esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Physicochemical Parameters of Waters
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Statistical Parameters**} & \multicolumn{6}{c}{**Physicochemical Parameters**} \\ \cline{2-7}  & **T\({}^{\circ}\)C** & **pH** & **EC** & **Sa** & **PO\({}_{4}\)\({}^{3-}\)** \\ \hline Minimum & 28.49 & 6.43 & 8.24 & 4.20 & 0.48 \\ \hline Maximum & 30.94 & 7.46 & 35.91 & 21.20 & 1.38 \\ \hline Average & 29.33 & 6.99 & 15.51 & 8.52 & 0.80 \\ \hline Median & 29.27 & 7.01 & 14.30 & 7.93 & 0.74 \\ \hline SD \({}^{\star}\) & 0.48 & 0.29 & 7.38 & 4.41 & 0.28 \\ \hline CV \({}^{\star}\) & 1.64 & 4.21 & 47.54 & 51.82 & 34.63 \\ \hline Skewness & 1.16 & \(-\)0.27 & 1.51 & 1.57 & 0.86 \\ \hline Kurtosis & 3.26 & \(-\)0.71 & 1.94 & 2.25 & \(-\)0.17 \\ \hline
1 st quartile & 29.03 & 6.79 & 9.86 & 5.10 & 0.58 \\ \hline
3 rd quartile & 29.50 & 7.22 & 18.80 & 10.49 & 0.94 \\ \hline Standards WHO \({}^{\star}\) & 25 & 6.5-9.5 & 0.18-1 & 0.3-0.5 & 0.5-0.9 \\ \hline \end{tabular} \({}^{\star}\)CV: coefficient of variation; SD: standard deviation; WHO: World Health Organization.  
\end{table} 
Table 1: Statistical values of the physicochemical water parameters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Physico-Chemical**} & \multicolumn{6}{c}{**Semivariogram Models and Parameters**} \\ \cline{2-9}  & **Models** & **Nugget (Ca)** & **Sill (C\({}_{\star}\)C)** & **C/Co\({}_{\star}\)C + s SD (\%)** & **Range (km)** & **ME \({}^{\star}\)** & **RMSE \({}^{\star}\)** \\ \hline T\({}^{\circ}\)C & Exponential & 0.00 & 0.236 & 0.00 & 0.025 & \(-\)0.018 & 0.45 \\ \hline pH & Spherical & 0.023 & 0.103 & 22.33 & 0.159 & \(-\)0.005 & 0.22 \\ \hline EC & Spherical & 5.74 & 75.847 & 7.57 & 0.242 & 0.122 & 3.09 \\ \hline Sa & Spherical & 1.99 & 27.528 & 7.22 & 0.242 & 0.069 & 1.88 \\ \hline TDS & Gaussian & 1844.93 & 19,449.50 & 9.48 & 0.242 & 1.033 & 48.07 \\ \hline PO4 & Exponential & 0.004 & 0.018 & 21.79 & 0.024 & \(-\)0.009 & 0.137 \\ \hline \end{tabular} \({}^{\star}\)ME: mean errors; RMSE: root mean square error; SD: spatial dependence.  
\end{table} 
Table 2: Semi-variogram models and their parameters of physicochemical variables.
## 3 Results and Discussion
### Thermal Observation in the Setting Process  
Figure 4 displays the thermal behavior of \(\alpha\)-CSH biocearamic during the setting time. Clearly, during 5, 10, 15, 20, and 25-min setting time, the \(\alpha\)-CSH biocearamic experienced a temperature change only less than 2 \({}^{\circ}\)C (Figure 4a); the material began to set at 5 min, and the maximum temperature at 18 min during the setting time was about 31 \({}^{\circ}\)C (Figure 4b). The thermal behavior demonstrated by the \(\alpha\)-CSH biocearamic included a quick setting and lower temperature variations during the setting time, which are beneficial properties as a bone graft material. Due to the low temperature, this material is safe to use as a bone graft, because it does not cause protein denaturation that will lead to bone damage [22; 35; 42; 43]. A quick setting also can speed up the healing process as various proteins and growth factors are absorbed in the blood to the affected area [40; 44].  
Figure 4: (**a**) Thermal images of the \(\alpha\)-CSH biocearamic during the setting time and (**b**) the relationship between the maximum temperature and setting time.  
Figure 3: Characteristics of the \(\alpha\)-CSH biocearamic bone graft substitute material: (**a**) field emission scanning electron microscope (FE-SEM) micrograph and (**b**) energy-dispersive spectroscopy (EDS) spectrum.
## 4 Omnidirectional Images
### Datasets for Omnidirectional Images
All analyzed datasets provide a ground truth in terms of either scanpaths (for eyes and head movement) or fixations-related saliency maps, i.e., without precisely-annotated salient object regions, possibly due to the intrinsic difficulty in segmenting equirectangular projection images. The Salient360! [41] dataset was created for the Grand Challenge \"Salient360!\" organized in conjunction with ICME 2017 (International Conference on Multimedia and Expo). The dataset has been updated through the years [40], with the last edition also including a set of omnidirectional video clips. It is supplied with a script toolbox for the evaluation of predicted scanpaths and saliency maps. The SVR [33] (Saliency in Virtual Reality) dataset is a collection of both head and eye orientation data (scanpaths), coming from the observation of 22 stereoscopic omnidirectional images. The environmental condition of the stimuli include combinations of users being seated or standing, with or without a head-mounted display. In all conditions, an eye tracking device was used. Wild-360 [36] is an exclusively video-based dataset for omnidirectional saliency. The original clips were retrieved from YouTube using specific keywords such as \"nature\", \"wildlife\", and \"animals\", in order to collect a dataset with heterogeneous and dynamic contents. The video sequences were manually annotated by multiple users, without any head- or eye- tracking device. LAY [34] (Look Around You) was built with the objective of developing saliency estimation methods without the support for an eye tracking device for data collection. Specifically, the head orientation of the viewers (called Viewport Center Trajectory) is used as a proxy ground truth for the generation of saliency maps. Different experiments have been conducted by varying the viewing time of each stimulus.
## 4 Fault Analysis
### External Fault Analysis
With reference to Figure 3, the presence of the distributed neutral wire/DC middle point for all ports suggests that the neutral wire should be directly earthed, as is usual for AC systems. In this case, both for AC and DC systems, the proposed three-wire converter allows for correct system operation and earth fault clearance, since earth faults are equivalent to pole-to-pole faults and the considerations presented in the previous paragraph apply. Furthermore, in hybrid distribution systems, the proposed converter topology forces the state of active parts towards earth of each section to be the same, unless isolated ports are introduced. This greatly simplifies the definition of earthing policies with respect to systems with many isolated sections and avoids unnecessary cascaded conversion stages. A significant advantage is also gained if the AC port is connected to a load or load feeder and if the AC port is connected to an AC power source. If the AC port is feeding loads, this implies that it is possible to use either a TT or TN earthing scheme, which was not possible with the two-wire MCs considered in [11], and simplifies the evaluation of electrical safety issues. If, on the contrary, the AC port is connected to an AC power source, this does not require an isolation transformer, even if a transformer is already present (i.e., MV/LVtransformer), to isolate the star centre from earth. Furthermore, problems similar to the ones discussed in [40] cannot arise due to the symmetry of the considered MC. To complete the discussion on earth faults, a detailed analysis of this configuration in earth-isolated systems should be performed. However, this can be difficult since it is necessary to evaluate parasitic system parameters, which are difficult to assess and very case-dependent. Even though a detailed analysis of earth faults lies outside the purposes of this paper, the presented considerations clarify that, in the case of a directly earthed neutral wire, correct operation can be achieved, and fault clearance is possible both for pole-to-pole and pole-to-earth faults. Furthermore, AC and DC ports can be freely introduced with no need for specific adaptations and with no risks of common-mode voltage-related issues.
## 3 Materials and Methods
### Data Collection
Improving the data quality through more sophisticated pre-processing steps, denoising approaches, and adding well-known features such as frequency band power estimations are out of the scope of this study and will be subject to examination in future research depending on the availability of data from industry. Figure 4 provides a comprehensive overview of the distribution of key numerical features through individual box plots, including rotation speed, temperature, kurtosis, peak values, and vibration measurements across axial, vertical, and horizontal directions. Figure 3: Temporal trends of key features in the database. (**a**) Peak values. (**b**) Vibration acceleration. (**c**) Vibration velocity. (**d**) Kurtosis across the axial, vertical, and horizontal axes. (**e**) Rotation speed. (**f**) Surface temperature. (**g**) Ultrasound levels. Each subplot illustrates the interquartile range (IQR), central tendency (median), spread, whiskers, and outliers. The IQR, representing the middle 50% of the data, provides insight into data variability, while the median indicates the central value. The whiskers extend to cover the majority of data points, and values outside these whiskers are considered outliers, which may indicate anomalies or early-stage faults in the machinery. The spread of the data reveals fluctuations in machine performance; a larger spread indicates higher variability. By comparing different features such as vibration acceleration and ultrasound levels, parameters with higher variability or extreme values can be identified, potentially indicating mechanical issues such as bearing faults or rotor imbalance. This distribution analysis aids in setting thresholds for anomaly detection, enabling predictive maintenance by triggering alerts when machine behavior deviates from normal operating ranges, thus improving fault detection and maintenance planning accuracy. The features shown in each subplot of Figure 4 are as follows: (a) rotation speed; (b) temperature, reflecting thermal conditions; (c) ultrasound level, sensitive to minor faults; (d-f) kurtosis values along axial, vertical, and horizontal axes, indicating sharpness in vibration signals and transient faults; (g-i) peak values along the three axes, representing maximum vibration amplitudes, which may signal mechanical stress or faults; and (j-l) vi  
Figure 4: Distribution of numerical features using box plots: (**a**) Rotation Speed, (**b**) Temperature, (**c**) Ultrasound Level, (**d–f**) Kurtosis in the axial, vertical, and horizontal axes, (**g–i**) Peak values in the axial, vertical, and horizontal axes, (j–l) Vibration accelerations in the axial, vertical, and horizontal axes. bration acceleration measurements in axial, vertical, and horizontal directions, reflecting dynamic mechanical responses.
# Simplified Welch Algorithm for Spectrum Monitoring
## 3 Modified Welch Algorithm
### Proposed Algorithm
 \tag{13}\]  
Equation \(PSD_{i}[v]=a\times PSD_{i-1}[v]+P_{i}[v]\) allows us to calculate an averaging with a single multiplication and addition. Figure 1 explains how this principle is applied to all FFT samples. The displayed design in Figure 1 explains the basic idea, but its implementation is still complex because of several reasons:  
* This design needs each FFT sample to implement weighted averages separately, thus the amount of multiplications and summations will increase significantly. * It is not flexible enough to support different FFT sizes. For example, if the FFT size changes from 1 K to 2 K, all of the design must change completely to support it. * Certain types of FFT blocks, e.g., in the Xilinx DSP Generator library, work in a series manner that makes the design more complex. Given these reasons, a simple solution with the help of the RAM is presented to calculate Welch samples based on the weighted averaging. Figure 2 symbolically explains the modified design of Welch implementation. This design reduces the required memory to single RAM by NFFT address. Each averaged FFT samples are added to the previous averaged samples that are multiplied by the \(\alpha\) factor. The only consideration here is the use of a FFT computation in series instead of a parallel version. Figure 1: Calculating Welch samples based on the weighted averaging. For each segment of input data stream with the length of \(M\), a windowed \(N_{F}\) point FFT is calculated. After the \(M\) clock, \(M\) samples of a segment enter this block, then it calculates the \(N_{F}\) point FFT of this sample. After calculation, FFT samples are ready at the output, sample by sample and synchronized with the clock. Each time that FFT samples of an input segment are ready, the FFT block generates a \"Data Ready\" pulse that determines the start point of the sample series. The \"Data Ready\" pulse is used to reset a counter. The output number of the counter will read the previous value and save the FFT sample into the RAM at the correct address where each address is a specific frequency PSD estimation.
## 3 Case Studies
### Case Study of the Selected IGS/CORS Stations in Canada and the USA  
Fourteen IGS stations were selected for this case study; they are listed in the first column of Table 5 with the station hardware information. The summary of the GNSS observation files is shown in Table 6. Pseudorange multipath errors, carrier-phase multipath errors, and carrier-phase measurement noise of GPS and GLONASS at the selected stations at a 95% confidence level are shown in Tables 17 and 18, respectively.
## 2 Materials and Methods
### Measurements  
The subjects were assessed at four different stages: before the intervention (baseline), after the first stretching session, at the end of the intervention, and again during a follow-up evaluation conducted one month later.  
The primary outcome measured was hamstring flexibility, evaluated using the Knee Extension Angle (KEA) test, which is considered the gold standard for assessing hamstring flexibility [31]. During the test, the participant lay in a supine position with both lower limbs extended. One examiner stabilized the hip at 90\({}^{\circ}\) flexion in the limb selected formeasurement, while the other passively extended the knee until the participant reported discomfort or greater resistance was detected. Knee extension was measured using a goniometer, with the fulcrum placed at the external femoral cordyle, the fixed arm aligned with the greater trochanter along the thigh, and the movable arm aligned with the external malleolus along the leg. Care was taken to ensure the ankle remained relaxed and no pelvic tilting occurred during the measurement. It was determined whether the subject had hamstring retraction if he scored less than 160\({}^{\circ}\).  
The pressure pain threshold (PPT) of the tibial nerve was assessed using a manual pressure algometer (Wagner Force Dial FDK 20), with results recorded in kilograms per square centimeter (kg/cm\({}^{2}\)). Participants were positioned prone on a stretcher, with both lower limbs fully extended. Measurements were conducted at the midpoint of the knee flexor line within the popliteal fossa. Participants were instructed to indicate the point at which pain or discomfort became noticeable. To ensure comprehension of the procedure, a preliminary test was performed on the forearm prior to assessing the lower limb. On the dominant leg, three measurements were taken with a 10 s interval between each. The final pressure pain threshold value was calculated as the mean of the three readings.  
Maximum isometric hamstring strength was evaluated in Newtons using a digital dynamometer (microFET(r) 2, Hoggan Scientific LLC, Salt Lake City, UT 84104, USA). Participants were positioned prone on a stretcher, with the dominant leg visually aligned at 90\({}^{\circ}\) knee flexion. The dynamometer was placed against the participant's heel, and a gradually increasing force was applied toward knee extension for 3-5 s, while the participant resisted the movement to maintain the initial position without allowing knee extension. Three measurements were conducted in this position, with a 30 s rest interval between each. Each measurement was recorded at the point when the participant began to extend the knee. The final strength value was determined by averaging the three recorded measurements.
# Passive Control of Base Pressure: A Review
## 4 Conclusions  
In this review, various passive control techniques to control the base pressure and regulate the drag force are discussed. When the passive strategies in the form of ribs,cavities, static cylinder, boattail, and step-body are used, the expansion level plays a crucial role in controlling the base pressure. After a critical area ratio, the effect of NPR is negligible. The control does not give the desired results at a very high level of under-expansion. When the area ratio is in the range of 2 to 4, the passive control in the form of ribs is beneficial and reduces the base suction. The review shows that for some combination of the parameters, under expansion, level 1.5 is the optimum level of under-expansion, resulting in a maximum gain in base pressure. In the case of the ribs, the ribs' geometry and location play an important role. Depending upon the duct diameter, both the parameters show different optimum values. For the lower area ratio, the place, as well as the size values, are small. However, for a high area ratio, both the parameters assume higher values as the reattachment length is considerable. For the splitter plate, a drag reduction of as high as 39% is achieved. It is seen that cavities are effective in reducing oscillation in the enlarged duct. The effect of spike and grooved spike plays a significant role in affixing the base pressure values. Aerospike was tested for transonic and supersonic speeds. It is observed that as the Mach number increases, the separation zone becomes steeper, and hence aerospike leads to a decrease in drag when attached to payload fairing. When the static cylinder is placed in the recirculation zone, it leads to a drag reduction of 59%. In summary, this review has critically assessed and summarized the crucial elements in the passive control device that control the base pressure, enhance the base pressure, and reduce the base drag. Hence, it further established passive control as a very efficient technique to favorable control the pressure under the influence of favorable gradient at the nozzle exit. Table 1 below gives the summary of the type of passive control discussed in this paper and their effect.
# Attention-Based Active Learning Framework for Segmentation of Breast Cancer in Mammograms
## 4 Experimental Results
### Ablation Experiments  
To evaluate the effectiveness of the attention-based sampling mechanism and the active labeling strategy, we designed several ablation experiments as shown in Table 1. Note that PFMs represent multiple promotion feature modules used in the basic segmentation model, Att refers to the proposed attention-based sampling scheme, and Act is the proposed active labeling strategy.  
After adding PFMs, Att and Act, the segmentation performance on breast cancer images gradually improved on our four breast cancer image datasets, proving the effectiveness of these three modules. Specifically, we found that UNet+PFMs could achieve more precise boundaries of polyp regions and performed accurate segmentation when compared with the basic network (i.e., UNet). However, the shallow usage of boundary information without multi-scale refinement for boundary regions leads to uncompact performance towards larger and more regularized-shape polyp regions. In contrast, due to the usage of PFMs to extract a more distinguishing feature map by fusing multi-scale information, boundaries achieved by the proposed method were much more obvious with clear contour lines, thus providing better segmentation performance.  
The ablation experiment on Att proved that the attention-based sampling scheme improved segmentation performance on all datasets. The attention-based design helped in effectively selecting more valuable samples for the further manual labeling process. It is beneficial to focus on the most informative unlabeled samples, which brings the feature information required by the current model for performance improvement with the fewest updating iterations.  
Act, representing the active labeling strategy, enlarges size of the labeled breast cancer set, thereby improving the effect of tumor segmentation. Due to the guidance of optimized selection on unlabeled samples, we observed that informative samples for the current trained model were added to the labeled samples set for further retraining.
## 3 Results and Discussion  
### Water and Salt Variation during Desalting Process  
During cod desalting, rehydration of the cod matrix occurs, while sodium and chloride ions flow into the surrounding water [6]. As can be seen in Figure 1, the flow of water (H\({}_{2}\)O) and sodium chloride (NaCl) showed a higher rate during the first hours of rehydration, starting to decline thereafter until reaching an osmotic equilibrium, which occurs after about 8 h of the desalting process. Initially, salted cod samples showed a water and salt content of 1.15 \(\pm\) 0.03 g H\({}_{2}\)O/g IS and 0.46 \(\pm\) 0.03 g NaCl/g IS, respectively, which correspond to 44 and 18% of the muscle weight, respectively. These results are similar to values previously reported in the literature [4; 23]. Over desalting time, the water content in muscle increased to 2.58 \(\pm\) 0.12 and 2.60 \(\pm\) 0.10 g H\({}_{2}\)O/g IS, when the process was carried out at 20 and 4 \({}^{\circ}\)C, respectively (Figure 1A), while the salt content decreased to 0.055 \(\pm\) 0.001 and 0.086 \(\pm\) 0.005 g NaCl/g IS at 20 and 4 \({}^{\circ}\)C, respectively (Figure 1B). No significant differences (\(p>0.05\)) were observed between desalting at 20 and 4 \({}^{\circ}\)C. In the literature, it is known that high temperatures increase the rate of desalting ([PERSON] et al., 2005) [10], thoughtemperatures between 0 and 4 \({}^{\circ}\)C are desirable since the cod water content increases during the desalting process; consequently, the water activity increases, being less of a hurdle to microbial growth, so the cod progressively becomes more perishable. Temperature control during desalting is so particularly important owing to the rapidity of microbial growth in cod [24; 25].  
Since the salt content in commercially available desalted cod is usually in the range of 2-3% [6], the desalted cod samples obtained in this work had a suitable salt content for its consumption after 16 h of desalting (1.5 and 2.5% of salt content, at 20 and 4 \({}^{\circ}\)C of process temperature, respectively).
## 1 Introduction  
In the preliminary phases of product development and engineering design, the ability to evaluate the load capacity and durability of components is of the highest interest. A fundamental aspect of this process involves conducting numerical analyses, often finite element analysis (FEA), to simulate the behavior of engineering components considering various operational conditions and different materials. To effectively capture the mechanical behavior of components, FEA software includes various material models, whose parameters are typically obtained through standardized material tests. Monotonic stress-strain curves obtained from tensile tests, or cyclic stress-strain curves obtained from cyclic experiments, can be represented with different material models [1; 2]. When these models are not natively integrated into FEA, they can be implemented using multilinear material models.