esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Life-Cycle Costs (LCC)
### Operation and Maintenance Costs
The undiscounted and discounted cumulative costs (NPV) with regard to the years of operation are shown in Figure 2. It is clear that the cumulative costs of the construction years of the three cases are almost the same because no operation or maintenance costs exist. With the increase of the years of operation, all three cases show linear variations. However, the increased rate of the undiscounted cumulative costs (i.e., i = 0) is much larger than those of the other two cases. In contrast, the cumulative costs with a discounted rate of 4% show an almost constant trend and are much smaller than those with the 1% discount rate. The final cumulative costs at 100 years for the undiscounted case are about 1.5 times those for the discounted case with a discounted rate of 1%. Therefore, the discount rate can have a significant influence on the NPV of a project. In practical applications, this value can be accurately evaluated to provide a reliable cost evaluation.
## 2 Mathematical Formulation of OPF Problem
### Cost Function Formulation for Thermal Generators  
For the operation of thermal generators, they require fossil fuels and their output power depends on the consumption of fossil fuels. The fuel cost ($/h) and output power (MW) of the thermal generators have a relationship which can be expressed as below:  
\[C_{TG} =\sum_{i=1}^{nT}ai+b_{i}+c_{i}P_{TGi}^{2} \tag{1}\]  
where \(C_{TG}\) represents the total cost of \(n\) thermal generators producing \(P_{TG}\) (MW) real power, while \(a_{i}\), \(b_{i}\) and \(c_{i}\) represent fuel cost coefficients for \(i\)th thermal generators whose values are mentioned in Table 2.  
There is a greater divergence in the cost function of thermal generators with multi valve steam turbines. Therefore, for modeling a precise and more accurate cost function for thermal generators, valve point effects must be considered. Valve point effect is a sinusoidal function and must be included in the basic cost of thermal generators as an absolute value. The equation for the cost function, after counting the valve point effect, now becomes:  
\[C_{TG}=\sum_{i=1}^{nT}a_{i}+b_{i}+c_{i}P_{TGi}^{2}+\Big{|}d_{i}\times\sin\Big{\{} e_{i}\times\Big{(}P_{TGi}^{\min}-P_{TGi}\Big{)}\Big{\}}\Big{|} \tag{2}\]  
where \(e_{i}\) and \(d_{i}\) represent the valve point effect coefficients of steam turbines. All cost and valve point coefficients used in Equations (1) and (2) are taken from reference [8] and are summarized in Table 2 along with emission coefficients.
## 3 Results
### Results of Contact Angle Measurements and Surface Free Energy Calculation
The calculated surface free energy from contact angle measurements of water and DIM test liquids and polar and dispersed parts of SFE are presented in Figure 7.  
The SFE was 49.07 \(\pm\) 0.89 mJ/m\({}^{2}\), 45.65 \(\pm\) 0.66 mJ/m\({}^{2}\), and 47.42 \(\pm\) 0.71 mJ/m\({}^{2}\) on polished SDR, TECBF, and TEC composite surfaces, respectively. On the sandblasted surface, the SFE was 48.10 \(\pm\) 0.6 mJ/m\({}^{2}\), 47.70 \(\pm\) 0.46 mJ/m\({}^{2}\), and 46.76 \(\pm\) 0.53 mJ/m\({}^{2}\) on SDR, TECBF, and TEC, respectively. The dispersed parts of SFE were 40.46 \(\pm\) 0.45 mJ/m\({}^{2}\), 42.42 \(\pm\) 0.44 mJ/m\({}^{2}\), and 41.94 \(\pm\) 0.37 mJ/m\({}^{2}\) for polished SDR, TECBF, and TEC, respectively. For sandblasted surface, the dispersed parts of SFE were 44.61 \(\pm\) 0.27 mJ/m\({}^{2}\) at SDR, 45.69 \(\pm\) 0.31 mJ/m\({}^{2}\) at TECBF, and 44.40 \(\pm\) 0.36 mJ/m\({}^{2}\) at TEC. The polar parts of SFE on polished SDR, TECBF, and TEC surfaces were 8.62 \(\pm\) 0.44 mJ/m\({}^{2}\), 3.23 \(\pm\) 0.22 mJ/m\({}^{2}\), and 5.48 \(\pm\) 0.34 mJ/m\({}^{2}\), respectively. The polar parts of SFE on sandblasted SDR, TECBF, and TEC surfaces were 3.49 \(\pm\) 0.33 mJ/m\({}^{2}\), 2.01 \(\pm\) 0.15 mJ/m\({}^{2}\), and 2.37 \(\pm\) 0.18 mJ/m\({}^{2}\), respectively. At every RBC, the dispersed part represented higher fractions of SFE than the polar parts of it. Due to the sandblasting, the dispersed parts increased, opposing the polar parts. The adhesive contact angle on polished and sandblasted composite surfaces is presented in Figure 8. These measured data can investigate from many aspects regarding the type of adhesive (self-etch, universal), functional molecules content of adhesive, and HEMA content of adhesives. In this study, we focused on the relationship between surface treatment and the best wettability of dental adhesive on the tested composite surface. The contact angle of adhesives was lower on polished composite surfaces than on sandblasted surfaces. The \(p\)-values of statistical analysis are summarized in Table 3. The statistical analysis of the dental adhesive contact angle revealed that on the polished surface, the SDR differs significantly from TECBF and TEC in terms of HB, TBF II, and SU. In terms of CL and GP, SDR only differs significantly from TEC and TECBF. The SU adhesive best wets all polished composite surfaces. On polished surfaces, the HB and SU displayed significantly lower contact angles on SDR than TECBF or TEC. The TBF II displayed a significantly higher contact angle on SDR than TECBF and TEC. The wettability of CL and GP on SRD and TECBF was similar, but TEC was more wettable by these two adhesives than SDR and TECBF. Figure 4:0 OWRK model-based calculated surface free energy (SFE) of SDR, TECBF, and TEC RBCs, indicating the disperse and polar fraction of SFE.
# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
## 4 Experimental Results
### Comparison with Other Methods
In addition, for ORB-SLAM2, GeoNet, MLF-VO, and D3 VO, the trajectories are aligned with the ground truth using [40], with 20 frames used for alignment. The results of VISO2-M, PCNN, DeepVO, Flowdometry, and TartanVO come from their paper or source code. Figure 4 illustrates the trajectories of the stereo version of ORB-SLAM2 and ORB-SLAM3, PCNN, GeoNet, D3 VO, TartanVO, MLF-VO, our StereoVO, and ground truth on sequences 07, 09, and 10 (if available). Our StereoVO achieves the best average performance in both translation and rotation compared to other learning-based methods. In comparison to PCNN, which employs optical flow calculated by BROX [39] as input for their network, our method significantly outperforms them in the task of VO. Flowdometry and TartanVO use similar strategies as ours, where optical flow is utilized as the middle procedure. However, Flowdometry uses FlowNetS [31] to generate optical flow, while TartanVO employs ground truth optical flow as a part of their label. In our StereoVO, we use a fixed-parameter FlowNet2 without ground truth optical flow, yet our method still demonstrates superior results. Similarly to our method, MLF-VO employs depth net and pose net in a series manner. However, MLF-VO did not consider the influence of moving objects. On the other hand, GeoNet uses separate components to learn the rigid flow and optical flow by rigid structure reconstructor and non-rigid motion localizer, respectively. They estimate rigid flow and optical flow in parallel, but in this case, the pose estimator in the rigid parts cannot become fully informed about moving objects. D3 VO employs a back-end non-linear optimization module to optimize final poses. However, their \(T_{rel}\) error is more than twice that of ours. We further compare the RPE (relative pose error) with MLF-VO, which has a similar average translational and rotation RMSE drift, shown in Figure 5, while results in Table 4.  
Figure 4: Qualitative evaluation on (**a**) Sequence 07, (**b**) Sequence 09, and (**c**) Sequence 10 of KITTI odometry benchmark. Figure 5: RPE comparison of MLF-VO and our StereoVO on (**a**) Sequence 07, (**b**) Sequence 09, and (**c**) Sequence 10 of KITTI odometry benchmark. We compare the running time of our method with ORB-SLAM2, ORB-SLAM3, MLF-VO, and TartanVO, and the results are shown in Table 5. The frames in the original KITTI VO dataset were resized to 192 \(\times\) 640 for accurate comparison. The experiments were performed on Ubuntu 18.04 with AMD Ryzen7 3700x 3.6 GHz CPU and NVIDIA RTX2080S 8G GPU. Our stereo VO method was found to be superior to both ORB-SLAM2 and ORB-SLAM3, exhibiting half the inference runtime to deliver precise results. Furthermore, our method uses 1.9 GB memory, which is almost the same as MLF-VO, making it efficient and practical for real-world applications.
## 4 Discussion
### Selection of Penalty Coefficient  
As part of the constraint term, selecting the penalty coefficient in the penalty term is also crucial. A larger penalty coefficient is not necessarily better. If the constraint is too strong, the algorithm is prone to getting stuck in local optimal solutions. Since the penalty term involves only one parameter, we choose a simple loop iteration to test the impact of different penalty coefficients on inversion accuracy. The penalty coefficient linear increases regularly with each loop iteration, ranging from \([10^{2},10^{13}]\). The details are shown in Figure 8. From Figure 8, it can be seen that the most suitable penalty coefficient is \(5\times 10^{8}\). The RMS error calculated with this penalty coefficient is 0.67 mGal. Meanwhile, we found that, when an unsuitable penalty coefficient is chosen, the RMS error can reach up to 1.6 mGal, with a deviation rate of 160% from the lowest accuracy. And in 100 loop iterations, unsuitable penalty coefficients are chosen quite often. This indicates that testing the selection of the penalty coefficient is very necessary.
## 4 Engine Test Results
### Fame  
To compare with ethylene glycol monomethyl ether types of biodiesel, cottonseed oil methyl ester was prepared and used to study the combustion and exhaust emissions characteristics by 2102 QB diesel engine.  
When the engine fueled with this cottonseed oil FAME purely, its brake thermal efficiency improved to be 32.2% and 33.6% at the conditions of 70 N\(\cdot\)m, 1400 r\(\cdot\)min\({}^{-1}\) and 2200 r\(\cdot\)min\({}^{-1}\), which are 29.6% and 30.0% for the petrodiesel operation, respectively.  
Figure 6: Experimental setup for engine tests.  
Under 1400 r-min\({}^{-1}\) constant speed operating conditions, the exhaust smoke reduced by 30.0-47.4%; CO emissions reduced by 20.0-33.3% at 1400 r-min\({}^{-1}\); HC emissions decreased by 18.2-36.4%; and NOx emissions decreased by 3.2-16.6%, respectively.  
Under 2200 r-min\({}^{-1}\) constant speed operating conditions, the exhaust smoke reduced by 26.8-45.5%; CO emissions reduced by 33.3-50.0%; HC emissions decreased by 11.1-20.0% and NOx emissions decreased by 13.1-24.2%, respectively.
# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 3 Materials and Methods
### Dataset  
For the optimisation of the 2D U-Net model, we use a private dataset [35], which is collected following ethical approval and informed consents. The dataset is obtained without an endorectal coil, using a Siemens Skyra 3.0 Tesla magnet located at the Calvary Mater Newcastle Hospital, Australia. The dataset consisted of 41 prostate, T2 weighted, MRI scans with three expert manual delineations on each scan. Furthermore, each scan contains \(320\times 320\times 60\) voxels with a voxel size of \(0.625\times 0.625\times 2\) mm. For the performance evaluation in Section 3.2, the second expert label is used for both training and testing as this expert has the highest mean Dice's similary coefficient (DSC) score against the majority voting [54]. This will also reduce the labelling bias on the smaller volumes of the organ to be segmented, as majority voting tends to be biased.
## 2 Data Collection
### Statistical Filtering Pre-Processing  
When millimeter-wave radar scans a target object, errors in the hardware and software will cause the offset of the 3D coordinates of points within a point set region, then the outliers, and finally redundant feature information and an algorithm model training below the global optimum.  
In this study, the curvature tensor was estimated by the least square iterative method and weights were assigned to the samples during the iterations based on the neighborhoods around the point cloud, thus refining each neighborhood around each point. The curvature  
Figure 4: Flowchart of time registration.  
Figure 3: Diagram of coordinate system conversion.  
obtained from the calculation was used along with the statistical weights to recalibrate the normal distribution [24]. The global quantities were minimized, and the curvature and normals were calculated to remove outliers, thus better retaining the texture features of the vehicle point cloud.  
In a Cartesian coordinate system, each point of a point cloud exists in \(x\), \(y\) and \(z\) coordinates. A point cloud sample can be supposed as:  
\[D=\Big{\{}p_{i}\in\mathbf{R}^{3}\Big{\}}i=1,2,\ldots,n \tag{4}\]  
where \(n\) denotes the total number of sampled point cloud points and \(p_{i}\) denotes the unordered points in the sampled sample \(D\). If only the \(x,y\) and \(z\) coordinates of each unordered point are taken, the distance threshold dmax for the points can be calculated as:  
\[\overline{d}_{i}=\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}}d _{i} \tag{5}\]  
\[\sigma=\sqrt{\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}}\left( d_{i}-\overline{d}_{i}\right)^{2}}, \tag{6}\]  
\[d_{\text{max}}=\frac{1}{n}\underset{i=1}{\overset{n}{\underset{i=1}{\sum}}}d _{i}+\alpha\times\sigma. \tag{7}\]  
where \(d_{i}\) is the distance between two disorder points, \(\overline{d}_{i}\) is the average distance between the sample disorder points of the point cloud, \(\sigma\) is the sample standard deviation and \(\alpha\) is the threshold factor. If the distance between two points is greater than \(d_{\text{max}}\), an outlier occurs and should be excluded from the point set. In our study, the number of proximity points for each point was set to 50, the distance threshold was 1, the threshold coefficient \(\alpha\) was 0.5 and the threshold factor \(\alpha\) was 0.2.  
Figure 5 shows the initial view of the point cloud sample. The point cloud at the near end is scattered, the ground point cloud is not clear, and in the distant point cloud around the obstacle exist more chaotic outliers and uneven distribution, which makes vehicle detection more difficult. Figure 6 shows the view after data preprocessing, where the point distribution in the local area is comparatively more uniform, the ground point cloud at the near end is flat and easy to implement the subsequent ground segmentation, and the distant point only directs to the vehicle and wall point cloud information, uniformly distributed.  
Figure 5: Point cloud data before filtering.
# A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations
## 4 Result
### Marker Recognition Test  
The most important elements of image processing indoor positioning technology are the response speed and recognition rate. We found that the HMIPS provides more than 95% of the way-to-walk information in under 1 s, under the conditions of : a 1.2 m/s walking speed; a distance of 2 m from the marker; in approximately 500 directions. Problems such as damage due to marker contamination and durability were solved by manufacturing the marker and the attachment location. It was confirmed that the HMIPS could be put to practical use via a pilot operation for one year in 2019 in order to assess its feasibility for a practical navigation service. The system was used for pilot operation and stabilization work is in progress.  
Figure 9a illustrates the guide screen that is displayed when a marker is recognized by a smartphone. Figure 9b displays the screen when moving along the sections between markers. The distance from the point of recognition of the marker to the destination is displayed on the screen, and a section such as the stairs is highlighted for the user by the spatial characteristics of the direction of approach through an icon so that the user can select another path if desired. This is a characteristic of the HMIPS that can provide potential users with a distinct path from ordinary pedestrians by utilizing information stored in markers and expressing different results according to the recognition conditions. Figure 9c shows a contaminated marker, if the level of contamination is not enough to completely cover the pattern area, it does not affect the recognition of the marker. As shown in Figure 9d, the marker without contaminants exhibited the same recognition rate of 30 to 500 lux illuminance.  
Before finally determining the size of the marker, we experimented with the recognition rate at a height of approximately 1 m by varying the size of the marker. The marker size was increased in 5 cm increments from the smallest size of 5 cm to the largest of 25 cm, and marker recognition was attempted 100 times for each size. Figure 10 shows the recognition rate according to the marker size.  
Figure 8: Testbed subway station for HMIPS test.  
Figure 9: Guide screen of the HMIPS: (**a,b**); installed marker: (**c,d**).  
As can be seen in Figure 10, a marker size of 5 cm was not practical because it showed a recognition rate of just 27%. It can be seen that the recognition rate gradually increased up to a marker size of 20 cm, but there was little difference above that. Based on this result, we decided to make the final marker size 20 cm and set it as such in the experiment.
## 2 Background and Related Work  
### ML/AI Based HDD Failure Prediction  
Traditionally, SMART data are used to monitor disk states and raise the alarm when failures happen; and, in general, SMART attributes are set by manufacturers, who also set a conservative threshold for each attribute to raise the alarm when the data exceed the threshold. However, this approach always leads to lower failure alarm rates (FAR), and also lower failure detection rates (FDR). As reported in [11], this original threshold-based method only achieves a 3-10% FDR. To improve failure prediction performance, many works have proposed some ML/AI methods based on SMART attributes. Generally, this task is defined as a binary classification problem, where one class is for healthy disks and the other is for failures. For example, [PERSON] et al. [5] used gradient-boosted regression trees (GBRT) [12] to design a residual life prediction model, and their results showed that this method can offer a practical improvement in disk failure prediction. [PERSON] et al. [13] compared several methods for sector errors prediction; as a result, random forest (RF) [14]-based classifiers obtained the most accurate result. In order to make better use of time-series SMART data, many works [8; 9; 15] employed recurrent neural networks (RNN) and long short-term memory (LSTM) [16], in which there are memories cell to record historical information of SMART attributes, and these can achieve outstanding prediction results.
# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 3 System Model
### Analysis of Varying BS Antenna Count  
The analysis of varying BS antenna counts for varied values for VIM6G IoT such as 32, 34, 40, \(-\)50, \(-\)40, \(-\)30, \(-\)20, 64, 66 and 80 are discussed in this section. The NCE is analyzed for a varied range of SNRs such as 0.2, 5.5, 0.7, 5.10, 0.12, 15.0 and 17.5. The analysis of varying BS antenna counts for varied values such as 32, 34 and 40 is shown in Figure 3. The analysis of varying BS antenna counts for varied values such as \(-\)50, \(-\)40, \(-\)30 and \(-\)20 is shown in Figure 4. The analysis on varying BS antenna counts for varied values such as and less power is used for lower computation distortion, it can be seen that the normalized computation error increases when the minimum SINR is increases. Moreover, when the number of BS antennas rises, the computation error lowers. This is so that the overall performance can be enhanced by obtaining greater array gains. This lower NCE is owing to the adopted variance-based integration model. Here, ECC evaluation is carried out with the proposed variance-based integration with arithmetic-mean and geometric-mean specifications. Moreover, the proposed IACO algorithm deals with the sub-problem for optimization of transmit beams that also ensure the attainment of minimal MSE.  
Figure 3: Analysis of VIM6G IoT for varying BS antenna count for varied values such as (**a**) 32, (**b**) 34, and (**c**) 40.  
Figure 4: _Cont_.  
Figure 4: Analysis of VIM6G IoT for varying BS antenna count for varied values such as (**a**) \(-\)50, (**b**) \(-\)40, (**c**) \(-\)30 and (**d**) \(-\)20.  
Figure 5: _Cont._
###### Abstract  
Background: The aim of the study was to identify and critically evaluate the best available evidence on the impact of physical exercise on patients with primary Sjogren's syndrome. Methods: Studies were searched in four electronic databases (PubMed, Web of Science, Scopus, and SportDiscus) from their inception up to September 2024. The methodological quality of the included studies was assessed using the 10-point Physiotherapy Evidence Database (PEDro) scale and the Methodological Index for Non-Randomized Studies (MINORS). Results: A total of four randomized controlled trials and one comparative study were analyzed. The training programs evaluated varied in duration, ranging from 12 to 28 weeks. Exercise was found to have a positive intra-group impact on fatigue, quality of life, and functional capacity. However, exercise does not demonstrate superior effects compared to standard treatment for improving quality of life and disease impact. Conclusions: It is essential to increase the number of studies involving individuals with primary Sjogren's syndrome across various exercise conditions to more comprehensively evaluate the potential benefits.  
fatigue; exercise; quality of life; metabolic syndrome; physical activity; sleep; anxiety; depression +
Footnote †: journal: Applied sciences