analysis +
Footnote †: journal: Applied Sciences  
1
## 3 Implementation Method
### Cross-Correlation Analysis  
#### 3.1.1 Cross-Correlation Function  
CCA is the most commonly used time-domain detection method in signal processing science and depends on the typical features of the target signals and noise signals in correlation characteristics. It can suppress noise signals and improve the detection sensitivity of the target signals by calculating the cross-correlation function between the target and noise signals. Considering two time series, \(x(t)\) and \(y(t)\), the cross correlation function (CCF) of the two signals is defined as:  
\[R_{xy}(\tau)=\lim_{T\rightarrow\infty}\frac{1}{2T}\int_{-T}^{T}y(t)x(t-\tau)dt \tag{1}\]  
where the integration time is infinite and cannot be realized in practical applications. Therefore, when the integration time is finite, Equation (1) can be rewritten as Equation (2).  
\[\hat{R}_{xy}(\tau)=\frac{1}{T}\int_{0}^{T}y(t)x(t-\tau)dt \tag{2}\]  
where \(\hat{R}_{xy}(\tau)\) is the estimation value of the \(R_{xy}(\tau)\). Because the integration time is finite, there is always a deviation between the estimated and real values. However, the deviation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Friction Pairs** & **Properties** \\ \hline Ball specimen (fixed and upper specimen) & GCr15 steel, Ra 0.13 \(\upmu\)m \\ Disk specimen (driven and lower specimen) & C45 steel, Ra 0.73 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the friction pairs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experiment Settings** & **Values** \\ \hline Normal load & 20 N \\ Lubricant & CD40 lubricating oil \\ Relative humidity & 58\% \\ Temperature & 294 K \\ Reciprocating stroke & 5 mm \\ Relative sliding velocity & 0.067 m/s \\ Test duration & 60 min \\ Vibration signals sampling interval & 0.049 ms (10,240 data points) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Wear experiment settings.  
can be controlled within an allowable range as long as the integration time is sufficiently large. To facilitate computer realization, Equation (2) in the discretization is defined as:  
\[\hat{R}_{xy}(m)=\frac{1}{N}\sum_{n=0}^{N-1}y(n)x(n-m),m=0,1,2,\cdots N-1 \tag{3}\]  
where \(\hat{R}_{xy}(m)\) is the CCF values of the time series \(x(n)\) and \(y(n)\); \(x(n)\) and \(y(n)\) are the time series of the signals \(x(t)\) and \(y(t)\); \(t\) is the integration time; \(T\) is the sampling period, \(t=nT\); \(N\) is the sampling point in the limited integration time; \(m\) is the sequence number of the delay time; \(\tau=mT\) is the time delay.
## 5 Experiments and Discussions  
### Experimental Environment and Datasets  
Figure 10 depicts the visualization interface implemented of the proposed MAP-Vis framework, which includes three correlated views: map view, timeline view, and attribute histogram view. In the map view, the brightness indicates the spatial distribution of spatial objects, i.e., the brighter place contains denser objects while the darker place means relatively sparse ones. The MAP-Vis framework is implemented using several open source projects and libraries. Leaflet and OpenStreetMap are used for the background map display, while the timeline and attribute histogram use the d3 library to support user interaction.  
The MAP-Vis framework is deployed on a nine-node Linux cluster. Each node in this cluster is equipped with two six-core Intel Xeon E5-2620 2 GHz CPUs, 32 GB memory and connected with 1 GB Ethernet. The operating system is CentOS 6.2 64 bit; the required HBase 1.2 and Spark 1.6 is also installed.  
To determine the efficiency and performance of the proposed MAP-Vis framework, we selected four datasets ranging in size from four million to over one billion records. Each of these datasets includes geospatial, temporal, and domain-specific attribute dimensions. A summary of all datasets, including the number of records, the original data size, timespan, and data size in database, are presented in Table 2. Shown from Table 2, the size of the generated MAP data in NYC Taxi is about half of the raw datasets, while the other three increase about three to 80 times. The size difference can be attributed to the sparsity degree of input datasets, i.e., more dense and compact data after aggregation will be less, while more sparse data will become much bigger by blank filling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Records** & **Raw Data Size** & **Time Span** & **Table Size** \\ \hline NYC Taxi & 1.17 billion & 170 GB & 2008.01–2015.12 & 90 GB \\ BrightKite & 11.19 million & 740 MB & 2008.04–2010.10 & 50 GB \\ China Enterprise Data & 20 million & 2.3 GB & 1945.01–2010.12 & 7 GB \\ Global Earthquakes & 3.38 million & 580 MB & 1970.01–2018.12 & 41 GB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information of our experimental datasets.  
Figure 9: The multidimensional query process in the proposed MAP-Vis.  
Figure 10: The MAP-Vis web visualization interface. (**a**) NYC Taxi; (**b**) BrightKite; (**c**) Chinese Enterprise Data; (**d**) Global Earthquakes.
## 3 Results and Discussion
### Agronomic Performance Under Semi-Organic and Conventional Systems
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Treatment**} & **Post-Emergence** & **Pre Harvest** & \multirow{2}{*}{
\begin{tabular}{c} **Plant Height at** \\ **Density** \\ **(Plants Per m\({}^{2}\))** \\ \end{tabular} } & **Number of** & **Maturity** & **Converted to 13\%** & **TSW (g)** \\  & & **(Plants Per m\({}^{2}\))** & **(Plants Per m\({}^{2}\))** & **Stage(cm)** & **Moisture (dt ha\({}^{-1}\))** & \\ \hline \multirow{2}{*}{2021} & P1 \({}^{x}\) & 55.10 \(\pm\) 2.20 \({}^{\text{h.*}}\) & 50.20 \(\pm\) 2.00 \({}^{\text{a}}\) & 29.90 \(\pm\) 3.05 \({}^{\text{a}}\) & 74.90 \(\pm\) 11.30 \({}^{\text{a}}\) & 30.20 \(\pm\) 0.20 \({}^{\text{a}}\) & 223.20 \(\pm\) 5.12 \({}^{\text{a}}\) \\  & P2 & 62.30 \(\pm\) 2.49 \({}^{\text{a}}\) & 48.80 \(\pm\) 1.96 \({}^{\text{a}}\) & 31.00 \(\pm\) 3.20 \({}^{\text{a}}\) & 70.50 \(\pm\) 12.02 \({}^{\text{b}}\) & 28.60 \(\pm\) 0.20 \({}^{\text{b}}\) & 201.30 \(\pm\) 5.40 \({}^{\text{b}}\) \\ \hline \multirow{2}{*}{2022} & P1 & 49.60 \(\pm\) 1.98 \({}^{\text{a}}\) & 48.60 \(\pm\) 1.94 \({}^{\text{a}}\) & 30.30 \(\pm\) 2.85 \({}^{\text{a}}\) & 60.90 \(\pm\) 8.80 \({}^{\text{a}}\) & 21.20 \(\pm\) 0.14 \({}^{\text{a}}\) & 192.60 \(\pm\) 4.80 \({}^{\text{a}}\) \\  & P2 & 50.20 \(\pm\) 2.00 \({}^{\text{a}}\) & 43.80 \(\pm\) 1.75 \({}^{\text{b}}\) & 27.10 \(\pm\) 3.10 \({}^{\text{b}}\) & 60.30 \(\pm\) 7.60 \({}^{\text{a}}\) & 20.40 \(\pm\) 0.15 \({}^{\text{a}}\) & 180.70 \(\pm\) 4.30 \({}^{\text{b}}\) \\ \hline \multirow{2}{*}{2023} & P1 & 52.40 \(\pm\) 2.09 \({}^{\text{a}}\) & 50.20 \(\pm\) 2.00 \({}^{\text{a}}\) & 30.50 \(\pm\) 4.12 \({}^{\text{a}}\) & 74.90 \(\pm\) 7.19 \({}^{\text{a}}\) & 30.40 \(\pm\) 0.22 \({}^{\text{a}}\) & 215.10 \(\pm\) 4.80 \({}^{\text{a}}\) \\  & P2 & 53.60 \(\pm\) 2.14 \({}^{\text{a}}\) & 49.30 \(\pm\) 1.97 \({}^{\text{a}}\) & 30.60 \(\pm\) 4.00 \({}^{\text{a}}\) & 71.90 \(\pm\) 8.30 \({}^{\text{b}}\) & 27.20 \(\pm\) 0.21 \({}^{\text{b}}\) & 205.30 \(\pm\) 4.60 \({}^{\text{b}}\) \\ \hline \multirow{2}{*}{2024} & P1 & 58.20 \(\pm\) 2.32 \({}^{\text{b}}\) & 57.40 \(\pm\) 2.30 \({}^{\text{a}}\) & 36.50 \(\pm\) 3.60 \({}^{\text{a}}\) & 87.70 \(\pm\) 12.40 \({}^{\text{a}}\) & 35.60 \(\pm\) 0.20 \({}^{\text{a}}\) & 220.70 \(\pm\) 5.30 \({}^{\text{a}}\) \\  & P2 & 62.40 \(\pm\) 2.49 \({}^{\text{a}}\) & 57.70 \(\pm\) 2.30 \({}^{\text{a}}\) & 34.40 \(\pm\) 3.18 \({}^{\text{b}}\) & 81.90 \(\pm\) 13.10 \({}^{\text{b}}\) & 33.40 \(\pm\) 0.20 \({}^{\text{b}}\) & 215.40 \(\pm\) 5.10 \({}^{\text{a}}\) \\ \hline \hline \end{tabular} \({}^{\text{a}}\) P1—post-emergence mechanical weeding with rotary harmony, P2—conventional herbicide-based weed control without mechanical weeding. *—small superscript letters (a, b) indicate significant differences between P1 and P2 within each year according to [PERSON]’s HSD test (\(p=0.05\)). TSW—thousand seed weight.  
\end{table} 
Table 3: Agronomic parameters (mean \(\pm\) SE) for soybean cv. ‘Abaca’ cultivated from 2021 to 2024.