esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Deterministic Analysis Method
### Verification  
The developed FDM-based deterministic analysis method is verified with the Timoshenko-Winkler-based closed-form analytical solution which is capable of evaluating the longitudinal responses of a tunnel lying on heterogeneous strata with two discontinuities [36]. A scenario of an existing tunnel under-crossed by a new tunnel is hypothesized for verification, as shown in Figure 3. The subgrade reaction coefficient \(k(x)\) in the scenario is given in Equation (31). The diameters of the new tunnel and the existing tunnel are both set to 6 m. The buried depths of the existing and new tunnels are assumed as 12 m and 21 m, respectively, and the skew angle between the existing and new tunnel is 90\({}^{\circ}\). The volume loss caused by the new tunneling is set to 1%, and the Poisson's ratio of the soil is 0.3. The equivalent longitudinal flexural and shearing stiffness of the existing tunnel are assumed as \(1.2\times 10^{5}\) MN\(\cdot\)m\({}^{2}\) and \(2\times 10^{3}\) MN, respectively.  
\[k(x)=\left\{\begin{array}{ll}10&(x\leq-5\rm m)\\ 5&(-5\rm m<x\leq 5\rm m)\ \ MN/m^{3}\\ 10&(x>5\rm m)\end{array}\right. \tag{31}\]  
Figure 4 compares the new tunneling-induced longitudinal settlement, bending moment, and shear force of the existing tunnel calculated by the analytical solution and the proposed analysis method. The results obtained from the proposed method agree well with those by the analytical solution, thereby verifying the accuracy of the method. It is noted that the tunnel responses affected by spatial variability of soil properties, as in the field, are more complex and cannot be evaluated with the analytical solution that is used here, considering only few discontinuities in the strata [26]. Therefore, the FDM-based analysis method proposed in Section 2.2 should be used in the following analysis.  
Figure 3: Hypothetical scenario of an existing tunnel under-crossed by a new tunnel.
# Economic and Environmental Impact of Energy Saving in Healthcare Buildings
## 3 Results
### Optimization of Lighting System  
Lightening consumption in the Spanish health sector is about 1000 GWh/year, which means 0.6% of the national energy consumption, with an emission of 600,000 annual tons of CO\({}_{2}\)[31]. Therefore, it is  
Figure 3: Potential of energy saving derived from DWH installations.  
very important to use efficient lighting with high-performance lights, incorporating low consumption equipment and high-regulation lumen-per-watt lamps.  
Lighting installation in a healthcare building must take two aspects into account: a qualitative aesthetic aspect and a quantitative engineering character. The aesthetic issue guarantees a friendly space and distribution of lights and shade inside. The technical issues control the amount of light needed for each activity in the different rooms and/or buildings [32]. Clearly, deficient levels of light could cause professionals to make mistakes in diagnoses and dispensing errors [33].  
With the objective of determining the positive economic and environmental impacts thanks to optimizing lighting systems, the annual average saving and investment for the whole set of buildings was quantified for a three-year period.  
Figure 4 shows the potential average energy saving per built surface area (m\({}^{2}\)), by means of improving lighting system performance, in healthcare buildings with less than 5000 m\({}^{2}\) (no beds) and hospitals larger than 5000 m\({}^{2}\) (with beds). An annual average energy saving of 0.12 kWh and 0.15 kWh per built surface area (m\({}^{2}\)), with a payback of less than 2.2 years, was achieved, respectively.  
The specific energy saving measures applied were installation of electronic ballast in fluorescent lamps, replacement of fluorescent and incandescent lamps by light-emitting diodes (LEDs), organization of lighting according to activity type, and the limitation of intensity and timing of the controlled lighting by using occupancy sensors.
## 9 Global Perturbation Bounds  
Since analytical expressions for the global perturbation bounds of the singular value decompositions are unknown up to this moment, we present an iterative procedure for finding estimates of these bounds based on the asymptotic analysis presented above. This procedure is similar to the corresponding iterative schemes proposed in [26] but is more complicated since determining bounds on the parameter vectors \(x\) and \(y\) must be performed simultaneously because the equations for these parameters are coupled.
###### Abstract  
This study proposes an improved few-shot learning model of the Siamese network residual Visual Geometry Group (VGG). This model combined with time-frequency domain transformation techniques effectively enhances the performance of across-load fault diagnosis for induction motors with limited data conditions. The proposed residual VGG-based Siamese network consists of two primary components: the feature extraction network, which is the residual VGG, and the merged similarity layer. First, the residual VGG architecture utilizes residual learning to boost learning efficiency and mitigate the degradation problem typically associated with deep neural networks. The employment of smaller convolutional kernels substantially reduces the number of model parameters, expedites model convergence, and curtails overfitting. Second, the merged similarity layer incorporates multiple distance metrics for similarity measurement to enhance classification performance. For cross-domain fault diagnosis in induction motors, we developed experimental models representing four common types of faults. We measured the vibration signals from both healthy and faulty models under varying loads. We then applied the proposed model to evaluate and compare its effectiveness in cross-domain fault diagnosis against conventional AI models. Experimental results indicate that when the imbalance ratio reached 20:1, the average accuracy of the proposed residual VGG-based Siamese network for fault diagnosis across different loads was 98%, closely matching the accuracy of balanced and sufficient datasets, and significantly surpassing the diagnostic performance of other models.  
few-shot learning model; across-load fault diagnosis; limited data; signal processing; induction motors +
Footnote †: journal: Applied sciences
# Force Analysis of Circular Diaphragm Wall Based on Circular Cylindrical Shell Theory
## 3 Force Analysis of Circular Diaphrag Wall
### Horizontal Earth Pressure on the Pit-Back Surface  
As shown in Table 2 and Figure 4, the horizontal earth pressure \(e_{\rm an}\) and \(e^{\prime}{}_{\rm an}\) on the pit-back surface of the circular diaphragm wall could be calculated through the Rankine active earth pressure theory by considering earth and water pressure together. The Rankine active earth pressure calculation process for cohesive soil is shown as follows.  
The earth pressure strength of Layer 1 can be obtained by Equations (4) and (5).  
\[e_{\rm a0}=q_{0}\tan^{2}\left(45^{\circ}-\frac{\phi_{1}}{2}\right)-2c_{1}\tan \left(45^{\circ}-\frac{\phi_{1}}{2}\right) \tag{4}\]  
\[e_{\rm a1}=(q_{0}+\gamma_{1}\alpha_{1})\tan^{2}\left(45^{\circ}-\frac{\phi_{1} }{2}\right)-2c_{1}\tan\left(45^{\circ}-\frac{\phi_{1}}{2}\right) \tag{5}\]  
The earth pressure strength of Layer 2 can be obtained by Equations (6) and (7).  
\[e^{\prime}{}_{\rm a1}=(q_{0}+\gamma_{1}\alpha_{1})\tan^{2}\left(45^{\circ}- \frac{\phi_{2}}{2}\right)-2c_{2}\tan\left(45^{\circ}-\frac{\phi_{2}}{2}\right) \tag{6}\]\[e_{\alpha 2}=(q_{0}+\gamma_{1}\alpha_{1}+\gamma_{2}\alpha_{2})\tan^{2}\biggl{(}45^{ \circ}-\frac{\phi_{2}}{2}\biggr{)}-2c_{2}\tan\biggl{(}45^{\circ}-\frac{\phi_{2} }{2}\biggr{)} \tag{7}\]  
\[\ldots\ \ldots\]  
The earth pressure strength of Layer \(n\) can be obtained by Equations (8) and (9).  
\[e^{\prime}{}_{\alpha(n-1)}=(q_{0}+\gamma_{1}\alpha_{1}+\gamma_{2}\alpha_{2}+ \ldots+\gamma_{n-1}\alpha_{n-1})\tan^{2}\biggl{(}45^{\circ}-\frac{\phi_{n}}{2} \biggr{)}-2c_{n}\tan\biggl{(}45^{\circ}-\frac{\phi_{n}}{2}\biggr{)} \tag{8}\]  
\[e_{\rm an}=(q_{0}+\gamma_{1}\alpha_{1}+\gamma_{2}\alpha_{2}+\ldots+\gamma_{n} \alpha_{n})\tan^{2}\biggl{(}45^{\circ}-\frac{\phi_{n}}{2}\biggr{)}-2c_{n}\tan \biggl{(}45^{\circ}-\frac{\phi_{n}}{2}\biggr{)} \tag{9}\]  
In Equation (9), \(q_{0}\) represents the applied load above the calculation surface; \(\gamma_{n}\) represents the bulk density of the rock or soil at Layer \(n\); \(\phi_{n}\) represents the internal friction angle of the rock or soil at Layer \(n\); and \(c_{n}\) represents the cohesion of the rock or soil at Layer \(n\). For in cohesive soil, the cohesion \(c_{n}\) is just set to 0.
## 3 Results
### Quantification of TPC and TFC  
As shown in Table 2, the LC-10 sample, which overexpressed the \(ZmLC\) gene, showed a significantly higher TPC (28.07 \(\pm\) 0.22 GAE mg/g DW) and TFC (42.35 \(\pm\) 0.67 QE mg/g DW) (Figure 4), whereas the control showed a lower TPC (19.92 \(\pm\) 1.66 GAE mg/g DW) and TFC (30.55 \(\pm\) 0.15 QE mg/g DW). Based on these results, we can conclude that the sample overexpressing the \(ZmLC\) gene contains more TPC and TFC than the control.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Compound** & **Control** & **LC-10** & **Fold Change** \\ \hline
1 & Caffeic acid & 0.687 \(\pm\) 0.015 & 0.951 \(\pm\) 0.026 & 1.38 \\
2 & \(p\)-coumaric acid & 0.005 \(\pm\) 0.001 & 0.022 \(\pm\) 0.001 & 4.40 \\
3 & Ferulic acid & 0.01 \(\pm\) 0.001 & 0.011 \(\pm\) 0.004 & 1.10 \\
4 & Benzoic acid & 0.141 \(\pm\) 0.007 & 0.148 \(\pm\) 0.003 & 1.05 \\
5 & Rutin & 0.29 \(\pm\) 0.001 & 0.292 \(\pm\) 0.003 & 1.01 \\
6 & Trans-cinnamic acid & 0.004 \(\pm\) 0.001 & 0.012 \(\pm\) 0.001 & 3.00 \\
7 & Rosmarinic acid & 24.4 \(\pm\) 0.394 & 26.803 \(\pm\) 0.635 *** & 1.10 \\
8 & Acacetin & 0.007 \(\pm\) 0.001 & 0.03 \(\pm\) 0.003 & 4.29 \\ \hline \hline \end{tabular} All results were carried out in triplicate, and the values are shown as mean \(\pm\) SD. Asterisks denote statistical significance (***\(p\)\(<\) 0.001). The column without asterisks denotes no significant differences between the control and LC-10.  
\end{table}
Table 1: Effects of overexpressing the \(ZmLC\) transcription factor on the phenylpropanol content (mg/g DW) in _A. rugosa_ extracts.  
Figure 4: Correlation matrix using correlogram between the phenylpropanol biosynthetic pathway genes and identified metabolites in \(ZmLC\)-overexpressing _A. rugosa_ transgenic hairy root line. The correlation values are shown as a heatmap in the bottom left, using colour boxes to denote the strength of association, and the pie chart shown in the top right provides a quick overview.
## 3 Methodology
### Design of Data-Computing Engine
#### 3.3.2 Execution of Data-Computing Tasks  
After all computing tasks are generated, they can be prepared for execution, as shown in Figure 8. All computing tasks are stored in the task queue in order of priority. If the computing tasks have the same priority, they can be executed at the same time. Otherwise, the tasks with higher priority are executed first. When a task obtains the execution right, the resource files (such as data resources, data-processing method resources, etc.) required by the computing task are first synchronized from the data service container to the specified computing server. Due to the possibility of multiple tasks running simultaneously on a computing server, when system resources are insufficient to support the current task, it can lead to task failure. Therefore, before executing each task, it is necessary to determine whether the current server resources can support its execution. If the judgment result is true, the computing task can be started. Otherwise, the task will continue to wait for the appropriate execution time.  
In order to avoid the interference between different computing tasks, this paper proposes a virtual computing container to manage the resources involved in the running of each computing task, including hardware resources such as CPU, memory, hard disk space, and related software resources such as Java runtime. The container process will monitor these software and hardware environments to ensure the correct execution of computing tasks. It also reports to the computing engine in real time when system resources are insufficient. In addition, the container process also manages the executing process and the  
Figure 7: The XML description of data-computing tasks.  
monitoring process, with the former responsible for running computing tasks and the latter responsible for monitoring task execution.  
When a data-computing task is started, its entire execution period is monitored by the monitoring process. The monitoring process determines the running status of the computing task by obtaining its status information (e.g., running, exception, finished, etc.). When a computing task encounters an exception, the monitoring process will check the running logs to determine the cause of the exception. For example, if the exception is due to insufficient system resources (such as memory, hard disk size), the monitoring process will re-run the task at the appropriate time. If the exception is caused by illegal input of a computing task, the monitoring process will directly report the exception to the user. In this case, all computing tasks that are executed after this computing task are suspended until the user reconfigures correct data for this computing task.
## 3 Results
### Earthquake Probability Assessment  
A probability assessment was conducted using the RNN model to predicts earthquake (1) and non-earthquake (0) classifications. The model was unable to predict 50 earthquake data points out of 1800 events. Due to heterogeneity and irrelevant values obtained for some pixels in the thematic layers, the model predicted 1750 events out of 1800 events. In total, 1850 points were predicted as earthquakes, while 850 were non-earthquakes. The RNN model predicted 48900 km\({}^{2}\) as the highest earthquake probable location in Odisha. Training (70%) and testing (30%) were conducted for 2700  
Figure 7: SSA assessment for Odisha.  
points, where 1800 were earthquakes and 900 were non-earthquakes. The RNN model achieved 94% accuracy. The accuracy was estimated as 0.94, whereas as precision was 0.94, recall was 0.97, F1 score was 0.96, TS/CSI was 0.921053, and FM was 0.95. The probability map is presented in Figure 8. Accuracy is plotted through ROC (Figure 9a) and every true negative event is plotted in Figure 9b. All unpredicted earthquakes showed no discernible pattern.  
The probability of future earthquake occurrence is higher in the northwestern parts of the state, as shown in Figure 8. However, the northwestern part of Odisha has already experienced moderate magnitude events of 5.3 Mw due to the NOBF active fault system. Four sections of the very high probable areas were shown within the map, showing that NOBF has the capability to strike high magnitude events in the future. NOBF extends from the east to the west, having a strike length of 250 km, whereas width ranges from 2-5 km. The NOBF is not uniform and is rather characterized by interdigitation of rocks, which makes northern Odisha irregular with complex geo-structures. Therefore, high probability in the northwestern and southern Odisha can be associated with NOBF. Medium probability was characterized as having high probability, whereas low probability could be expected in east and central Odisha. The districts under very high (0.85-1) to high (0.65-0.85) probability includes Sundargarh, Jharsuguda, Sambalpur, Bargarh, Subarnapur, Balangir, Nuapada, Kalahandi, and Nabarangpur. All other districts fall in the moderate to very low categories. Approximately 33.46% and 52,014 km\({}^{2}\) of Odisha fall in the very high to high category (Table 4).
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 2 Materials and Methods
### Phantom Acquisition  
#### 2.3.1 Translation Stage  
Constrained SSA was achieved with a known ground truth motion profile using a Newport UTM100 linear translation stage (Newport, Irvine, CA, USA). The P4-2v transducer was attached to the stage using a custom molded holder such that the array direction was aligned with the direction of motion. The stage was programmed over a serial communication interface, and motion was synchronized with the imaging pulse sequence described below.  
Figure 2: Sample pulse sequences for correlation-based swept synthetic aperture imaging using a 500 Hz frame rate and 260 μs between pulses to allow propagation and data acquisition. The ellipses represent a gap between transmissions. (**a**) Alternating plane and diverging waves with plane waves to provide highly correlated data and diverging waves to provide a wide acceptance angle for coherent combination. (**b**) Diverging waves transmitted from each edge instead of the center to maximize the effective transmit aperture extent.
## 4 Results
### 2D Inversion Models
The thickness of **R1** is almost the same in the north-east of the rift for both the TM and invariant models (Figure 9) but at the center of the rift and in the south-west, the invariant model shows a relative increase in the thickness of layer **R1**. While both models (TM and invariant) seem to have **C1** layer of comparable width, it is a little thicker in the TM model. This high conductivity **C1** layer can be the result of fluid trapped in the host rock. The **C2** and **C3** layers have a wider extension in the TM model than in the invariant model and their relative high conductivity could be interpreted as aquifers. The resistivity contrast between **R2** and **R3** can be related to the difference between a high resistive rock R3, which was perturbed by a localized hot ascandant material originating from the center of the rift that could be the expression of a magmatic fluid or melt having lower resistivity R2. Comparing inversion results, the TE model and joint inversion of TE-TM model resolve better the conductive features (Figure 9 and Appendix A), while the invariant model and the TM model may resolve in reasonable way the conductive features (Figure 9). The same pattern and characteristic of TE, TM, joint inversion of TE-TM and invariant models was found by [43]. Moreover, the invariant model is less affected by galvanic distortion and is independent of the value of geoelectric strike and would be the best model. Additionally, previous studies concluded that stable TE inversion model or joint inversion model of the TE and TM data can better resolve both the resistive and conductive feature [32,42] and similar conclusions can be inferred from the results illustrated in Figure 9.  
Results of two joint inversion of TE-TM data, one rotated to the geoelectric strike N45\({}^{\circ}\)E (Figure 9) and another one rotated to the geoelectric strike N120\({}^{\circ}\)E (Appendix A) display high conductivity areas at great depth. Those results with the 2D inversion models of TM, invariant and the TE model (Figure 9) have some similarities and consequently, regional 2D conductivity structure approximation can be considered to explain the subsurface geoelectric structure beneath the Asal rift.
#### Near-Infrared Spectroscopy in Assessing Salivary Biomarkers  
NIRS could be potentially used as a non-invasive intra-oral analysis technique. In vivo studies of the oral cavity should account for the presence of saliva and consider it as diagnostic fluid complementing the information gathered from the oral mucosa [34; 35; 36; 37; 38; 39]. The use of saliva instead of blood or serum in diagnosing various conditions, such as cancers [30; 31; 35; 40; 41], diabetes [20], oral leukoplakia [35; 40], Sjogren's syndrome [36], or testing for COVID-19 [42; 43; 44], is a growing area of research. Saliva consists of 94-99% of water [34; 42] but it also contains biomarkers, including locally expressed proteins and substances, such as DNA, RNA, mucins, amino acids, enzymes, and primary metabolites. The concentrations of saliva biomarkers change with different diseases [20; 35; 46; 47]. Compared to blood and serum, saliva has a less complex and varying composition [20; 35]  
Oral diseases cause abnormal changes in oral mucosal tissue. Oral epithelial cells constantly shed into the saliva [35; 48], and oral cancer cells start to clear into the saliva at the early stages of cancer [35; 48]. Saliva can exchange substances with systemic circulation via diffusion or active transport due to its direct contact with oral mucosa [35; 36; 48; 49]. Collecting saliva samples is easier (e.g., feasible at home), less expensive, less uncomfortable for the patients, and safer (excludes the risk of needle stick) than collecting blood samples or biopsies, thereby allowing the increased use of saliva as diagnostic fluid in the future, especially for a screening of potential diseases. However, to date, the biomedical studies of NIRS have mainly focused on studying skin [15; 16; 17; 18; 29] or cartilage [50; 51; 52]. [PERSON] et al. [38] utilized NIRS to study saliva of oral cancer patients and healthy controls with the capillary tube Fourier transformation-NIR (FT-NIR) [53]. However, no biomarkers were investigated in their study. Other NIR studies of saliva focus on hemodynamic signals [54; 55] or forensic applications [56]. However, the previous studies do not utilize NIRS to characterize saliva with the known oral cancer biomarkers.  
A previous nuclear magnetic resonance (NMR) study by [PERSON] et al. [45] studied potential salivary biomarkers of oral cancer. Their study showed that oral cancer patients exhibited significantly increased concentrations of salivary L-fucose while salivary L-proline levels were significantly decreased compared to healthy controls [36; 45]. However, saliva has high water content [34; 35; 36; 45], and water is a strong NIR absorber. In this proof-of-concept study, we aimed to identify the spectral regions for discriminating solutions with different concentrations of L-fucose and L-proline and water. These regions could be further utilized in salivary diagnostics to recognize and discriminate between healthy, patients suffering from premalignant conditions, and cancerous patients based on saliva samples.