esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cultivar Discrimination
The scientific value and industrial benefit of this study lies on the detection of specific regions of the Vis/NIR electromagnetic spectrum or even distinct single wavelengths that can be incorporated in models and operate in portable devices or station based screening equipment. Thus, potatoes may be classified into separate cultivars and decisions regarding postharvest management and retail distribution be made in relation to their specific requirements and consequently even affect the quality of the processed product in the potato chip industry. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline  & & \multicolumn{6}{c}{Calibration} & \multicolumn{6}{c}{Cross-Validation} \\ \cline{3-10}  & & \multicolumn{3}{c}{**Spunta**} & **Banba** & **Sylvana** & \multicolumn{3}{c}{**Spunta**} & **Banba** & **Sylvana** \\ \hline
**Technique** & \multicolumn{3}{c}{**NER***} & \multicolumn{3}{c}{**Number of Fruit**} & \multicolumn{3}{c}{**NER**} \\ \hline VisNIR spectra (400-1000 nm) & Outside & 99 & 44 & 45 & 45 & 93 & 42 & 44 & 40 \\  & Inside & 96 & 44 & 40 & 45 & 95 & 42 & 42 & 44 \\ VisNIR spectra (458-516, 546-662, 721-809, 956-984 nm) & Outside & 97 & 43 & 44 & 44 & 91 & 41 & 43 & 39 \\  & Inside & 99 & 43 & 45 & 45 & 94 & 42 & 42 & 43 \\ Color (l, C, H) & Outside & 66 & 19 & 42 & 28 & 66 & 20 & 41 & 28 \\  & Inside & 61 & 18 & 39 & 25 & 60 & 17 & 39 & 25 \\ (NIR-G/NIR+C), (NIR-B/NIR+B), Fi, Fe & Outside & 78 & 33 & 42 & 30 & 76 & 33 & 40 & 30 \\  & Inside & 60 & 19 & 38 & 24 & 61 & 19 & 38 & 25 \\ \hline \end{tabular}  
* NER: Non-Error Rate = Accuracy of classification (C) = (Currently classified samples/total samples) \(\times\) 100.  
\end{table} 
Table 2: Calibration and cross-validation classification of correctly discriminated potatoes into the three cultivars (Spunta, Banba, or Sylvana) using the partial least squares discriminant analysis (PLSDA) classifier from color, digital imaging, and chlorophyll fluorescence as well as Vis/NIR spectroscopy data of the whole 400-1000 nm region, as well as from the selected regions upon the genetic algorithm implementation.
## 2 Background
### Deployment in Wireless Sensor Network
#### 2.5.2 Random Deployment  
When the sensing area is difficult to reach (to put sensors) or not known in advance, due to disasters, fire forests, or battlefields, then the random deployment is preferred, i.e., randomly dispersing the nodes in the desired area, which can be thrown from a plane in a disaster area, for example. This method is simple but, at the same time, has many drawbacks, such as bad coverage or loss of connectivity due to obstacles or failures. So many optimization strategies are used to find the best location, taking the requirements of coverage, connectivity, lifetime, and robustness into account to achieve one of these objectives or some of them simultaneously. Classical, heuristic, and meta-heuristic optimization algorithms are used to solve the random deployment problem of WSNs. Random deployment can be represented in Figure 6.
## 3 Developed Methodology
### Numerical Model Preparation  
The basis of Finite Element Analysis(FEA) analysis is the CAD model, and depending on the design stage, the model has different levels of detail. So, it was necessary to elaborate a process after which the input geometry for analysis will mostly be the same. This issue forced the authors to add additional steps that proceed with the central part of the study.  
To unify the input geometry, the authors decided that the CAD model must be adequately prepared in the preprocessing stage. This preparation includes:  
* geometry simplification;
* removing all unnecessary chamfers and filets as well as holes that will be smaller than the mesh size;
* removing all unrelated components such as fasteners, additional equipment, etc.  
After the simplification process, the model must be divided into segments that correspond to the composite structure. An example of the simplification and segmentation process is shown in Figure 9.  
In the segmentation process, each composite structure acquires a unique color code, which allows to quickly interpret the model material structure and define the material properties in the future steps of the analysis. The final phase of the geometry preparation is the extraction of the mid-surface for each component. This process provides the consistency geometry input for the loads and constraints definition in the later stages.  
In general, composite structures can be calculated using a few different approaches. In the basic strategy, the layer modelling technique is used, that using 2D or 3D finite elements, is dominant. In the presented method, the authors decided to use only 2D shell elements. This decision is since in thin-layer composites, the thickness is the smallest dimension, and the ratio to the other dimensions is quite large.  
The next step in the numerical model preparation process is a simplification of contact areas, especially where ribs or other elements are joined to outer structures. In case for  
Figure 9: An example of the simplification and segmentation process. On the left is the model before the process, and the right is the model after the process.  
a perpendicular connection, the authors reduced the connection to a single component which contains layers of both base laminate and rib laminate. An example of the contact area simplification is presented in Figure 10.  
In case of a parallel connection, simple nods joining is enough and there is no need to create a new component, as in the case of perpendicular connections. After this stage, the discrete model was ready to apply loads and constraints.
## 5 Conclusions  
This study focused on the relationship between slope state and crack development in fissured expansive soils and used a series of physical model tests to consider different types of cracks, different overburden loadings and irrigation. The main conclusions are as follows:  
Overburden pressure or gravity stress results in tensile strain in the middle and compressive strain at the foot of the expansive soil slope without prefabricated cracks. Water infiltration promotes the extension of tensile strain on the surface of the slope.  
Excessive overlying stress revives the existing cracks and produces sliding along the crack interface of an expansive soil slope with one crack. Additionally, water infiltration promotes the formation of a shallow sliding surface.  
The sliding surface commonly appears where crack networks exist in the shallow of an expansive soil slope. Water infiltration promotes the extension of tensile strain on the surface of the slope with crack networks, which intensifies the formation of surface- or shallow-layer sliding surfaces.  
The middle and foot of the fissured expansive soil slope are the key positions for reinforcement, which were revealed by the mutual transformation of tensile strain and compressive strain on the surface of the slope.  
The controlling effects of cracks on strain continuity, stress transfer and the stability of the expansive soil slope were revealed from the perspective of slope strain development under different loadings and irrigation.  
Water infiltration has a great influence on the stability of the slope surface; more importantly, it causes the sliding surface of the fissured expansive soil slope to be shallower.  
Because the water used in the test was plain water, there was no color mark, so the flow of water could not be seen in the seepage process. In future studies, colored water or tracers could be used for seepage tests to track fluid flow through cracks.  
The quantitative study of the effect of cracks on slope stability should be the focus of future work.  
Conceptualization, Z.L.; Data curation, Z.L. and Y.K.; Formal analysis, Z.L. and L.F.; Investigation, Z.L., Y.Z. and Z.Q.; Methodology, Z.L. and R.H.; Project administration, Z.L.; Validation, Z.L.; Writing--original draft, Z.L.; Writing--review and editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Plan of China (Grant No. 2019 YFC1509903), the Second Tibetan Plateau Scientific Expedition and Research Program (STEP) (Grant No. 2019 QZKK0904), National Natural Science Foundation of China (Grant No. 42177146, 41790442), Key Research and Development Plan of Yunnan Province (Grant No. 202103 AA080013), Chinese Academy of Sciences Key Deployment Project (Grant No. KFZD-SW-422), Youth Innovation Promotion Association CAS (Grant No. 2017092) and the International Cooperation Program of Chinese Academy of Sciences (Grant No. 131551 KYSB20180042).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Waste-heat recovery (WHR) systems based on the organic Rankine cycle (ORC) improve the thermal efficiency of natural gas engines because they generate additional electric power without consuming more gas fuel. However, to obtain a cost-effective design, thermoeconomic criteria must be considered to facilitate installation, operation, and penetration into real industrial contexts. Therefore, a thermo-economic analyses of a simple ORC (SORC), ORC with recuperator (RORC) and a double-pressure ORC (DORC) integrated with a 2 MW Jenbacher JMS 612 GS-N. L is presented using toluene as the organic working fluid. In addition, the cost rate balances for each system are presented in detail, with the analysis of some thermoeconomics indicator such as the relative cost difference, the exergoeconomic factor, and the cost rates of exergy destruction and exergy loss. The results reported opportunities to improve the thermoeconomic performance in the condenser and turbine, because the exergoeconomic factor for the condenser and the turbine were in the RORC (0.41 and 0.90), and DORC (0.99 and 0.99) respectively, which implies for the RORC configuration that 59% and 10% of the increase of the total cost of the system is caused by the exergy destruction of these devices. Also, the pumps present the higher values of relative cost difference and exergoeconomic factor for B1 (rk = 8.5, fk = 80%), B2 (rk = 8, fk = 85%).  
thermoeconomic analysis; cost balance; organic Rankine cycle; waste heat recovery; natural gas engine; exergoeconomic factor; relative cost difference
## 2 Problem Formulation and Framework  
### Problem Formulation  
The main challenge in ship system-level health condition prognosis is the development of a modeling framework that allows the various factors influencing the evolution of system degradation to be taken into account, including the mutual interactions of the components, the impact of uncertainty, and O&M requirements. Before establishing the method for dynamic reliability assessment and health prognostics, some general specifications regarding the considered ship mechanical system are presented as follows:  
(1) The ship mechanical system is an agent or intelligent unit composed of various components used to complete specific functions. From the perspective of intelligent O&M, the health prediction focuses on whether the system can meet the functional requirements.  
(2) The system proposed in this study is a continuous-operation system. The system does not stop for maintenance, and the prediction end point is that the system function is lost or does not meet the actual demand.  
(3) There are many state variables of the ship mechanical system that often lack dominant failure characteristics. The ship mechanical systems consist of \(m\) components, the state of each component at time \(t\) is denoted as \(x_{i,t}\), and the states of all components are \(x_{i,t}=(x_{1,t},x_{2,t},\ldots,x_{m,t})\). When the system is in the process of degradation, a single \(x_{i,t}\) cannot accurately describe the health condition of the system. Therefore, it is necessary to integrate multiple sensor information to form an SHI to represent the health condition.  
(4) The system works alternately in a variety of working modes. When the modes are different, the optimal value (baseline value) and limit (threshold) value of the same variable will be significantly different. Therefore, the influence of different mode parameters must be considered when quantifying health status.  
(5) The health conditions of the ship mechanical system are mainly affected by three factors. One factor is the global degradation caused by uncertainty. The second factor is the local self-healing influenced by the switching, self-clearing, and recoil of some devices and components. When the equipment in the system recovers some performance, the function of the whole system will also recover to a certain extent. This phenomenon has a great impact on system failure and should be considered in the prediction process. The third factor is local fluctuations caused by wind, waves, current, and other instantaneous disturbances.
# A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging
## 3 Results  
### Phantom Study Results  
Phantom studies simulated two lung cancer patients treated with a five-fraction SBRT technique. About 15,000 images were acquired and analyzed in the phantom study. Markers were successfully detected and removed. Tumors were identified on every single image even in the presence of strong signals caused by the surrounding anatomical structures.  
Figure 4 illustrates the lung tumor trajectories in the SI (Figure 4a) and orthogonal (Figure 4b) directions detected during regular motion R20 at GA = 330\({}^{\circ}\). From this angle, the tumor projection is very close to the spine projection making it difficult to detect tumors. The black dash lines correspond to the sinusoidal motion detected by markers, and the red solid lines are the differences between the detected positions and the corresponding actual positions. 2D motions could be evaluated by using motions in both directions. The maximum 2D deviation experienced in this case was 1.84 mm, while the average deviation was within 0.98 mm. The deviation studies were performed on the tumor-tracking results for both large and small tumor phantoms at nine gantry angles when the tumors were moved with five different ranges. Details are listed in Tables A1 and A2 in the Appendix A. Overall, the large tumor phantom results had a maximum 2D error of 1.84 mm, while the average 2D error was 0.98 mm. The small tumor phantoms results had a maximum 2D error of 1.75 mm, while the average 2D error was 0.98 mm.  
Figure 4: Lung tumor trajectory detected for a single fraction in two directions, (**a**) for superior–inferior (SI) and (**b**) for orthogonal direction against the expected trajectory used as ground truth for a gantry angle of 330\({}^{\lx@math@degree}\) with regular motion R20 (range of 2 cm). The difference between the detected and the expected tumor positions are indicated for every frame by the squiggly red line.
###### Abstract  
The imidazolium-based ionic liquids (ILs) are solvents known for selectively solubilizing CO\({}_{2}\) from a gas CH\({}_{4}\)/CO\({}_{2}\) mixture, hence we have produced new hybrid adsorbents by immobilizing two ILs on xerogel silica to obtain a solid-gas system that benefits the ILs' properties and can be industrially applied in CO\({}_{2}\) capture. In this work, the ILs (MeO)\({}_{3}\)5 ipmim.Cl and (MeO)\({}_{3}\)5 ipmim.Tf\({}_{2}\)N were used at different loadings via the sol-gel process employing a based 1-methyl-3-(3-trimethoxysylisopropyl) imidazolium IL associated to the anion Cl\({}^{-}\) or Tf\({}_{2}\)N\({}^{-}\) as a reactant in the synthesis of silica xerogel. The CO\({}_{2}\) adsorption measurements were conducted through pressure and temperature gravimetric analysis (PTGA) using a microbalance. SEM microscopies images have shown that there is an IL limit concentration that can be immobilized (ca. 20%) and that the xerogel particles have a spherical shape with an average size of 20 \(\upmu\)m. The adsorbent with 20% IL (MeO)\({}_{3}\)5 ipmim.Cl, SILCLX20, shows greater capacity to absorb CO\({}_{2}\), reaching a value of 0.35 g CO\({}_{2}\)/g adsorbent at 0.1 MPa (298 K). Surprisingly, the result for xerogel with IL (MeO)\({}_{3}\)5 ipmim.Tf\({}_{2}\)N shows poor performance, with only 0.05 g CO\({}_{2}\)/g absorbed, even having a hydrophobic character which would benefit their interaction with CO\({}_{2}\). However, this hydrophobicity could interfere negatively in the xerogel synthesis process. The immobilization of ionic liquids in silica xerogel is an advantageous technique that reduces costs in the use of ILs as they can be used in smaller quantities and can be recycled after CO\({}_{2}\) desorption.  
carbon dioxide; imidazolium ionic liquids; immobilization; xerogel; silica +
Footnote †: journal: Applied Physics Letters B
## 3 Results and Discussion
### Experiments on the Common Benchmark  
The proposed method is comprehensively evaluated on three widely-used tracking benchmarks (OTB100, TC-128, and UAV123). A summary table is presented, as shown in Table 1, to compare our algorithm with other state-of-the-art algorithms, mainly in terms of both success rate (SR) and precision (P).  
In addition, success and precision plots are created, ranking them based on area under the success curve and precision. The success plot shows the relationship between the probability of successfully tracking the target and the probability of false alarms at different success rate thresholds. The precision plot provides a detailed representation of the trade-off between precision and the confidence score threshold in a tracking algorithm. It visualizes how the precision of the algorithm changes as the confidence score threshold varies.
###### Abstract  
In response to the rising volume and sophistication of cyber intrusions, data-oriented methods have emerged as critical defensive measures. While machine learning--including neural network-based solutions--has demonstrated strong capabilities in identifying malicious activities, several fundamental challenges remain. Chief among these difficulties are the substantial resource demands related to data preprocessing and inference procedures, limited scalability beyond centralized environments, and the necessity of deploying multiple specialized detection models to address diverse stages of the cyber kill chain. This paper introduces a contextual bandit-based reinforcement learning approach, designed to reduce operational expenditures and enhance detection cost-efficiency by introducing an adaptive decision boundary within a layered detection scheme. The proposed framework continually measures the confidence of each participating detection model, applying a reward-driven mechanism to balance cost and accuracy. Specifically, each potential action, representing a particular decision boundary, earns a reward reflecting its overall cost-to-effectiveness ratio, thereby prioritizing reduced overheads. We validated our method using two highly representative datasets that capture prevalent modern-day threats: phishing and malware. Our findings show that this contextual bandit-based strategy adeptly regulates the frequency of resource-intensive detection tasks, significantly lowering both inference and processing expenses. Remarkably, it achieves this reduction with minimal compromise to overall detection accuracy and efficacy.  
contextual bandit; cyber threat detection; reinforcement learning; cost-efficiency; layered detection scheme +
Footnote †: journal: Applied Sciences
## EMD_CS_ST Denoising Method
### Soft-Thresholding  
Wavelet threshold denoising is to distribute the energy of MS signals mainly in larger wavelet coefficients, and noise primarily distributed in smaller wavelet coefficients. Based on this distribution characteristic, the denoising can be achieved by converting the time domain signal into the wavelet domain and filtering the noise by setting an appropriate threshold value [36].  
The key of the wavelet threshold is to select the appropriate threshold T to perform threshold quantization on the wavelet coefficients \(w_{i,j}\) of the noisy signal, to be as close as possible to the wavelet coefficient w of the original signal. Processed wavelet coefficients are represented as \(w_{0}\), and wavelet reconstruction is performed to obtain a denoising signal.  
Currently widely used is the data-driven threshold proposed by [PERSON] [36], the expression is as follows:  
\[T_{i}=\frac{median\{|Y_{i}|\}}{0.6745}\sqrt{2\log N},(i=1,2,\cdots,k) \tag{8}\]  
where \(Y_{i}\) is the \(i\)-th IMF component, _median_ is the median function, and \(T_{i}\) is the threshold of the \(i\)-th reconstruction coefficient \(\theta_{i}\).  
Threshold denoising has a hard threshold and a soft thresholding. The hard thresholding function is better than the soft thresholding function in the sense of mean square error, but the signal will produce additional shocks, generate jumping points, and do not have the smoothness of the original signal, which greatly affects the initial pick-up of P wave and S wave of MS signal and the location of MS source. The overall continuity of the wavelet coefficients obtained from the soft threshold estimation is excellent, so that the estimated signal will not generate additional shocks, which is conducive to the later research work. Therefore, soft-thresholding denoising is used in this paper. The expression is:  
\[\theta_{i,new}=\left\{\begin{array}{cl}\text{sgn}(\theta_{i})\cdot(|\theta_ {i}|-T)&|\theta_{i}|\geqslant T\\ 0&|\theta_{i}|<T\end{array},(i=1,2,\cdots,k)\right. \tag{9}\]  
where sgn is a symbol-function, and \(\theta_{i,\text{new}}\) is a reconstruction coefficient after filtering the \(i\)-th IMF component.
## 4 Results  
### UAV Stereo Imagery  
Figure 3 showed the ortho-rectified mosaic images and DSM of UAV stereo imagery acquired in leaf-on and leaf-off seasons over the test site. The reported density of point cloud extracted from the UAV stereo imagery of leaf-on and leaf-off was 32.4 point/m\({}^{2}\) and 35.9 point/m\({}^{2}\), respectively. The UAV trajectory was overlaid on Figure 3a showed as yellow lines. The coverage area was about 1.52 km\({}^{2}\) with a 1.6 km length and 0.95 km width. Both the leaf-on and leaf-off stereo imagery were collected along the same trajectory within one flight with an effective duration of flight of about 12 minutes. One obvious difference between leaf-on and leaf-off was that more roads were visible in Figure 3b than in Figure 3a. There were no harvesting activities that occurred between the two flights, but the snow covered the ground, and the leaves were off during the collection of early spring images.  
The accuracy of GCP was shown in Table 2. The standard deviation of GCP in leaf-off season along the direction from west to east was 0.59 m, while that along the direction from south to north was 0.48 m. The vertical accuracy was 0.8 m. For GCP in leaf-on season, the vertical accuracy was 0.83 m, while the horizontal accuracy was 0.34 m and 0.32 m along the direction from west to east and the direction from south to north, respectively.  
The accuracy of GCP reported in Table 1 indicated the relative co-registration errors between the UAV imagery of Leaf-on and Leaf-off. The absolution positioning error is evaluated by making an accurate co-registration between the DSM of Leaf-on and that of lidar using the method proposed in [40]. The results show that the horizontal offset between the two DSM is about 3.63 pixel (3.63 pixel * 0.5 m/pixel = 1.815 m) and 0.67 pixel (0.67 pixel * 0.5 m/pixel = 0.335 m) along the X and Y directions, respectively. For the field plot with a size of 15 m \(\times\) 15m or even larger, the positioning error was only about 1/10 of plot size, which is acceptable.