esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Biomass Generation from Mixed Grassland and Sorghum  
The sorghum plots showed better homogeneity in settlement (Figure 2), while pasture showed more variability in this parameter, reaching an average dry biomass yield equivalent to 32.6 and 6.5 Tn/ha, respectively (Table 7). On the other hand, these results suggest that sorghum has better established abilities in saline soils irrigated with high salinity effluents. These results are in agreement with the exploratory study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatments**} & \multicolumn{2}{c}{**Fresh Biomass**} & \multicolumn{2}{c}{**Dry Biomass**} & \multirow{2}{*}{**Dry Matter (\%)**} & \multirow{2}{*}{**Height (cm)**} \\  & **(kg/m\({}^{2}\))** & **(kg/m\({}^{2}\))** & & \\ \hline Pasture & 1.96 \(\pm\) 0.60 & 0.70 \(\pm\) 0.20 & 33.9 \(\pm\) 5.10 \({}^{\mathrm{a}}\) & 97.7 \(\pm\) 40.1 \({}^{\mathrm{a}}\) \\ Sorghum & 6.30 \(\pm\) 3.3 & 3.30 \(\pm\) 1.80 & 51.5 \(\pm\) 1.50 \({}^{\mathrm{b}}\) & 273.00 \(\pm\) \\  & & & & 26.70 \({}^{\mathrm{b}}\) \\ \hline \hline \end{tabular} Different letters indicate significant differences (\(p<0.05\)).  
\end{table}
Table 7: Final biomass in each treatment.  
Figure 2: (**a**) General view of the plots cultivated in the field assay (in the front: sorghum; in the background: perennial pasture); (**b**) North view of the plots cultivated in the field assay (in the front: perennial pasture; in the background: sorghum); (**c**) South view of the plots cultivated in the field assay (in the front: perennial pasture; in the background: sorghum).  
Although sorghum had a higher biomass yield than pasture (Figure 3), pasture was more efficient in nutrient uptake in different soil extracts (Tables 5 and 6). This is closely related to the nutrient analysis found in the plant biomass (Figure 3). Most of the analysed macronutrients (NPKs), ash content, and soluble salts (EC) are more concentrated in the pasture biomass.  
This fact presents different alternatives according to the production objectives. If the main strategy has the objective of maximising biomass production (e.g., for bioenergy, animal feed, or composting), the most appropriate approach would be to plant a summer annual crop such as sorghum. On the contrary, if the strategy is to capture as much of the nutrients provided by the effluent, the most appropriate approach would be to plant a summer polyphytic pasture (e.g., sweet clover and wheatgrass).
## 3 Prediction Model
### Neural Network Prediction  
In this paper, the neural network technique is used to study the correlations between the dispositional first-order parameters (laser power, wire feed rate and travel speed) and bead geometrical measurements (height and width) in the LWAM process. The Levenberg-Marquardt algorithm (LM) is used to train the neural network and solve the optimization problem [43].  
The LM algorithm is designed to work specifically with loss functions that take the form of a sum of squared errors. It works without computing the exact Hessian matrix. Instead, it works with the gradient vector and the Jacobian matrix [44]. For the paper self-consistency, the LM algorithm computation is briefly discussed in this subsection.  
Let us consider the following loss function which takes the form of a sum of squared errors,  
Figure 5: Flowchart of the bead height prediction model.  
\[f=\sum_{i=1}^{m}e_{i}^{2} \tag{5}\]  
where \(m\) is the number of training samples. Then let's define the Jacobian matrix of the loss function with respect to the parameters,  
\[J_{i,j}=\frac{\partial e_{i}}{\partial w_{j}},\;\;i=1,\dots,m,\;j=1,\dots,n \tag{6}\]  
where \(m\) is the number of samples in the data set and \(n\) is the number of parameters in the neural network. Now, we can compute the gradient vector of the loss function as,  
\[\
abla f=2J^{T}\cdot e \tag{7}\]  
where \(e\) is the vector of all error terms. Then, we can approximate the Hessian matrix with the following expression,  
\[H_{f}\approx 2J^{T}\cdot J+\lambda I \tag{8}\]  
where \(\lambda\) is a damping factor that ensures the positiveness of the Hessian and \(I\) is the identity matrix. Finally, The next expression defines the parameters improvement process with the Levenberg-Marquardt algorithm,  
\[w_{(i+1)}=w_{i}-(J_{i}^{T}\cdot J_{i}+\lambda_{i}I)^{-1}\cdot(2J_{i}^{T}\cdot e) \tag{9}\]  
for \(i=0,1,\dots,m\), were \(w\) is the parameters vector. The parameter \(\lambda\) is initialized to be large so that the first updates are small steps in the gradient descent direction. If any iteration results in a fail, then \(\lambda\) is increased by some factor. As we will see in the following, this relatively simple method gave satisfying results for finding the relations between the input parameters and bead geometries.
## 5 Discussion and Conclusions  
In this study, we have proposed a new network inference method for use with time series gene expression data, which we call MIDNI. Although many previous methods have been proposed, most deal with real-valued or Boolean expression data. Therefore, they are limited in terms of the applicable network size or experience performance degradation due to reliance on simple representations. Our method implements a multi-level discretization network inference model, and outperforms the compared methods (DBN, MICRAT, and MIBNI). Specifically, MIDNI showed the best results in terms of dynamics accuracy. However, MIDNI has limitations that need to be improved. First, its results in terms of structural accuracy are low, as the current approach relies on the mutual information to select a set of candidate regulatory genes. For certain genes which interact with others via an intermediate gene, the mutual information can be larger than that of genes which directly interact, potentially leading to erroneous positive predictions. Second, MIDNI uses a correlation coefficient to determine the interaction direction of genes. This approach is relatively simple, and there is a room for improvement. Finally, as the MIFS and SWAP subroutines used in MIDNI are greedy algorithms, the run time could be further improved by developing more efficient search algorithms.  
Figure 8: Running time comparison on the _E. coli_ dataset: (**a–c**) show the results on the dataset with noise rates of 0%, 5%, and 10% respectively.  
**Author Contributions:** Formal analysis, C.-T.A.; Writing--original draft, C.-T.A.; Writing--review & editing, Y.-K.K.; Supervision, Y.-K.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the 2023 Research Fund of the University of Ulsan.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** [[https://github.com/tacaomta/MIDNSourceCode](https://github.com/tacaomta/MIDNSourceCode)]([https://github.com/tacaomta/MIDNSourceCode](https://github.com/tacaomta/MIDNSourceCode)).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 System Configuration  
The grid-connected PV system configuration is simple compared with the off-grid PV system, which requires battery storage. It contains PV solar plants, an inverter, a meter, wiring, and a mounting system, as shown in Figure 1. The solar PV plant converts the photons in the sunlight into electricity that runs as DC [26; 27]. The electricity enters the inverter, and then, it is transformed into AC to suit the appliances of the building [28]. The extra electricity is exported to the utility grid where it is measured by a meter. The meter also measures the electricity imported from the utility grid.  
The site selected in this case study is denoted as building A, which is described in Table 2. The building is located in the Bangi area in Selangor, Malaysia and has a lot of empty spaces on the roof without any shading from nearby buildings or trees. The roof was sturdy and made of clay and concrete tiles, which make it possible to install a PV system that the roof will bear its weight.
###### Abstract  
This research evaluates the operational heating and cooling energy consumption of cross-laminated timber (CLT) office buildings in China. The evaluations involve a comparison of the energy consumption of a reference RC structure and CLT system office buildings. Computational simulation results are based on IES-VE 2019 and show that the estimated heating energy saving ratio of CLT buildings in Harbin, Beijing, Shanghai, and Kunming to the reference structure are 11.97%, 22.11%, 30.94%, and 23.30% respectively. However, the CLT buildings consume more energy for cooling in the summer. The results of the research show significantly higher heating energy reductions for CLT buildings in the Cold Region and Severe Cold Regions of China. Thus, the application of the CLT system is better suited to northern China than southern China. The results of the research can be used in further assessment of the use of CLT systems in different climatic regions in China.  
energy saving; conventional material; cross laminated timber (CLT); office buildings Article
## 1 Introduction
Specifically, iterative control and adaptive feedforward cancellation (AFC) algorithms can cancel out the periodic disturbances produced during operation on the target platform [41; 42]. The iterative control algorithm utilizes an internal or external model-based controller design to remove periodic sinusoidal disturbances; this enhances the disturbance-attenuation performance without requiring the feedback controller to be redesigned [43; 44]. However, given the hardware-defined narrow stroke range of FSM motion systems, the performance of the classical AFC algorithm is insufficient for precise control of the rotational position. This paper showcases the design of a high-performance piezo-actuated FSM motion system integrated with a proportional-integral (PI) control system for high-speed steering operation. Based on the results, the mechanical structure of the PZT FSM has been designed and analyzed to maximize performance metrics. Furthermore, designing an effective controller for the piezo-actuated FSM motion system, using a reliable modified AFC algorithm, can reduce control error signals (CES) and enhance vibration compensation for fast tracking. The study demonstrates such an improvement in control performance under multiple sinusoidal vibrations. The remainder of this paper is organized as follows. Section 2 elaborates on the various system components and design choices for the FSM motion system. Section 3 provides the specifications of the FSM motion system along with its model-based performance analyses. The experiments conducted to validate the proposed control algorithm are described in Section 4. Finally, the conclusions are presented in Section 5.
## 3 The Second-Order Zeeman Coefficient Measurement and Zeeman Shift Evaluation
### The Experimental Results and Zeeman Shift Evaluation  
The quadratic Zeeman shift was measured at different \(B_{\rm eff}\) and the result is shown in Figure 4a. The blue dots are the experimental data, where the errors indicate purely statistical 1\(\sigma\) deviation of each datum, and the red solid line is the parabola fitting with the fitting function of \(ax^{2}\)\(+\)\(b\). The quadratic Zeeman shift coefficient (corresponding to the parameter \(a\)) was -23.0(4) MHz/T\({}^{2}\), consistent with other groups [23; 24; 25; 26; 27]. A typical Allan deviation calculated by the data of \(\Delta v_{\rm B}\) is shown in Figure 4b. The Allan deviation reached \(7\times 10^{-17}\) after 1800 s averaging time. While routinely operating the \({}^{87}\)Sr optical lattice clock, the frequency gap between spin-polarized peaks of \(m_{\rm F}\)\(=\)\(-\)9/2 and \(m_{\rm F}\)\(=\)\(+\)9/2 could be obtained by the lock-in data as is shown in Figure 4c. The average frequency gap was 399(1) Hz, and the corresponding bias magnetic field intensity [13] was 409(1) mG. Combining this measurement and other measurements of the quadratic Zeeman shift coefficient of \({}^{87}\)Sr in references [23; 24; 25; 26; 27], the weighted average was \(-\)(23.47 \(\pm\) 0.12) MHz/T\({}^{2}\). The final quadratic Zeeman shift was 9.20(7) \(\times\)\(10^{-17}\) using the weighted average coefficient when our clock routinely operated. The uncertainty was introduced by the uncertainty of the magnetic field and coefficient with equal value of \(5\times 10^{-19}\).  
The short-term drift of the magnetic field was unavoidable in one feedback clock cycle, which may have caused extra error--that is, the residual first-order Zeeman shift. According to the method described in the reference [18], the upper bound of the residual first-order Zeeman shift could be inferred by the full lock-in data of stretched-states frequency gap, as shown in Figure 4c. The upper limit of the residual first-order Zeeman shift was 4(5) \(\times\)\(10^{-18}\) and the uncertainty was 1\(\sigma\) statistical uncertainty that mainly came from the fluctuation of lock-in data. The magnetic field had no obvious drift because the residual first-order Zeeman shift was zero under its 1\(\sigma\) measurement uncertainty.
# Modelling Wear Phenomena Specific to Mixer Blades in Concrete Production Plants
## 3 Results and Discussion  
Figures 5-7 show the results obtained both for elastic deformation and plastic deformation of the surface under wear. \begin{table}
\begin{tabular}{c c} \hline \hline
**Elastic Deformation** & **Plastic Deformation** \\ \hline \(E_{m}\)—modulus of elasticity of the surface material—1.85 \(\times\) 10\({}^{11}\) N/m\({}^{2}\); & HB—target surface hardness—490 \(\times\) 10\({}^{6}\) N/m\({}^{2}\) \\ \(v_{p}\)—Poisson coefficient—0.27 & HB—target surface hardness—490 \(\times\) 10\({}^{6}\) N/m\({}^{2}\) \\ \(\alpha_{0}\)—angle of incidence—0\({}^{\circ}\)\(\div\) 90\({}^{\circ}\); with three specific values: 30\({}^{\circ}\), 45\({}^{\circ}\) and 60\({}^{\circ}\) \\ \(v_{0}\)—particle velocity—0.1 \(\div\) 10 m/s; with a specific velocity of 2.5 m/s \\ \(\mu\)—coefficient of friction between particle and material—0.25 \(\div\) 0.35 & \\ \(\rho_{m}\)—material density of the target surface (cast iron)—7600 kg/m\({}^{3}\); & \\ \(\rho_{ab}\)—density of particle material (crushed mineral aggregate)—2700 kg/m\({}^{3}\); & \\ \(\sigma_{c}\)—flow resistance of pallet material—350 \(\times\) 10\({}^{6}\) N/m\({}^{2}\); & \\ \(k\)—constant depending on the assumed breaking assumption—3; & \\ \(t\)—friction fatigue parameter—1. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specific parameters of the mixer blade. Figure 5: Variation in wear intensity as a function of the angle of incidence, for v\({}_{0}\) = 2.5 m/s and friction coefficient \(\mu\): 0.25, 0.3, and 0.35 (red, blue, and green in the figures): (**a**) elastic deformation; (**b**) plastic deformation. Figure 6: Variation in wear intensity as a function of impact velocity for \(\mu\) = 0.25 and impact angle values of \(\alpha_{0}\): 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\) (red, blue, and green in the figures): (**a**) elastic deformation; (**b**) plastic deformation. Figure 7: Variation in wear intensity as a function of friction coefficient, for v\({}_{0}\) = 2.5 m/s and impact angle \(\alpha_{0}\): 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\) (red, blue, and green in the figures): (**a**) elastic deformation; (**b**) plastic deformation. The interpretation of the modelling results, presented as graphs in Figures 5-7, is as follows:  
* Depending on the angle of impact, three values of the coefficient of friction (Figure 5): Elastic deformation:
* The maximum value of the erosion intensity occurs at an angle of approx.
# Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm
## 3 Study Area and Data  
### Study Area  
The Three Parallel Rivers cover the Nujiang River, the Lantsang River, the Jinsha River, and the mountains in the basin at 26\({}^{\circ}\)03\({}^{\prime}\)-29\({}^{\circ}\)16\({}^{\prime}\) N and 98\({}^{\circ}\)7\({}^{\prime}\)-101\({}^{\circ}\)19\({}^{\prime}\) E (see Figure 4). The north and northwest have temperate and cold temperate monsoon climate, and the south has a plateau monsoon climate. From the Meili Snow Mountain at 6740 m above sea level to the Nu River at an altitude of nearly 700 m (see Figure 5), the terrain height varies significantly, and the geological structure is complex. Thus, this area is a geological disaster-prone area [25].  
The occurrence of landslides has numerous factors. The landslide monitoring data generally refers to the quantitative value of the induced factors (e.g., rainfall and water level) affecting the slope  
Figure 4: Three Parallel Rivers area location.  
Figure 5: Topographical profile of the Three Parallel Rivers.  
variation. During the landslide formation and occurrence, there are also many uncertain factors (e.g., human engineering and sudden earthquakes) [26], as well as numerous landslide monitoring data, including graphical data (GIS data), image data (remote sensing data), and relevant geophysical data. The spatial data (e.g., spatial location and spatial relationship) in landslide monitoring data takes up a large proportion, so landslide monitoring data have close correlation.  
The landslide monitoring data exhibit the following characteristics: the attributes of the landslide monitoring data exhibit strong and continuous spatial correlation, and the use of the regression algorithm and the fitting algorithm helps build the pattern for analysis; the effect of rainfall on the displacement of landslide is certainly accumulated and delayed, and the occurrence of landslide may be shortly affected by heavy rainfall or attributed to the rainfall accumulated over time.
# Model-Robust Estimation of Multiple-Group Structural Equation Models
## 2 Model-Robust Estimation in Structural Equation Modeling
### Robust Moment Estimation Using Robust Loss Functions  
It is evident that the DWLS fitting function is not robust to outlying observations because the square loss function is used. In model-robust SEM estimation in the context of this article, outlying observations are defined as deviations \(\mu_{g}-\mu_{g}(\theta)\) and \(\Sigma_{g}-\Sigma_{g}(\theta)\). In order to allow resistance of the estimator against model deviations, the square loss function in (7) can be replaced with a robust loss function \(\rho\) (see [13])  
\[F_{\text{rob}}(\mathbf{\theta};\{\overline{\pi}_{g}\},\{\mathbf{S}_{g}\})=\sum_{g=1}^{ G}\sum_{i=1}^{I}w_{1 gi}\rho(\overline{\pi}_{gi}-\mu_{gi}(\mathbf{\theta}))+\sum_{g=1}^{ G}\sum_{i=1}^{I}\sum_{j=i}^{I}w_{2 gij}\rho(s_{gij}-\sigma_{gij}(\mathbf{\theta})). \tag{8}\]  
The robust mean absolute deviation (MAD) loss function \(\rho(x)=|x|\) was considered in [28; 29]. This fitting function is more robust to a few model violations, such as unmodeled item intercepts or unmodeled residual correlations of residuals \(\varepsilon_{i}\) (see [8]). In this article, we consider the more general \(L_{p}\) loss function \(\rho(x)=|x|^{p}\) with \(0<p\leq 1\) can ensure even more model-robust estimates [30]. This estimation approach is referred to as robust moment estimation (RME). The \(L_{p}\) loss function with \(p=2\) is the square loss function \(\rho(x)=x^{2}\) and corresponds to ULS estimation.  
The \(L_{p}\) loss function has been successively applied in linking methods [13; 31], which can be considered as an alternative approach to a joint estimation of SEM for multiple groups. The loss function with \(p=0.5\) or \(p=0.25\) has been proposed in the invariance alignment linking approach [32; 33; 34].  
In the minimization of (8), the nondifferentiable loss function \(\rho\) is substituted by a differentiable approximation (e.g., \(\rho(x)~{}=~{}|x|\) is replaced by \(\tilde{\rho}_{\varepsilon}(x)~{}=~{}(x^{2}+\varepsilon)^{1/2}\) for a small \(\varepsilon>0\), such as \(\varepsilon=10^{-3}\); see [30; 35; 36; 30]). In practice, it is advisable to use reasonable starting values and to minimize (8) using a sequence of differentiable approximations with decreasing \(\varepsilon\) values (i.e., subsequently fitting \(\tilde{\rho}_{\varepsilon}\) with \(\varepsilon=10^{-1},10^{-2},10^{-3}\) and using the previously obtained result as the initial value for the subsequent minimization problem).
## 3 Results
### Fecal Lipids and Microbiome Profiles  
We also quantified fecal output and fecal lipid content to account for discrepancies between intake and metabolism of dietary fat in SR and NR mice. There were no changes in fecal pellet characteristics among any of the study groups (Figure 8a). However, the lipid content of the feces was higher for HFD controls (8.8 \(\pm\) 2.9%) than the LFD controls (5.7 \(\pm\) 1.4%, \(p\) = 0.008). Unexpectedly, both SR and NR mice showed lipid fecal contents similar to the LFD controls (Figure 8b), suggesting that both groups absorbed dietary lipids more efficiently than the HFD mice, yet they processed and/or metabolized them differently, which resulted in body weight gain and fat body mass disparities among these groups.  
Figure 8: Fecal outputs and microbiome profiles determined at the end of the study among lean controls (LFD), obese controls (HFD), obesity-resistant non-responders (NR), obesity-prone super-responders (SR), and super-responders fed an LFD, including (**a**) fecal pellet weight, (**b**) fecal fat content, and (**c**) microbiome composition at the phylum level. Results are expressed as means \(\pm\) SEM (n = 8). Data were analyzed using one-way ANOVA followed by [PERSON]’s multiple comparisons; * \(p\) < 0.05 and ** \(p\) < 0.01 versus the HFD controls.  
Figure 7: Changes in (**a**) food intake, (**b**) energy intake, and (**c**) feed efficiency among lean controls (LFD), obese controls (HFD), obesity-resistant non-responders (NR), obesity-prone super-responders (SR), and super-responders fed an LFD. Results are expressed as means \(\pm\) SEM (n = 8). Data were analyzed using one-way ANOVA followed by [PERSON]’s multiple comparisons; * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001, and *** \(p\) < 0.00001 versus the HFD controls.  
Fecal microbial profiles were strongly affected by the obesity-prone and obesity-resistant phenotypes. The LFD controls showed a typical murine fecal microbiome profile consisting of 55% _Firmicutes_, 24% _Bacteroidetes_, 17% _Actinobacteria_, 1.8% _Veruomicrobia_, 1.8% _Eururucheota_, and 1% _Proteobacteria_. Feeding with an HFD resulted in subtle shifts in the fecal microbiome profile towards increased _Firmicutes_ (70%) and decreased _Bacteroidetes_ (11%). These changes were potentiated in the SR obesity-prone mice, with a further reduction in _Bacteroidetes_ (4%) and expansion of _Actinobacteria_ (25%). The NR obesity-resistant mice showed a clear opposing trend, with expansion of _Bacteroidetes_ (37%) and a reduction in _Actinobacteria_ (1%). Even after switching to the LFD, SR-LFD mice preserved the obesity-prone fecal microbiome profile, with reduced _Bacteroidetes_ (4%) and expanded _Actinobacteria_ (41%) until the end of the study (Figure 8c). Unfortunately, the lack of prestudy fecal samples prevented us from drawing conclusions about the initial microbial profiles in these animals.