esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Evaluation Criteria  
To comprehensively assess the performance of the IGWO-MALSTM model, we utilized four key metrics: Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R\({}^{2}\)). Each of these metrics provides a unique perspective on the model's predictive accuracy and its ability to capture both short-term and long-term trends in stock prices.  
MAE is a metric that calculates the average absolute difference between predicted and actual values. It provides a straightforward measure of prediction accuracy, with smaller values indicating better model performance. MAE is calculated as follows:  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}|\hat{y}_{i}-y_{i}| \tag{9}\]  
MAPE calculates the average absolute percentage difference between predicted and actual values. It provides a relative measure of prediction accuracy, with smaller values indicating better model performance. MAPE is calculated as follows:  
\[MAPE=\frac{1}{n}{\sum_{i=1}^{n}}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right| \times 100 \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Static** & **Value** \\ \hline Mean & \(-166.96\) \\ Standard Deviation & \(122.07\) \\ Minimum & \(-571\) \\ Maximum & \(108\) \\ \hline \end{tabular}
\end{table}
Table 2: Rebar and HRC futures basic statistical properties of closing price data.  
Figure 6: Illustration of the walk-forward evaluation strategy using a moving window approach. Each blue segment represents the input sequence used for training, while the following green segment represents the corresponding prediction targets. After each iteration, the window is shifted forward in time. This method ensures that predictions are made only on future, unseen data, closely simulating a real-world forecasting scenario.  
MSE is a common metric for evaluating model performance, especially in regression. A smaller MSE indicates better model accuracy. However, MSE is sensitive to outliers, as squaring the differences amplifies their effect. MSE is calculated as follows:  
\[MSE=\frac{1}{m}{\sum_{i=1}^{m}}(y_{i}-\hat{g}_{i})^{2} \tag{11}\]  
\(R^{2}\) measures how well a regression model fits the data. An \(R^{2}\) value near 1 indicates a good fit, while a value near 0 suggests a poor fit. \(R^{2}\) is calculated as follows:  
\[R^{2}=1-\frac{\sum_{i}^{n}}{\frac{\sum_{i}^{n}}{\left(\hat{g}_{i}-y_{i}\right) ^{2}}{\sum_{i}^{n}}} \tag{12}\]  
These metrics allow for a comprehensive evaluation of both prediction accuracy and the model's ability to capture the underlying trends in financial data.
## 4 Results and Discussion
### Potential Sources of Misclassification  
Although the classifier can properly detect most rice areas, some land cover types can cause misclassifications. Wetland or seasonal water bodies that are subjected to drying followed by sudden vegetation growth can contribute significantly to an increase in commission errors. This behavior is observed for some rivers that drain very quickly and also for water tanks used to store irrigation water (Sivaganga, for example). The temporal signature of this land cover is similar to the typical rice signature, and if the timing of the event corresponds to the known rice crop calendar, then the discrimination of rice is extremely challenging. One solution is to use multi-temporal SAR or optical images acquired in suitable periods, often outside the rice-growing season, to develop such a mask. In some cases, but this depends on the rice environmental conditions, the use of additional polarizations or the combination of different frequencies (for instance, the C- and L-band, as shown in [25]), which may help in the exclusion of some non-rice areas.  
Many of the omission errors were associated with a lack of correspondence between the observed rice crop calendar in isolated areas within the footprint and the acquisition period. This is particularly problematic when rice is sown early with respect to the average crop calendar, since the signature does not include the critical land preparation/agronomic flooding, which is the foundation of rice detection. A further problem relates to extremely short-duration varieties, around 90 days, which are transplanted as 15- or 20-day-old seedlings. This means that the remaining vegetative stage in which biomass increases substantially is of very short duration and can be hard to detect, especially if there are cancellations during that time of the season. Photoperiod-sensitive varieties with a long growth duration, such as those cultivated in Muang Yang, are also challenging, and further work is required to determine the most appropriate parameters for those situations.  
We have considered only six temporal feature descriptors in this study, but other features could be extracted and related to the rice crop phenology. This could improve the accuracy, but also further guide the parameter selection by providing bounds on both sides of the parameter value.
## 3 Results  
### Site-Specific Parameter Variability  
#### 3.1.1 Allocation and Respiration Parameters  
Model parameters describing carbon allocation show the largest variations both between sites and between tree species (Tables 3 and 4). The parameter \"relative share of foliage growth to root growth\" (FRTALLOC_BASE) exhibits the largest CV for all tree species (>50%). The only other parameter that shows CVs >40% for all tree species is the \"wood maintenance respiration as a fraction of gross photosynthesis\" (WOODMRESP). In addition, spruce and pine both show high CVs (>50%) for the \"minimum ratio of carbon allocation to wood and foliage\" (QWODFOLMIN). Specifically for spruce forests, high CVs are further obtained for the respiration-related parameters RESPQ10 (>50%) and BASEFOLRESPFRAC (>40%) (see Table 2 for explanations). In contrast, pine specific high CVs  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Group** & **Fagus Sylvatica** & **Picea Abies** & **Pinus Sylvestris** \\ \hline BASEFOLRESPFRAC & A & 0.085 & 0.133 & 0.146 \\ FRTALLOC\_BASE & A & 86.0 & 17.7 & 52.4 \\ FRTLOSS\_SCALE & A & 2.423 & 5.689 & 4.240 \\ GRESPFRAC & A & 0.240 & 0.214 & 0.238 \\ MFOLOPT & A & 0.332 & 1.583 & 0.423 \\ QWODFOLMIN & A & 3.052 & 4.123 & 0.602 \\ RESPQ10 & A & 1.693 & 2.637 & 2.094 \\ ROOTMRESPFRAC & A & 0.662 & 0.553 & 0.759 \\ WOODMRESPA & A & 0.166 & 0.130 & 0.118 \\ AMAXB & N & 62.6 & 23.3 & 52.0 \\ EXPL\_NH4 & N & 0.245 & 0.306 & 0.209 \\ EXPL\_NO3 & N & 0.301 & 0.189 & 0.062 \\ FRET\_N & N & 0.520 & 0.420 & 0.617 \\ NCFOLOPT & N & 0.030 & 0.016 & 0.014 \\ NCFTROPT & N & 0.009 & 0.020 & 0.004 \\ NCSAPOPT & N & 0.001 & 0.001 & 0.001 \\ SENESCSTART & N & 208.9 & 207.3 & 258.4 \\ GDDFOLEND & T & 521.3 & 1257.7 & 1054.3 \\ GDDFOLSTART & T & 184.4 & 311.3 & 234.1 \\ GDDWODEND & T & 1738.9 & 1012.9 & 1317.1 \\ GDDWODSTART & T & 139.7 & 256.9 & 202.5 \\ PSNTMAX & T & 45.1 & 38.8 & 40.6 \\ PSNTMIN & T & 4.450 & \(-\)2.494 & 0.650 \\ PSNTOPT & T & 34.5 & 35.1 & 20.5 \\ EXT & W\_L & 0.532 & 0.632 & 0.560 \\ H2 OREF\_A & W\_L & 0.349 & 0.295 & 0.212 \\ WUECMAX & W\_L & 12.3 & 13.7 & 10.3 \\ \hline \end{tabular}
\end{table}
Table 4: Summary of physiological parameters obtained by multi-site calibrations per species type. Parameters are grouped by allocation and respiration (A), nitrogen availability (N), temperature (T) and water availability and light extinction (W\_L).  
are obtained for FRT_LOSS_SCALE (>40%). All other parameters of this group consistently exhibit CVs <35% for all tree species.
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 3 The Concept of a Security Domain of Sensor Nodes
### The Concept of Protection for Sensor Nodes
#### 3.2.1 The Architecture of the Sensor Node  
The sensor node is a mobile device. The XBee interface is used to exchange data inside the sensor node domain. The LoRa interface allowing data transmission over longer distances is intended for use by the sensor node to exchange data with other domains in a situation where a given sensor node acts as a Gateway. The component of the sensor node that processes data is the Arduino microcontroller. The TPM is an obligatory component of the sensor node. Also, each sensor node can use one or more measuring sensors. A block diagram of the sensor node is shown in Figure 3.
## 3 Results  
### Model Validation  
Subject parameters involved in parameter adjustment were selected based on parameter sensitivity analysis, and 217,074 simulations were completed using different parameter combinations. After determining the optimal combination of key parameters, 7599 additional simulations were performed by adjusting the remaining parameters one by one. In total, 224,673 model simulations were completed. Figure 3a shows the time series of the Danzhou flux site monthly observation GPP value (GPP_Obs) and the monthly estimated GPP value (GPP_Est) simulated by the calibrated model during 2010-2015, while Figure 3 bshows the scatter point plot of the GPP_Obs and the GPP_Est simulated by the calibrated model during 2010-2015. The missing portions in Figure 3a were mainly caused by the absence of site data. After calibration, the model's root mean square error (RMSE) was 1.932 g C/m\({}^{2}\)/day, with an R square value of 0.643, but GPP_Est was slightly underestimated. The overall trend of the model fit well, and there was a high correlation between GPP_Obs and GPP_Est. SEIB-DGVM effectively reproduced the monthly dynamics of observation GPP at the Danzhou flux site, and can be applied to simulate GPP and carbon dynamics in rubber plantations under multi-gradient drought scenarios.
## 4 Experiments and Analysis
### Ablation Experiments
#### 4.4.1 The Impact of NWD on Network  
To validate the effectiveness of NWD for small object bounding box regression, we conducted the following ablation study: keeping other parts of the model unchanged, we replaced the localization loss function with the IoU, GIoU, DIoU, CIoU, and NWD, respectively. Table 5 shows the changes in the algorithm's detection metrics when using different localization loss functions. Findings show that employing NWD as the loss function markedly enhances the algorithm's accuracy, retrieval, and mAP50 relative to loss functions based on the IoU.  
Additionally, we found in our experiments that using NWD as the loss function resulted in an AP of 17.8% at 20 epochs and 19.7% at 40 epochs, whereas using the CIoU as the loss function resulted in an AP of 11.1% at 20 epochs and 12.6% at 40 epochs. Utilizing NWD as the loss function resulted in stabilization of the loss, leading to the model's convergence at approximately 200 epochs. Conversely, employing the CIoU as the loss function resulted in the model reaching convergence only after 300 epochs. This indicates that the loss function, based on NWD, adeptly grasps the spatial variations among bounding boxes, enhancing its resilience to alterations in bounding box sizes, thus boosting the precision of detecting small objects.
# Improved Shear Strength Equation for Concrete Wide Beams
## 2 Shear Strength Equation  
### ACI Building Code (ACI 318-14)  
The shear strength equation in ACI 318-14 [13] is calculated using the sum of the concrete's shear strength and the shear reinforcement, as shown in Equations (1)-(3).  
\[V_{n,ACI}=V_{c,ACI}+V_{s,ACI} \tag{1}\]  
\[V_{c,ACI}=\frac{1}{6}\sqrt{f^{r}_{c}}b_{w}d \tag{2}\]  
\[V_{s,ACI}=\frac{A_{s}f_{w}d}{s_{L}} \tag{3}\]  
where \(V_{n,ACI}\) is the nominal shear strength, and \(V_{c,ACI},V_{s,ACI}\) are the shear strength of concrete and the shear reinforcement, respectively. The shear strength of concrete as shown in Equation (2) can be obtained using the compressive strength of concrete (\(f^{r}_{c}\)), beam width (\(b_{w}\)) and effective depth (\(d\)). Equation (3) calculates the shear strength of the shear reinforcement materials, which includes the stirrup area (\(A_{s}\)), the stirrup's yield strength (\(f_{yo}\)), effective beam depth (\(d\)) and the longitudinal spacing of the stirrups (\(s_{L}\)).
## 5 Numerical Simulation of Film Impurity Separation Device Performance
### Polar Variance of Analysis  
Table 6 shows the results of the impact of the test factors on the residual film impurity rate after separation. From the test results and Polar variance of analysis (K) in Table 5, it is observed that the spindle velocity has a minimal impact on the residual film impurity rate after separation, while the drum velocity and air inlet velocity have a larger effect, indicating that these two factors have a significant impact in the test. The order of influence on the test results by the three factors is A \(>\) B \(>\) C, meaning that the drum velocity has the greatest impact, followed by spindle velocity, and then the air inlet velocity. By analyzing the results of the tests in Tables 6 and 7, the levels of each factor in terms of the residual film impurity rate are ranked as A\({}_{2}\) \(>\) A\({}_{3}\) \(>\) A\({}_{1}\), B\({}_{2}\) \(>\) B\({}_{1}\) \(>\) B\({}_{3}\), C\({}_{2}\) \(>\) C\({}_{1}\) \(>\) C\({}_{3}\). Therefore, the optimal combination is A\({}_{2}\)B\({}_{2}\)C\({}_{2}\), which corresponds to a drum rotational velocity of 25 r/min, an air inlet velocity of 14 m/s, and a spindle rotational velocity of 35 r/min.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Level** & **Drum Velocity (r/min)** & **Air Inlet Velocity B (m/s)** & **Spindle Velocity (r/min)** \\ \hline
1 & 20 & 12 & 30 \\
2 & 25 & 14 & 35 \\
3 & 30 & 16 & 40 \\ \hline \end{tabular}
\end{table}
Table 5: Test Factors and Levels.  
Based on the analysis presented in Table 8 and the \(p\)-values of each factor, it can be concluded that A (drum velocity) has a highly significant effect on the residual film impurity rate, while B (air inlet velocity) and C (spindle velocity) have a significant effect on the residual film impurity rate. The influence of each factor on the residual film impurity rate is in the following descending order: A (drum velocity), C (spindle velocity), and B (air inlet velocity). This same ranking is confirmed by the results of the range analysis. Through simulation experiments, it is found that when the rotating velocity of the drum is 25 r/min, the air inlet velocity is 14 m/s, and the spindle velocity is 35 r/min, the experiment results in an impurity content of 8.9% in the film, and the experimental effect is excellent.
## 2 Materials and Methods
### Cannabigerol Quantification  
CBG was quantified by gas chromatography-mass spectrometry (GC-MS). Samples were dissolved in dichloromethane, derivatized by the reagent N/O-bis(trimethylsilyl)trifluoracetamide with 1% trimethylcholosilane (Merck, Rahway, NJ, USA) and analyzed on GC-QQ model EVQQ (Bruker, Karlsruhe, Germany) mass spectrometer, with a Rai-5 Sil MS column (30 m \(\times\) 250 um \(\times\) 0.25 um). Helium served as the carrier gas at a consistent flow rate of 1 mL/min, and the conditions followed the outlined by [PERSON] et al. [14]. The injector was set at 340 \({}^{\circ}\)C with a split of 10, and the oven temperature program consisted of initial conditions at 60 \({}^{\circ}\)C with a 1 min hold, followed by a ramp to 200 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min with a 1 min hold, then a further ramp to 315 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min with another 1 min hold. Finally, there was an increase of 5 \({}^{\circ}\)C/min until reaching 340 \({}^{\circ}\)C, and a 15 min hold at this temperature. The transfer line was maintained at 300 \({}^{\circ}\)C. The quadrupole operated with an electron ionization energy of 70 eV in positive mode, the source temperature set at 280 \({}^{\circ}\)C, and a scan range from 30 to 1000 Da. Compound identification was performed by comparing the obtained mass spectra with a phytocannabinoid standard mixture, as well as through comparison with the NIST library and the free online spectral repository SpectraBaseTM ([[https://spectrabase.com/](https://spectrabase.com/)]([https://spectrabase.com/](https://spectrabase.com/))).
## 4 Explanation of the Prediction Models Using Explainable AI  
### Shapley Value  
One commonly used technique to analyze the relationship between a predictive model and its variables is feature importance. However, this method can indicate only the degree to which each independent feature influences the predicted value, without revealing how much the predicted value changes as each feature is varied. Furthermore, feature importance is applicable only to tree- or boosting-based ensemble algorithms, making it challenging to discern the relationship between individual features and predicted  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Case 1** & **Case 2** & **Case 3** & **Case 4** \\ \hline RF & 332.51 & 381.24 & 307.61 & 327.62 \\ XGB & 203.08 & 247.21 & 199.63 & 265.41 \\ LightGBM & 200.82 & 262.23 & 197.65 & 257.51 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance of prediction model.  
Figure 13: Prediction of each algorithm for case 4: (**a**) RF; (**b**) XGB; (**c**) LightGBM.  
values in models derived through other algorithms, particularly those based on artificial neural networks.  
To address this limitation, Shapley values can be employed. These values rely on game-theory principles to calculate the contribution of each player to the gain, and are determined by constructing a combination of attributes to assess the importance of a single attribute and averaging the change in its presence or absence. In recent years, the SHAP framework has leveraged Shapley values to enhance the explanatory power of predictive models in a variety of fields related to machine learning and deep learning [31].
###### Abstract  
Shear deformation plays an important role in certain structures, and neglecting shear deformation can affect the accuracy of structural response. This paper proposes a non-destructive damage evaluation method that considers shear deformation, based on static response, for identifying corrosion in beam-like structures. The influence of shear deformation on nodal displacement for simply supported beams with different cross-sections was analyzed. The results indicate that even small errors yield inaccurate identification results when neglecting shear deformation. To solve this problem, analytical displacements of the structure were determined based on the Timoshenko beam theory, and the objective function was established. Additionally, the damage identification results were obtained by minimizing the objective function using the interior point method. Several progressively complex examples were used to demonstrate the effectiveness of the proposed method in identifying damage in beam-like structures.  
non-destructive damage evaluation; damage identification; static response; shear deformation; beam-like structure
## Appendix B Optimization Algorithms
### \(Co_{2}\) Content Minimization  
The second service that can be optimized during the scheduling is the \(\text{CO}_{2}\) content of the energy consumed in the MG. This variable is also minimized using the GA approach, as for the EC. Therefore, the \(\text{CO}_{2}\) content of the energy coming from the grid is considered known beforehand for every hour of the test period. The formulation of the problem is analogous to the one presented in Equation (A11). The complete formulation of the problem is presented in Equation (A12).  
\[\begin{split}\underset{P_{bat}}{Min}\Delta t\cdot\sum_{h=1}^{24}(P_ {grid}^{h}\cdot\text{CO}_{2 grid}^{h}+P_{batt}^{h}\cdot\text{CO}_{2 but}^{h}+P_{ pv}^{h}\cdot\text{CO}_{2 pv}^{h}+M\cdot P_{grid}^{h})+L\cdot SoC_{dev}+K\cdot SoC _{out}\\ where:\begin{cases}\text{CO}_{2 but}^{h}=0&\text{ if }P_{ batt}^{h}<0\\ \text{CO}_{2 grid}^{h}=0&\text{ if }P_{grid}^{h}<0\ s.t.\\ M=0&\text{ if }P_{grid}^{h}>0\\ P_{grid}^{h}+P_{batt}^{h}+P_{pv}^{h}+P_{load}^{h}=0\\ \Delta t\cdot\sum_{h=1}^{24}P_{batt}^{h}=0\ \left( SoC^{h=24}= SoC^{h=1}\right)\\ P_{pv}^{h}=P_{pv}^{h\,mppt}\\ 0\%\leq SoC_{batt}^{h}\leq 100\%\\ 0\,kW\leq P_{batt}^{h}\leq 27 kW\end{cases}\end{split}\] (A12)where CO\({}_{2}\) content of the battery and PV energy included in the objective function, correspond to the constant non-corrected nominal values CO\({}_{2}^{nom}\), presented in Table 11. The correction due to battery-life reduction and PV curtailment, expressed in Equations (19) and (20), is computed once the grid power profile for the entire test period has been generated, for the same reasons exposed for the EC minimization. The factors M, L and K represent respectively the penalizing weights of: the energy sent to the grid (\(P_{grid}^{h}<0\)), the deviation from the desired SoC at the end of the day (\(SoC_{dev}\)) and SoC values that go out-of-bounds (\(SoC_{out}\)), for a given battery profile, as previously explained in this section.
# A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection
## 2 Related Technologies  
In this section, we describe some basic concepts about information theory and feature selection, which are used in the proposed feature selection algorithm.  
In the classification process, the relevant features containing important information about the classification result are useful, whereas irrelevant features, which are also known as redundant features, are not preferred because they have little useful information about the classification result. Therefore, the purpose of feature selection is to select as many as possible relevant features and to avoid irrelevant features. As such, tools to measure whether the feature is related to the classification result are highly required, including information theory which provides a way to measure the correlation using MI [36; 37].  
The entropy, as a function of probability, describes the uncertainty of a random variable. Given two continuous variables \(X=\{x_{1},x_{2},...,x_{n}\}\) and \(Y=\{y_{1},y_{2},...,y_{n}\}\), where \(n\) is the total number of features, the entropy and MI [19; 20; 21; 22] are defined as  
\[H(X)=-\int p(x)\log p(x)dx \tag{1}\]\[H(Y)=-\int p(y)\log p(y)dy \tag{2}\]  
\[H(X;Y)=-\iint p(x,y)\log p(x,y)dxdy \tag{3}\]  
where \(H(X)\) and \(H(Y)\) are the information entropy of variables \(X\) and \(Y\), respectively [32]; \(H(X;Y)\) is the joint entropy of variables \(X\) and \(Y\)[33]; \(p(x)\), \(p(y)\) and \(p(x,y)\) are the probability density functions. The MI of variables \(X\) and \(Y\) can be written as [38; 39]:  
\[I(X;Y)=-\iint p(x,y)\log\frac{p(x,y)}{p(x)p(y)}dxdy \tag{4}\]  
\[I(X;Y)=I(Y;X)=H(X)+H(Y)-H(X;Y) \tag{5}\]  
It can be seen that \(I(X;Y)\) is symmetrical [22].  
In discrete forms, given two discrete variables \(F=\{f_{1},f_{2},...,f_{n}\}\) and \(C=\{c_{1},c_{2},...,c_{n}\}\), where \(n\) is the total number of features or classes, the entropy and MI are defined as [18; 19]:  
\[H(F)=-\sum_{f\in F}\ P(f)\log P(f) \tag{6}\]  
\[H(C)=-\sum_{c\in C}\ P(c)\log P(c) \tag{7}\]  
\[I(F;C)=-\sum_{f\in F}\ \sum_{c\in C}P(f,c)\log\frac{P(f,c)}{P(c)P(f)} \tag{8}\]  
where \(F\) is the original feature set of the input data; \(C\) is the class set of the output; \(P(f)\), \(P(c)\) and \(P(f,c)\) represent the probability functions.