esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories
## 2 State of the Art
### Modeling of Tree Crowns  
[PERSON] et al. [39] divide available algorithms for tree modeling from point clouds into two main types: algorithms using voxel models to represent the woody structure parts of a tree and algorithms that are based on geometric models describing the trunk and branches of a tree as geometric objects, mostly cylinders. A voxel is a regular 3D object, usually a cube with a defined edge length. The inner and shadowed parts of the stem and branches are taken into account when the wood volume is calculated [40; 41; 42]. [PERSON] [7], [PERSON] et al. [43] and [PERSON] [8] use voxel models to segment TLS data prior to skeletonization to identify tree segments. To describe the characteristics of forests, [PERSON] et al. [44] use voxels to compute probabilities for the reflection of a laser beam based on ray tracing. Geometric models rely on the segmentation of the trunk and branch components. In most cases this segmentation is based on voxel spaces or octree-algorithms. After the segmentation, rotationally symmetric shapes and objects (cylinders, cones, circles) can be fitted into the point cloud segments. [PERSON] et al. [45] use cones, while [PERSON] et al. [43] and [PERSON] et al. [46] fit cylinders into the primary trunk and branch structures. An automated tree modeling based on cylinders is presented in [PERSON] et al. [10; 35]. The use of high-resolution TLS and MLS data allows to extract and access multiple crown parameters on the individual tree level, and to measure the angle between branches and their orientation. In [PERSON] et al. [47] and [PERSON] et al. [48] the CPA is estimated by calculating the convex hull (closed polygon) of the horizontal projection of the crown points. [PERSON] et al. [49] use MLS to compute crown heights that are applied as ground truth data for the validation of crown height that are derived from panchromatic satellite imagery (WorldView-2). Crown volumes and crown shapes of separated trees can be computed using 3D alpha-shapes [50]. Overlapping crowns and regions of interactions of an individual tree with its neighbors are detected by determining the intersection of alpha-shapes [51; 52]. Using a voxel grid approach allows to analyze precisely the spatio-temporal dynamics in canopy space filling at the individual tree and local neighborhood level [53]. [PERSON] et al. [54] showed that interspecific competition may significantly influence individual tree crown structures. Nevertheless, difficulties remain in the segmentation of crowns in multi-layered stands, where the crowns merge into each other.
## 2 Materials and Methods
### Data Analysis  
The G*Power software (version 3.1.9.2; Kiel University, Kiel, Germany) was used to estimate the required sample size, the expected effect size was set at (Cohen's f) 0.65, the \(\alpha\) level was set at 0.05, and the power (1-\(\beta\)) was set at 0.9. The procedure included a minimum number of 22 participants, but 25 were recruited to account for potential dropouts [27]. The relative and absolute reliability values of the FFD and PPT were computed using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), and minimum detectable change (MDC) [28; 29]. The relative reliability was evaluated by calculating a 2-way mixed ICC32 (for absolute agreement). Statistical analysis was performed using PAWS Statistic SPSS 18 software. Two-way repeated-measures analysis of variance (ANOVA) with post hoc Bonferroni corrections was performed on the FFD and PPT data. The region of the foot (rearfoot: %FRF [%], FRF [N] and forefoot (%FFF [%], FFF [N]) were included as independent factors in two-way, repeated-measures ANOVA for the FFD and FFD changes from  
Figure 1: Low-dye taping technique.  
Figure 2: Plantar pressure pain reference points.  
baseline. Additionally, foot maps (location: 1-7) were included as independent factors in two-way, repeated-measures ANOVA for the PPT and PPT changes from baseline. In all tests, \(p\leq 0.05\) was considered significant.
## 2 Material and Methods
### Partitioning NEE into GPP and RE  
Net ecosystem productivity is the net gain or loss of C from the ecosystem and was the difference of gross primary production (GPP) and ecosystem respiration (RE).  
\[\text{NEP}=\text{GPP}-\text{RE} \tag{1}\]  
The net ecosystem production (NEP) is equivalent to net ecosystem exchange (NEE) but has an opposite sign [30]. Negative NEE or positive NEP indicates CO\({}_{2}\) uptake by the ecosystem.  
\[\text{NEP}=-\text{NEE} \tag{2}\]  
GPP represents CO\({}_{2}\) assimilation by photosynthesis of the plant during daytime, while RE represents the CO\({}_{2}\) released through respiration from the soil, aboveground plant parts, and the roots of the plants during day and night. As plant respiration during the day is unknown, direct measurement of GPP and RE is difficult [31]. The plant respiration rate was assumed to be the same during the day and night [32]. We estimated the daytime respiration RE (D) using the nighttime average of respiration RE (N). To partition NEE into photosynthetic and respiratory components, we applied the conventional method of [PERSON] et al. [33], in which GPP and RE (D) are expressed as empirical functions of air temperature. We estimated RE (D) using the relationship between nighttime NEE and air temperature. At nighttime, as GPP is zero, the NEE is taken to be equal to RE (N). The RE (N) increases as an exponential function of increasing temperature and is represented as:  
\[\text{RE}=\text{A.e}^{\text{BT}} \tag{3}\]where, A and B are empirical constants; B is related to the temperature coefficient Q\({}_{10}\), as B = ln(Q\({}_{10}\))/10, and A indicates RE at 0 \({}^{\circ}\)C [24]. We divided the whole study period into different growth stages viz. early vegetative (EVS), tillering, stem elongation, booting, heading, ripening and harvesting. For each growth period, the nighttime RE (filtered using u* \(>\) 0.1 ms\({}^{-1}\)) was separated into 1 \({}^{\circ}\)C wide temperature bins and averaged, A and B constants were determined by fitting the nighttime NEE as a function of averaged air temperature using the least squares method. At each growth stage, a regression analysis was performed between GPP and PAR [34]. The data after gap filling were used for averaging the CO\({}_{2}\) fluxes at different growth stages. The duration of each stage was used as the weight while computing the CO\({}_{2}\) flux for the whole season. Linear Regression and partial correlation of the datasets were carried out using SPSS (16.0).
# Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction
## 2 Related Work
### Hybrid Representation with 2D and 3D Gaussians  
Several recent methods have explored hybrid representations that combine 2D and 3D Gaussians. HybridGS [29] assigns 3D Gaussians to static regions to maintain view consistency, while employing 2D Gaussians for transient objects, enabling high-quality view synthesis under complex motion and occlusion. Mixed Gaussian Avatar [30] initially places 2D Gaussians on a facial mesh to ensure geometric alignment, and subsequently augments them with 3D Gaussians in areas where rendering quality is insufficient. MGSR [31] independently trains 2D and 3D Gaussians with mutual supervision. Specifically, 2D Gaussians assist lighting estimation in 3D, while 3D Gaussians guide surface reconstruction in 2D. This enhances photometric consistency and geometric accuracy under diverse lighting.  
In contrast to these approaches, we focus on the flat and volumetric nature of 2D and 3D Gaussians to enhance surface reconstruction. Based on this perspective, we propose a hybrid approach, Dual-Dimensional Gaussian Splatting (DDGS), which integrates both 2D and 3D Gaussians to leverage their complementary strengths. To achieve this, we extend the homogeneous transformation to jointly handle both 2D and 3D Gaussians, and introduce an adaptive mechanism that selects the appropriate Gaussian dimensionality based on its scale. In addition, we employ gradient-based regularization terms to further enhance geometric accuracy.
# Risk Perceptions on Social Media Use in Norway
## 4 Demographics and Sample Description
### Self-Assessment
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Choice** & **Count** & **Percentage** \\ \hline \multirow{3}{*}{IT generally} & 1 Caring very little & 9 & 2.70\% \\ \cline{2-4}  & 2 & 78 & 23.70\% \\ \cline{2-4}  & 3 & 107 & 32.50\% \\ \cline{2-4}  & 4 Care a lot & 135 & 41.00\% \\ \hline \multirow{3}{*}{Information security} & 1 Caring very little & 6 & 1.80\% \\ \cline{2-4}  & 2 & 39 & 11.90\% \\ \cline{2-4}  & 3 & 148 & 45.00\% \\ \cline{2-4}  & 4 Care a lot & 136 & 41.30\% \\ \hline \multirow{3}{*}{Privacy} & 1 Caring very little & 4 & 1.20\% \\ \cline{2-4}  & 2 & 44 & 13.40\% \\ \cline{2-4}  & 3 & 133 & 40.40\% \\ \cline{2-4}  & 4 Care a lot & 148 & 45.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of self reported interest in \(\Pi\) in general, information security and privacy. The data are presented with the number of answers for each option and the percentage for the answer N = 329. \(1\) was caring very little, and \(4\) was caring a lot. Figure 4: Comparison of self-reported IT skill, in %, for the different questionnaires N = 329. \(1\) was very little skilled, and \(4\) was highly skilled in IT. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline  & **Category** & **N** & **Mean** & **Std. Dev** & **Std. Error** & **95\% CI** & \multicolumn{2}{c}{**Min**} & **Max** & **Sig.** \\  & **Hacket?** & & & & & & **Lower** & **Upper** & & \\ \hline IT competence & Yes & 50 & 2.9 & 0.735 & 0.104 & 2.69 & 3.11 & 2 & 4 & \\  & No & 279 & 3.29 & 0.728 & 0.044 & 3.2 & 3.38 & 1 & 4 & \\  & Total & 329 & 3.23 & 0.742 & 0.041 & 3.15 & 3.31 & 1 & 4 & 0.001 \\ \hline \multicolumn{8}{c}{Sex} \\ \hline IT competence & Male & 225 & 3.45 & 0.647 & 0.043 & 3.37 & 3.54 & 2 & 4 & \\  & Female & 99 & 2.69 & 0.665 & 0.067 & 2.55 & 2.82 & 1 & 4 & \\  & Total & 324 & 3.22 & 0.741 & 0.041 & 3.14 & 3.3 & 1 & 4 & 0 \\ Information security & Male & 225 & 3.33 & 0.732 & 0.049 & 3.24 & 3.43 & 1 & 4 & \\  & Female & 99 & 3.07 & 0.718 & 0.072 & 2.93 & 3.21 & 1 & 4 & \\  & Total & 324 & 3.25 & 0.737 & 0.041 & 3.17 & 3.33 & 1 & 4 & 0.003 \\ Privacy & Male & 225 & 3.33 & 0.749 & 0.05 & 3.23 & 3.43 & 1 & 4 & \\  & Female & 99 & 3.19 & 0.724 & 0.073 & 3.05 & 3.34 & 1 & 4 & \\  & Total & 324 & 3.29 & 0.743 & 0.041 & 3.21 & 3.37 & 1 & 4 & 0.127 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Self-rating differences between the self-assessment categories.
## 3 Controller Design
### Design of SRBF-MADRC
The radial basis vector is set as \(h=[h_{1},\cdots,h_{m}]^{T}\). The Gaussian function is selected as the radial basis function \(h_{j}\), and its expression is:  
\[h_{j}=\exp\left(-\frac{\left\|x_{1}-c_{j}\right\|^{2}}{2b_{j}^{2}}\right) \tag{20}\]  
where \(j=1,\cdots,m\); \(c=[c_{1},\cdots,c_{m}]\), \(b=[b_{1},\cdots,b_{m}]^{T}\). Set the weight vector as:  
\[W=[w_{1},\cdots,w_{m}]^{T} \tag{21}\]  
Figure 4: The structure of RBF neural network. The output of the SRBF neural network is as follows:  
\[u_{1}=h_{1}w_{1}+\cdots+h_{j}w_{j}+\cdots+h_{m}w_{m} \tag{22}\]  
where \(m\) is the number of nodes in the hidden layer. The error-index function of the SRBF neural network is set as:  
\[E=\frac{1}{2}[u_{2}-u_{1}]^{2} \tag{23}\]  
The gradient descent method is adopted to update the network weight. The learning algorithm of the weight is as follows:  
\[\Delta w_{j}=-\eta\frac{\partial E}{\partial w_{j}}=\eta[u_{2}-u_{1}]h_{j} \tag{24}\]  
\[w_{j}(t)=w_{j}(t-1)+\Delta w_{j}+\gamma(w_{j}(t-1)-w_{j}(t-2)) \tag{25}\]  
where \(\eta\in[0,1]\) is the learning rate factor, \(\gamma\in[0,1]\) is the momentum factor. To sum up, the schematic diagram of the overall speed control structure of the PMSM is shown in Figure 5.  
The total control quantity of the system is as follows:  
\[u=u_{0}+u_{1}-\frac{z_{2}}{b_{0}} \tag{26}\]  
Compared with a single control quantity, the combination of compound control variables can restrain disturbances faster, which consists of the outputs of the NLSEF, SRBF and MRESO.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 3 Results and Interpretation
### GNSS Buoy Vertical Accelerations  
Most of the poor solutions, with the greatest vertical differences, coincide with very large accelerations (Figures 7 and 8), mainly during the flood tides and when the buoy is in shallower water. When large accelerations occurred, the GNSS receiver Rinex data also indicated loss of satellite lock for the majority of visible satellites. Normal practice to improve the analysis when this happens is to experiment with increasing the satellite angle cut off, e.g., \(16^{\circ},17^{\circ},18^{\circ}\), etc., ignoring any data with lower satellite angles. Unfortunately, this did not improve the solutions because the lock problem occurred simultaneously on all the satellites. In fact, it produced progressively poorer solutions due to smaller numbers of useable satellites for the RTKLIB software. It is likely that these poor solutions occur when the antenna is becoming covered in water from spilling waves, which were observed on a number of occasions from the shore; the mooring could be seen to be physically jerked backwards as the wave passed through. For example, the GNSS buoy (photograph in Figure 9) prior to recovery on the 29 August at 13:24: large accelerations were observed in the satellite data (Figure 8). In this case, white water was seen behind the buoy and it eventually washed over it and this coincided with the high recorded accelerations and loss of satellite lock, and thus a poor positional solution.
# KACFormer: A Novel Domain Generalization Model for Cross-Individual Bearing Fault Diagnosis
## 3 Experiments
### Task Setups
#### 3.1.2 Task 2 on CWRU Dataset  
The dataset for Task 2 is derived from the highly cited CWRU dataset [27]. The test bench consists of a 2 HP motor, a torque transducer, and a power dynamometer. The accelerometers are mounted on the drive side and the fan side of the housing to collect vibration signals, as shown in Figure 9.  
The CWRU dataset provides two distinct types of bearings for cross-individual task research, namely the drive end (DE) bearing (6205-2 RS JEM SKF; deep groove ball bearing) and the fan end (FE) bearing (6203-2 RS JEM SKF; deep groove ball bearing). According to official records, the parameters for the two different bearings are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Data Sets** & **Bearing Code** & **Manufacturer** & **Damage** & **Combination** & **Arrangement** & **Extent of** & **Fault** & **Fault** \\  & K001 & IBU & - & - & - & - & N & 1 \\  & K002 & IBU & - & - & - & - & N & 1 \\  & K003 & IBU & - & - & - & - & N & 1 \\ Training sets & KA04 & FAG & fatigue: & S & no & 1 & OR & 2 \\  & & pitting & & repetition & & & & \\  & & Plastic & & & & & & \\  & KA15 & FAG & deforms: & S & no & 1 & OR & 2 \\  & & Indenations & & & repetition & & & \\  & KA16 & MTK & fatigue: & R & random & 2 & OR & 2 \\  & & pitting & & no & & & & \\  & KI04 & MTK & pitting & M & repetition & 1 & IR & 3 \\  & KI14 & MTK & fatigue: & M & no & 1 & IR & 3 \\  & KI16 & FAG & fatigue: & S & no & & & \\ \hline \multirow{2}{*}{Testing sets} & K006 & IBU & - & - & - & - & N & 1 \\  & KA22 & IBU/IBB & fatigue: & S & no & 1 & OR & 2 \\  & KI21 & FAG & fatigue: & S & no & & & \\  & & pitting & S & repetition & 1 & IR & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information on the chosen bearings from the PU dataset.  
Figure 9: Test platform on CWRU dataset.  
The CWRU dataset provides a relatively smaller scale of data for cross-individual fault diagnosis research. According to the relevant literature [28], models tend to face greater challenges in distinguishing fault classes when subjected to higher loads. Regarding the difficulty caused by a relatively insufficient data volume, Task 2 was set on the working condition with 0 HP load. In Task 2, we utilize data collected from the DE bearings to train the model, while data from the FE bearings is used to test the model. Task 2 is also a three-class classification task, including Healthy, OR fault and IR fault.
###### Abstract  
Chronic obstructive pulmonary disease (COPD) is a lung illness, marked by dyspnea, coughing, and sputum production. Cheonwangbosim-dan (CBD) is a traditional East Asian medicine, consisting of a combination of 15 medicinal herbs, which is frequently used to treat arterial/auricular flutter, neuroses, cardiac-malfunction-induced diseases, and insomnia. The present study evaluated the therapeutic effect of CBD (100 or 200 mg/kg) on COPD using a mouse model of COPD induced by cigarette smoke (CS) and lipopolysaccharide-asaccharide (LPS). The increase in inflammatory cell numbers caused by exposure to CS and LPS was significantly reduced by CBD administration. In addition, CBD therapy reduced interleukin-6 (IL-6) and tumor necrosis factor-\(\alpha\) (TNF-\(\alpha\)) in bronchoalveolar lavage fluid (BALF). In lung tissue, CBD not only reduced the levels of IL-18 (CBD 100: \(p<0.001\) and CBD 200: \(p<0.001\)), IL-6 (CBD 100: \(p<0.001\) and CBD 200: \(p<0.001\)), TNF-\(\alpha\) (CBD 100: \(p=0.005\) and CBD 200: \(p=0.014\)), and monocyte chemoattractant protein-1 (MCP-1; CBD 100: \(p=0.018\) and CBD 200: \(p=0.003\)), but also decreased the expression of \(\alpha\)-smooth muscle actin (\(\alpha\)-SCDA), CBD 100: \(p<0.001\) and CBD 200: \(p<0.001\)), transforming growth factor-\(\beta\) (TGF-\(\beta\); CBD 100: \(p<0.001\) and CBD 200: \(p<0.001\)), matrix metalloproteinases-7 (MMP-7; CBD 100: \(p=0.019\) and CBD 200: \(p<0.001\)), MMP-9 (CBD 100: \(p=0.015\) and CBD 200: \(p=0.013\)), and tissue inhibitor of metathe proteinase-1 (TIMP-1; CBD 100: \(p=0.035\) and CBD 200: \(p=0.013\)) compared with the COPD group. CBD was also found to suppress the phosphorylation of nuclear factor kappa B (NF-kB), extracellular signal-regulated kinase 1/2 (ERK1/2), and p38 mitogen-activated protein kinases (p38 MAPK). Taken together, these findings showed that CBD can attenuate respiratory inflammation and airway remodeling induced by exposure to CS and LPS, suggesting that CBD has probable preventive and therapeutic applications in patients with COPD.  
Keywords:anti-inflammatory; Cheonwangbosim-dan; chronic obstructive pulmonary disease; cigarette smoke; lipopolysaccharide-asaccharide; lung fibrosis +
Footnote †: journal: Applied Sciences
## 5 Impact on Structural Design  
The structural design of intermediate- and long-period structures situated on soil sites in southern Romania and exposed to the influence of Vrancea intermediate-depth earthquakes is particularly challenging due to the combination of large seismic design forces (the constant acceleration plateau extends up to 1.0 s or even 1.6 s) and large displacement demands. After the Vrancea intermediate-depth earthquake of March 1977, the design peak ground acceleration for Bucharest increased from 0.2 g to 0.3 g (the current value provided by the P100-1/2013 design code [29] with a mean return period of 225 years). The value of the control period T\({}_{\text{C}}\) for Bucharest has been almost unchanged in the past 45 years (it increased from 1.5 s to 1.6 s in the 2006 version of the design code). The smallest T\({}_{\text{C}}\) value used for design in Romania in the past 45 years was T\({}_{\text{C}}\) = 0.7 s, a value which is close to that of the design acceleration response spectrum constructed for Bucharest using the Eurocode 8 draft revision [1] parameters. Thus, each seismic design code generation has increased the level of safety of newly built structures compared to those designed using previous design code generations.  
The relative displacement check (for ultimate limit state) was introduced in the 1981 version of the design code in order to ensure the stiffness of the structural system. Starting with the 2006 version of the code, the relative displacement check was introduced for the serviceability limit state (SLS) as well. The story drift limits and the corresponding limit states for each version of the seismic design code in Romania since 1992 are shown in Table 2. The story drift limits for the SLS checks and for the ULS check in the 1992 version of the code are presented as a function of the type of non-structural elements.  
Figure 10: A comparison between the design acceleration response spectra for Bucharest computed using the site amplification proposed by [PERSON] et al. [4] and the site amplifications from the Eurocode 8 draft revision [1].  
Considering the differences between the values of the control period T\({}_{\text{C}}\) noted in Figures 4-6, the use of the design spectra computed based on the Eurocode 8 draft revision [1] in the areas exposed to Vrancea intermediate-depth earthquakes would lead to structures which have a lower structural safety level compared to the structures designed according to the current Romanian seismic design code P100-1/2013 [29], even though the mean return period of the seismic action increased (from 225 years to 475 years). In Bucharest, for instance, the maximum design spectral accelerations would correspond to those from the 2006 version of the seismic code P100-1/2006 [31], while the maximum design spectral displacements would be significantly smaller than the design values produced by the 1981 (P100-81 [37]) or 1992 (P100-92 [30]) versions of the code.