esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Uncertainty Analysis
### Transferability
The results showed that the DAE-MRCNN model achieved the highest accuracy, with an AUC value of 0.891, while the AUC values of the other three models were 0.842, 0.869, and 0.873, respectively. Therefore, the DAE-MRCNN model adequately captures the complex nonlinear relationships among evaluation factors, mitigates the issue of insufficient landslide samples, and significantly improves prediction accuracy. Additionally, compared to shallow machine learning methods, deep learning demonstrates greater advantages in terms of evaluation accuracy. In order to address the issue of transferability, [PERSON] et al. [143] proposed a feature-based domain adaptation method (Figure 10) to enhance the transferability of landslidesusceptibility models in two typical landslide-prone areas in the southeastern part of Fujian Province. They utilized five traditional machine learning algorithms to model the areas with samples and evaluated them in areas without samples. The results showed that the feature transfer method effectively improved the cross-regional prediction capabilities of different models, with an overall indicator improvement of 8.49%. Among them, the SVM and LOG models showed the most significant improvement, reaching 13.68% and 10.19%, respectively, thereby providing a new solution for landslide susceptibility assessment in areas without any samples.
# A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
## 4 Experimental Evaluations
### The Performance of HPCB and Its Peer Methods  
To analyze generalization ability, on the datasets EMODB, CASIA, and SAVEE, confusion matrices of the model HPCB were obtained by averaging 10 experimental results, as shown in Figure 3. The diagonal entry of each confusion matrix represents the recall rate. The prediction results of the three confusion matrices are summarized as follows.  
First, on the test sets of databases EMODB, CASIA, and SAVEE, the average UARs of the model HPCB are 84.65%, 79.67%, and 56.50% respectively. Obviously, it achieves the best performance on the EMODB database.  
Second, on the test set of the EMODB database, emotions Fear (F) and Sadness (S) achieve 100.00% UAR, which is a very impressive recognition result because it has rarely achieved in the previous literature. Similarly, emotions Neutral (N) and Surprise (Su) achieve 95.35% and 89.36% UAR on the test set of the CASIA database, emotions Happiness (H) and Neutral (N) achieve 81.25% and 92.00% UAR on the test set of the SAVEE database.  
Third, on the test set of the EMODB database, it is noted that the emotions Boredom (B) and Neutral (N) are easily confusing pairs, so do Happiness (H) and Anger (A). On the test set of the CASIA database, it is noted that the emotions Fear (F) and Sadness (Sa) are easily confusing pairs. On the test set of the SAVEE database, emotions Anger (A), Disgust (D), and Fear (F) have a low-level recognition performance.  
Tables 1-3 summarize the performance improvements of HPCB in terms of UAR with respect to the related peer methods on the databases CASIA, EMODB, and SAVEE. Among them, the authors of [9; 41; 42; 43; 44] used the research results of previous researchers as the baseline, while the study of [45] was originally proposed in the research of automatic speech recognition. When researchers in [46; 47; 48; 49] applied it to speech emotion recognition, the database used was also inconsistent with the database used in this study. Therefore, this study adopted the model structure proposed in [45; 46; 47; 48; 49] and verified the model performance on the three databases used in this study. Final results are shown in Tables 1-3.  
Figure 3: The confusion matrices of HPCB on the datasets EMODB, CASIA, and SAVEE. **(a)** Confusion matrix of HPCB on database EMODB. **(b)** Confusion matrix of HPCB on database CASIA. **(c)** Confusion matrix of HPCB on database SAVEE.  
The proposed HPCB model achieves much better performance on the CASIA and EMODB databases than the previous models. Among them, HPCB achieved 79.67% and 84.65% recognition performance on databases CASIA and EMODB, respectively. On database SAVEE, HPCB achieved 56.50% recognition accuracy, the UAR of HPCB on the SAVEE database is only 2.90% lower than that of the literature [9]. This suggests that the proposed model has good robustness and generalization.
# Research Trends and Future Direction for Utilization of Woody Biomass in Japan
## 8 Discussion
### More Detailed Forest Inventories  
Most studies estimating the supply of woody biomass in Japan have adopted a long-term macroeconomic perspective, with only a few relying on short-term estimates derived from actual harvest records. Furthermore, short-term estimates often deviate significantly from actual timber supply and demand records. One potential explanation for this discrepancy is the limited availability of detailed records of forest management and operations in Japan. Compared to the empirical growth models used to estimate changes in forest conditions, such as stocks and area, matrix models based on transition probabilities offer a more precise estimation of short-term changes through scenario analysis [81]. However, the application of matrix models requires a foundation of detailed forest management and operational records spanning several years, which are not readily available in Japan. Forests in Japan are managed by different entities based on ownership, and many management entities, including the national government, local governments, and private individuals, disperse their forest management and operations. This dispersion makes it challenging to obtain accurate and comprehensive forest management and operational records. The absence of such records not only hinders the accurate measurement of the current forest status, but also undermines the ability to provide a reliable basis for future projections.  
Many countries promote the establishment of National Forest Inventories to enhance forest surveys and monitor forest status at the national level [81]. Notably, the United States initiated The Forest Inventory and Analysis (FIA) research program in 1928. Over nearly 100 years of development, the FIA has evolved with various functions, ranging from early timber resource assessment to current forest ecosystem observations [82]. Numerous studies have been conducted on FIA across a wide range of fields, including forest resources assessment [83], forest management [84; 85], and forest disasters [86]. It is evident that detailed forest management and operational records are essential not only for Japan, but also for any country that values its forest resources.
# Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
###### Abstract  
Local buckling in steel tubes was observed to be capable of reducing the ultimate loads of thin-walled concrete-filled steel-tube (CFST) columns under axial compression. To strengthen the steel tubes, steel bars were proposed in this paper to be used as stiffeners fixed onto the tubes. Static-loading tests were conducted to study the compression behavior of square thin-walled CFST columns with steel bar stiffeners placed inside or outside the tube. The effect and feasibility of steel bar stiffeners were studied through the analysis of failure mode, load-displacement relationship, ultimate load, ductility, and local buckling. Different setting methods of steel bars were compared as well. The results showed that steel-bar stiffeners proposed in this paper can be effective in delaying local buckling as well as increasing the bearing capacity of the columns, but will decrease the ductility of the columns. In order to obtain a higher bearing capacity of columns, steel bars with low stiffness should be placed inside and steel bars with high stiffness should be placed outside of the steel tubes. The study is helpful in providing reference to the popularization and application of this new structural measure to avoid or delay the local buckling of thin-walled CFST columns.  
steel-bar stiffeners; square thin-walled CFST column; compression behavior; local buckling; structural measure +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Limitations  
General limitations of this study relate to assessment of operating efficacy under highly controlled conditions. In anticipated real-world use, environment and collective operating conditions may vary greatly, i.e., from home to home and hospital to hospital. Specific limitations identified in this study include a defined distance range for accurate motion capture, the inability to detect in a 3-D versus 2-D plane, the need for use of specific color markers, i.e., neon pink or green, and discrimination dependence based on ambient illumination intensity and camera frame capture rate. Hardware limitations relate to certain iPhone or smart phone requirements for an adequate pixel rate, capture rate, maximum aperture, depth of field capture, and frame capture rate. For home use patients may need to affix their phone to a stable support or tripod. Many of these limitations will be obviated with the rapid and continual evolution and advance of the smart phone technology.
# Activity Evaluation Methodology for the Disposed Medical Linear Accelerators
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Component**} & \multirow{2}{*}{**Target**} & **15 MV** & **6 MV** & **15 MV** & **6 MV** & **Ion** & \multirow{2}{*}{**MLC**} & \multirow{2}{*}{**Mirror**} & \multirow{2}{*}{**Y law**} \\ \cline{1-1} \cline{6-10} \multicolumn{1}{c}{**Nuclide**} & & & & & & & & & \\ \hline \({}^{22}\)Na & 2.605 y & & & & & & & & O & \\ \({}^{51}\)Cr & 27.70 d & O & O & O & O & O & O & O & O & O \\ \({}^{54}\)Mn & 312.2 d & O & O & O & O & O & O & O & O \\ \({}^{57}\)Co & 270.9 d & O & O & O & O & O & O & & O \\ \({}^{57}\)Ni & 35.65 h & O & O & O & O & O & O & O & O \\ \({}^{58}\)Co & 70.80 d & O & O & O & O & O & O & O & O \\ \({}^{59}\)Fe & 44.63 d & O & O & O & O & O & O & O & O \\ \({}^{60}\)Co & 5.271 y & O & O & O & O & O & O & O & O \\ \({}^{64}\)Cu & 12.70 h & O & & & & & & & O & O \\ \({}^{65}\)Zn & 64.02 d & & & O & & & & & O & \\ \({}^{82}\)Br & 35.30 h & & & & & & & & & \\ \({}^{99}\)Mo & 66.02 h & O & O & & O & & & & & \\ \({}^{122}\)Sb & 2.700 d & & & & & & & O & O & \\ \({}^{124}\)Sb & 60.208 d & & & & & & & O & O & \\ \({}^{181}\)W & 121.2 d & O & O & O & & & & & O & O \\ \({}^{184}\)Re & 38.00 d & O & & & & & & & O & O \\ \({}^{187}\)W & 23.83 h & & & O & O & & & & O & O \\ \({}^{110}\)Ag & 249.8 d & & & & & & & O & O & \\ \({}^{196}\)Au & 6.183 d & O & & & & & & & O & O \\ \({}^{198}\)Au & 64.80 d & O & & & & & & & O & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Radionuclides per components detected by spectroscopy. for self-disposal even though a dose rate of approximately 0.1 uSv/h higher than the background dose rate was measured. When the dose rate is inversely calculated based on the radioactivity value at the clearance allowance limit for self-disposal, self-disposal was judged to be possible only when the dose rate is at the background level. According to the Canadian regulatory limit, all the activities of the components, except the target, 15 MV linear collimator, and 15 MV flattening filter were allowed for disposal as their activities were under the clearance level [17]. The Japanese regulations are more stringent than the Canadian regulations in clearing the radioactivated medical components and require that the component radioactivity be comparable to the natural background radioactivity prior to clearance, despite the material and size-based differences.
# A Lightweight and High-Accuracy Model for Pavement Crack Segmentation
## 5 Conclusions  
To tackle the challenges of blurred segmentation edges, loss of detail, and excessive computational costs in pavement crack segmentation, this study presents a lightweight, spatial-information-enhanced, fully convolutional neural network model. First, the EfficientNet architecture was pruned, and skip connections were integrated to reduce network parameters while improving the retention and utilization of spatial information. Second, a wavelet transform down-sampling module and an adaptive feature reassembly up-sampling module were introduced to enhance the network's capacity for spatial detail perception. Finally, a dynamic combined loss function was employed to train the network, progressively refining its focus on edge information within images.  
We validated the proposed model on the Crack500 dataset, where it demonstrated superior performance compared to other mainstream models. The model achieved an  
Figure 8: Heatmaps of the original model and the final model.  
MIoU of 80.37%, an F1-score of 78.22%, and a parameter count of only 0.23 M, with an inference speed of 73.49 frames per second. This performance reflects an excellent balance between segmentation accuracy and inference speed, meeting the requirements of road inspection for both precision and efficiency. Additionally, cross-domain testing on the AsphaltCrack300 dataset confirmed the model's strong generalization capability across diverse road scenarios.  
Conceptualization, Y.Y. and W.X.; Data curation, B.H.; Formal analysis, Y.Y.; Investigation, W.X.; Methodology, Y.Y.; Project administration, B.H.; Resources, B.H.; Software, W.X.; Supervision, Z.Z.; Validation, Z.Z. and B.H.; Visualization, Y.Y.; Writing--original draft, Y.Y.; Writing--review & editing, Z.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The datasets used in this paper were generated using the publicly available datasets Crack500 and AsphaltCrack300 ([[https://github.com/guoguolord/CrackDataset](https://github.com/guoguolord/CrackDataset)]([https://github.com/guoguolord/CrackDataset](https://github.com/guoguolord/CrackDataset)), accessed on 10 May 2023). The data files related to this study are available upon request by email from the corresponding authors. Data supporting the conclusions of this study are accessible from the corresponding author upon reasonable request.  
The authors would like to thank each member of the team for their efforts.  
Author [PERSON] was employed by the company China Railway Siyuan Survey and Design Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Numerical Results and Discussion
### Stability Analysis of Large Section Tunnel under Different Excavation Sequences
#### 4.1.3 Stress Analysis of Lining
In terms of the stress of the supporting structure, the construction of Condition 3 was more conducive to the bearing performance of the concrete at the vault. However, Condition 3 was affected by tension at the arch waist, which was an unfavorable position. On the contrary, Conditions 1 and 2 can protect the safety of concrete at the arch waist better but were affected by the tension at the vault. Analysis of the Influence of the Second Primary Lining Supporting Timing on the Stability of Large Section Tunnel  
The primary support of the tunnel structure was set in two layers. Generally, in the process of tunnel excavation, the first primary lining should be carried out quickly after completing the excavation of the tunnel face. The reasonable support timing of the second primary lining was conducive to the continuous bearing performance of the second primary support after the first primary support was carried out together with the surrounding rock. To study the influence of the support timing of the second primary lining on the stability of the large section tunnel, the stress and deformation of the lining were analyzed according to the support timing of different second primary linings. Considering that the second primary support was an important link in the process of tunnel construction and excavation, the bearing performance should be brought into play as soon as possible. Two working conditions were set (Table 5). Similarly, the section at 20 m in the Z direction was selected as the target section for analysis. \begin{table}
\begin{tabular}{l l l} \hline
**Condition** & **Supporting Sequence** & **Supporting Diagram** \\ \hline \multirow{4}{*}{1} & The second primary lining shall be & \\  & supported at the same time as the first & \\  & primary lining of the next section of the & \\  & tunnel face after the current tunnel face & \\  & was advanced by one footage. & \\  & The second primary lining shall be & \\  & quickly supported after the excavation & \\  & of the current tunnel face was & \\  & completed. & \\ \hline \end{tabular}
\end{table} 
Table 5: The second primary lining support timing. Figure 14: The horizontal stress of the temporary support in different conditions (unit: Pa): (**a**) Condition 1; (**b**) Condition 2; and (**c**) Condition 3.
## 3 Results and Discussion
### Source Apportionment PMF
In February, the contribution of this source was increasing by 30%, and in April it was on the same level in both analyzed years. In March and May, it was lowering by 6 and 36%, respectively. The decrease in emission source contribution could be caused by the reduction in activity during the COVID-19 pandemic which started in March 2020. It could also be the result of limited traffic in April and May 2020 during the COVID-19 pandemic. Figure 5: Factor profiles and attributed emission sources of PM\({}_{10}\). The fourth factor was attributed to soil. The markers were PO4\({}^{3-}\) (92% of the mass), Cr (52% of the mass), Co (42% of the mass), Na\({}^{+}\) (31% of the mass), Ca\({}^{2+}\) (31% of the mass), Mg\({}^{2+}\) (22% of the mass), Cu (20% of the mass), Mn (23% of the mass), and Ti (26% of the mass). The contributions to the PM\({}_{10}\) mass were 4.5 \(\upmu\)g/m\({}^{3}\) and 6.5 \(\upmu\)g/m\({}^{3}\) in 2018 and 2020, respectively. Non-identified sources contributed 14.5 \(\upmu\)g/m\({}^{3}\) and 2.9\(\upmu\)g/m\({}^{3}\) to the PM\({}_{10}\) in 2018 and 2020, respectively. Figure 6 shows the factor fingerprints. Figure 7 presents the monthly contribution of sources to the mass of PM\({}_{10}\) in \(\upmu\)g/m\({}^{3}\). For our DISP (displacement analysis), the results present no swaps that means the solution is stable. For our BS (bootstrapping analysis) analysis, the resulting mapping values met the criterion values (according to the PMF guideline document) for which it can be concluded that the number of factors is appropriate. [PERSON] et al. [49] estimated that about 30% of the PM\({}_{10}\) mass at the urban roadside site is generated by local road traffic emissions in Bern, Switzerland. Mineral matter (43%), secondary inorganic aerosol (SIA) (17%), organic matter and elemental carbon (31%) contribute to the PM10 mass in the urban location of Granada, Spain [50]. [PERSON] et al. in Palermo, Italy, found that road traffic contributed almost 50% of the PM [51]. Road traffic, specifically emissions from vehicles, the suspension of dust due to vehicular movement, and tire break wear, add significantly to the PM\({}_{10}\) around traffic sites [51; 52; 53; 54]. Figure 8 presents the comparison of the modelled PM\({}_{10}\) and measured PM\({}_{10}\). Figure S2 shows the predicted versus the observed concentration of chemical species. Figure 6: Factor fingerprints. Figure 7: The monthly contribution of emission sources to PM\({}_{10}\) at traffic-dominated station in Krakow in measured period 2018 and 2020. Table 2 presents the results of the sources of contribution to the PM in the different urban areas.
# Molecular Targets of Minor Cannabinoids in Breast Cancer: In Silico and In Vitro Studies
## 4 Materials and Methods
### Anti-Aromatase Activity  
Considering the molecular docking results, the anti-aromatase activity of the nine selected minor cannabinoids, cannabigerol (**CBG**), cannabidivarin (**CBDV**), cannabibiol (**CBN**), cannabidiol-C4 (**CBDB**), cannabidiotic acid (**CBDA**), cannabidiol monomethyl ether (**CBDM**), cannabichromenic acid (**CBCA**), cannabigerovaniric acid (**CBGVA**), and cannabichromene (**CBC**; Figure 2), was assessed through a radiometric assay by measuring the tritinated water released from [1\(\beta\)-3H]-androstenedione (PerkinElmer Life Sciences, Boston, MA, USA), using human placental microsomes, according to [PERSON] [127] and [PERSON] et al. [128] modified methods [26]. This radiometric assay is the one recommended by OCSPP Guideline 890.1200 for aromatase activity assessment [129]. Despite the previously reported anti-aromatase activity of **CBD**[65], it was also assessed here at the same concentration used for the minor cannabinoids for comparison purposes. Each compound was diluted in a 67 mM potassium phosphate buffer (pH 7.4) and the aromatization reaction was performed using 20 ug of microsomal protein, 15 uM of NADPH, 40 nM of [1\(\beta\)-3H]-androstenedione, and 10 uM of each phytocanabinoid in a final reaction volume of 500 uL at 37 \({}^{\circ}\)C, during 15 min, as previously described [26]. Then, the samples were transferred to scintillation tubes containing a liquid scintillation cocktail (ICN Radiochemicals, Irvine, CA, USA), and scintillations were then counted in a liquid scintillation counter (LKB Wallace 1209 Rackbeta, LKB Wallace, Turku, Finland). The tritinated water released from [1\(\beta\)-3H]-androstenedione was used as an index of estrogen formation [26].  
Stock solutions of each cannabinoid were prepared in 100% DMSO and stored at \(-\)20 \({}^{\circ}\)C. All the experiments were performed in triplicate in at least three independent experiments. Ana, Let, and Exe at 1 uM were used as reference AIs.