esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 2 Snapshot Receivers and Beamforming
### Combined Acquisition Strategy  
An effective blind acquisition technique, which firstly implements incoherent spatial integration to find the correct correlation peak, secondly estimates the array steering vector, and thirdly does coherent spatial integration, has already been developed [25]. This method is completely blind, as no DOA estimation is done. Figure 1 shows the method that was used for acquisition and to estimate the array steering vector. Consequently, no calibration or information about the array configuration or orientation is required. The acquisition method first applies incoherent spatial acquisition followed by coherent spatial acquisition with an estimated array steering vector to improve performance. As this application focuses primarily on beamforming, the array steering vector is also referred to as the beamforming steering vector or the beamforming weights. Hence, these terms in this scenario can be used interchangeably.  
Firstly, blind incoherent acquisition is carried out for each receiver channel. The received signals from every array element are correlated simultaneously with the replica of the current satellite (denoted by \"Cor\"). The absolute squared values of the correlations are added (\"\(\sum|\cdot|^{2\prime}\)\") in order to estimate the code phase \(\tau^{(1)}\) and carrier Doppler \(f_{\rm D}^{(1)}\) in the acquisition stage (\"Acq\"). If the satellite is acquired, then the array steering vector is estimated (\"Est\") with the code phase \(\tau^{(1)}\) and carrier Doppler \(f_{\rm D}^{(1)}\) from  
Figure 1: Block diagram for a blind beamforming acquisition method.©IEEE. Reprinted, with permission, from [22].  
the incoherent acquisition results. Once the vector is estimated, the receiver channels are weighted accordingly (denoted by \"BF\") and a second acquisition takes place. This time the coherent \(\tau^{\text{(C)}}\) and carrier Doppler \(f_{\text{D}}^{\text{(C)}}\) are determined. After the second acquisition stage, the code phases and Doppler frequencies from the first (incoherent), and the second (coherent) acquisitions are compared. If there is a significant difference, the satellite will be discarded and not used after acquisition. This allows a secondary test to remove false positives from the acquisition process. This double acquisition is processing-intensive, but it has shown high reliability and false acquisition suppression in previous studies [25].  
A limitation of this method is that the estimation of the array steering vector is based upon the results of incoherent acquisition. These values may contain multi-path or cross-correlation components from other signals, which can obscure the estimation process. In turn, the erroneous array steering vector may form a beam that does not sufficiently suppress these unwanted signal components. This raises concerns, especially in harsh environments with significant multi-path components, as it may result in offsets in the acquisition estimates, which in turn result in a degraded PVT solution.
# Photodegradation Assessment of Calicotriol in the Presence of UV Absorbers by UHPLC/MSE
## 2 Experimental
### Method Validation
#### 2.5.1 Specificity  
The specificity of the method was evaluated by assessing the chromatograms of the mobile phase solution, the chromatograms of the methanolic calcipotriol solution, and the chromatograms after photodegradation in the presence of selected chemical UV filters. The obtained chromatograms were analyzed considering retention time (TR), peak areas, peak shape, and peak separation.
# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 4 Result
### Comparison with Unsupervised Domain Adaptation Methods  
We compare our MACS with the state-of-the-art unsupervised domain adaptation approaches: SAGAN [17], CUT [19], and F-LSeSim [18]. The image-to-image translation is from the self-acquired dataset to the CAMUS dataset. The self-acquired dataset A4 C view images are translated to the target domain and named X\({}^{\text{T}\to 5}\), which maintain the intrinsic content while having the style of source domain images. Then, the DAF is utilized to segment the X\({}^{\text{T}\to 5}\).  
The image-to-image translation results and segmentation predictions are presented in Figure 6. The CUT and F-LSeSim translate the echocardiography without a noticeable deformation, but the details have been confused with the noise, which causes the failed segmentation. The segmentation predictions on the images translated by the SAGAN are visually similar to the ground truth, but there is a great deformation between the fake image and the original image. The doctor cannot accept the result obtained from such a distorted image. Thus, these domain adaptation methods cannot be applied to solve the problem of cross-domain segmentation in echocardiography.  
Figure 5: The mean Dice Index (DI) for segmentation results of the MACS trained with different numbers of target domain images. **(a)** The line graph of the mean DI. **(b)** The boxplot of the mean DI.  
Figure 6: Fake images generated by image-to-image translation methods (SAGAN, CUT, F-LSaSim) and the visualization prediction of fake images that segmented by the DAF.  
The quantitative results from the comparative experiment are reported in Table 3. The MACS reduces the mean HD by at least 3% compared with the segmentation after the image-to-image translation. The mean DI and JI are also significantly improved.
## 4 Numerical Validation and Performance of Hybrid HCGA Algorithm
### Parameter Discussion
This indicates that when dealing with simple functions, the population space plays a major role, and when dealing with complex functions, the belief space plays a guiding role and has an influence on the evolution of the population space. Some rules for adjusting parameters can be obtained from analyzing the results in Table 5. For simple functions, \(P_{c}\) and \(P_{m}\) can be set to a lower level, while for complex functions, they need to be set to a higher level. For most functions, \(\alpha\) can be set to a level of about 0.2, and for multimodal functions with many local minima, \(\alpha\) should be set to 0.1. For parameter \(\gamma\), setting it to 0.3 is enough in most cases. \(\mu\) has roughly the same rule as \(P_{c}\) and \(P_{m}\). For high-dimensional multimodal functions, \(\mu\) should be set to 0.1, but for unimodal or low-dimensional functions, setting it at 0.3-0.4 is enough. \(K\) should be set to a smaller value as the complexity of the function increases, which determines the frequency of updating the knowledge in the belief space. As for \(p\), setting it at 0.1-0.2 should be enough for both unimodal and multimodal functions.
## 4 Results and Testing
### Testing Methods
(**a**) Well PS5, 5716.16–5716.71 m; (**b**) Well PS5, 5708.00–5708.10 m; (**c**) Well ZS101, 6352.41.00–6352.51 m; (**d**) Well PT1, 5729.36 m; (**e**) Well PT101, 5761.27 m; (**f**) Well ZS101, 6233.54 m.  
Figure 9: The correlation between the optimized predicted bitumen content and the experimentally measured bitumen content for the Dengying Formation 2 nd Member in the study area. Figure 10: Cross-section of reservoir facies identification results and optimized predicted bitumen content for the Dengying Formation 2 nd Member in the study area. To assess the proposed algorithm's applicability and the input data's reliability, control experiments are carried out using four unsupervised learning algorithms (Figure 11): PCA-FCM-DSDFP, FCM-DSDFP, FCM, and K-means. The Cramer's V and Crossplot coefficients served as standards for testing algorithm accuracy. Results showed that for denoised input data using the PCA algorithm, the Klem and convergence coefficients were 0.932 and 0.881, respectively, while for non-denoised data, they were 0.928 and 0.837. Non-denoised input data somewhat interfered with algorithm accuracy. In control experiments with FCM-DSDFP, FCM, and K-means algorithms, FCM-DSDFP accuracy was higher than FCM and K-means. This indirectly confirmed that PCA-FCM-DSDFP is more applicable than FCM and K-means, and it can be applied to similar reservoirs to further verify its suitability. It is noteworthy that for reservoirs with few categories, unsupervised learning algorithms are suitable. However, for reservoirs with complex categories, unsupervised learning classification remains challenging.
# Distributed Fault Detection and Isolation Approach for Oil Pipelines
## 4 Case Study: FDI System for Oil Pipeline
### Mathematical Modeling of Oil Pipeline
 \tag{20}\]  
In this model, internal pressure variables \(h_{uil}(i=1-3,L)\), flow variables \(Q_{i}(i=1-3,L)\), and head gain \(\Delta H_{i}(i=1-3)\) are related to each pumping station describing the plant; all these signals are considered as unknown variables, flow sensors \(y_{Q_{i}}(i=1-3,L)\), and pressure sensors \(y_{h_{uil}}(i=1-3,L)\) are considered as known or measured variables. Accordingly, fault variables \(f_{h_{2}}\), \(f_{h_{3}}\) and \(f_{h_{L}}\) are related to pressure sensors, fault variables \(f_{Q_{1}}\) and \(f_{Q_{L}}\) are related to flow sensors, and fault variables \(f_{i}(i=1-3)\) are related to pipeline segments (through flow balance at each section from the beginning to the end). To sum up, the structural model describing the plant consists of the system \(\Sigma(z,x,\texttt{f})\) with a total of 20 equations divided into 4 sub-systems \(\Sigma_{1}\) to \(\Sigma_{4}\), 8 known variables in \(z\), 14 unknown variables in \(x\), and 8 fault variables in \(\texttt{f}\).
# Numerical Study on the Application of Near-Infrared Temperature Distribution Measurement of HIFU
## 3 Results
### Shear Wave Effect  
When using the near-infrared method to measure the temperature distribution of tissues irradiated by HIFU, selecting a suitable observation window involves using infrared glass with infrared transmission functionality. There are significant structural differences between infrared glass and tissue, and when ultrasound waves propagate through the tissue, only the influence of longitudinal waves needs to be considered. Moreover, the influence of shear waves in infrared glass cannot be ignored [19].  
An ultrasound-solid coupling model for multi-layer tissue and infrared glass was developed, as shown in Figure 1b. The impact of infrared glass on HIFU temperature measurement was compared with linear models. The characteristic parameters of infrared glass are shown in Table 1. Assuming that the longitudinal wave velocity in the glass remains constant during the HIFU process, the comparison results of the ultrasound and thermal fields between the shear wave model and the linear model under different input powers are shown in Figure 6.  
Figure 6 shows that when the input power is 100 W, the maximum ultrasound pressure with infrared glass is 3.13 MPa, whereas without infrared glass, the maximum ultrasound  
Figure 6: Comparison results of ultrasound and thermal fields between the shear wave model and linear model under different input powers.  
pressure reaches 3.53 MPa. Meanwhile, in the absence of infrared glass, the maximum temperature reaches 71.9 \({}^{\circ}\)C. In contrast, in the presence of glass, the maximum temperature is 77.7 \({}^{\circ}\)C--about 8% higher than that without considering the shear waves. These results indicate that under the same input power, the maximum ultrasound pressure significantly decreases when considering shear waves in the presence of glass. It indicates that infrared glass has a particular blocking effect on the propagation of focused ultrasound. In addition, through the reflection and attenuation of the glass and multi-layer tissues, the maximum ultrasound pressure point coordinates moved from \(-5.10\) mm to \(-15.14\) mm. This indicates that at the same energy intensity, shear waves weaken the ultrasound pressure of HIFU and impact its focusing effect. At the same time, although infrared glass weakens the ultrasound pressure, it improves the efficiency of converting ultrasound energy into heat energy, thus increasing the tissue temperature.
## 4 Results and Discussion
### Pros and Cons of Using Machine Learning
#### 4.9.1 Advantages of Machine Learning  
* Enhanced Predictive Accuracy: ML algorithms are capable of processing and learning from vast amounts of data, detecting complex patterns that are not apparent through manual analysis or simpler models. This capacity significantly improves prediction accuracy compared to traditional methods, which often rely on fewer variables and assume linear relationships.
* Automation and Efficiency: ML automates the analysis of large data sets, reducing the reliance on manual data handing and analysis. This can lead to significant time savings and resource efficiency, which is particularly valuable in educational settings where early detection of at-risk students can lead to timely and effective interventions.
* Scalability: Unlike traditional methods that may become cumbersome as dataset sizes increase, ML algorithms excel at scaling with data, maintaining their effectiveness across larger and diverse datasets.
## 3 Results and Discussion
### Measurement of Mechanical Properties
The lowest level of deformation was found for pellets made of 100% tobacco stalks (P3) and 50% of coniferous sawdust (P4). The type of material used to produce pellets, the addition of a binder, and the water content affect, in particular, affect their mechanical properties [18]. According to research by [PERSON] et al. [19], pellets containing pyrolytic carbon are more durable than pellets made of compost or spruce sawdust. This is due to the presence of starch, which acts as a binder in the pelletization process. In addition, the researchers found that the addition of coniferous sawdust has a positive effect on the hardness of the tested pellets. Pellets made of 100% spruce sawdust turned out to be the hardest among the tested fuels. [PERSON] et al. [17], when examining pellets made of coniferous and deciduous sawdust and durable beech wood, noted the destructive force for the tested materials in the range from 166 N to 654 N. [PERSON] et al. [20] observed that the mechanical strength of barley straw pellets increased after adding coniferous sawdust. Research by [PERSON] and [PERSON] [21] shows that the mechanical durability of pellets from a mixture of different types of agricultural biomass is higher than pellets made of one type of agricultural biomass and are comparable to wood biomass pellets.
# Data Compression with a Time Limit
## 3 Lossless Compression
### Lossless Compression of Two-Dimensional Data with a Time Limit
#### 3.2.3 Compression of a Set of Non-Animated Images with Similar Scenes  
The data set \(D\) to be compressed was composed of 1000 images, for a total size of 659 MB (691,744,000 bytes).  
Single runs of the compression tools yielded the following pairs (\(b_{i}\), \(t_{i}\)).  
Table 5 shows that the fastest tool was BMP, while the slowest was JPEG2000. The best tool for output size was JPEG2000, while the worst was BMP. The calculated useful-setup was made up of the tools BMP, FELICS, JPEG-LS, and JPEG2000.  
With an assigned time budget, \(t_{1}\) = 58,000 ms then the optimal mix is given by the mix of the BMP and FELICS setups, and it is found in the center between them.  
All this is shown in Figure 5:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tool** & **Size of Output in Bytes** & **Compression Time in ms.** \\ \hline PNG & 367,459,743 & 89,924 \\ TIFF & 356,243,190 & 89,626 \\ JPEG-LS & 237,640,546 & 132,333 \\ JPEG2000 & 206,645,155 & 595,192 \\ BMP & 691,254,001 & 36,900 \\ FELICS & 316,963,000 & 75,920 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Non-animated images with similar scenes.  
Figure 4: Optimal mix, t = 69,000 ms.  
Here too, the optimum intersects the convex hull. The number of images to compress with BMP was 459, while for FELICS it was 541.  
The compression time was 58,600 ms, while the output size was 471 MB (493,429,735 bytes).  
If we increase the time budget to \(t_{2}\) = 130,000 ms, the optimal mix is given by the mix between the JPEG-LS and FELICS configurations, and it is very close to the JPEG-LS configuration.  
Figure 6 shows it:  
A total of 959 images will be compressed using JPEG-LS, the other 41 with FELICS.  
Figure 5: Optimal mix, t = 58,000 ms.  
Figure 6: Optimal mix, t = 130,000 ms.  
The compression time was 130,292 ms and the output size 245 MB (240,675,273 bytes). We had to use slightly more than the expected budget: about 292 ms more.
# Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programability
## 4 Architecture Design and Methodology
### Dynamic QoS Methodology  
Knowing that it is necessary to establish comprehensive dynamic service quality control within each slice, we imply that a single SDN controller controls one slice in our solution. We accomplish the synchronization of slice controllers by utilizing a shared database. This database presents a well-managed repository containing information about network functions, resources, service providers, architecture, and orchestration processes. This information supports scheduling similar services in a service group for a specific network slice. We propose a dynamic QoS traffic management methodology in which we utilize dynamic queue management and apply mechanisms to make intelligent decisions for max rates of service groups.  
Figure 1: The proposed model of 5G/6G multi-slice environment.
## 3 CFD Applied to Compost-Bedded Pack Barn Systems
### Boundary Conditions and Simulation Schemes  
Common considerations normally assumed in CFD modeling studies of animal facilities are incompressible, turbulent, and steady flow [28; 42]. Based on studies available in the literature, some distinctions regarding the boundary conditions in the CFD modeling of CBP systems with open and fully closed sides can be made (Table 1). It is important to note that Table 1 lists only general distinctions about boundary conditions that can be applied to modeling open and closed CBP systems. Therefore, for each case, procedures must be carried out to verify the applicable boundary conditions to adequately represent the heat and mass transfer processes that occur in the physical facility. Notably, the boundary and initial conditions for steady-state simulations must be defined based on the atmospheric boundary layer and experimental data [7; 83]. Regarding the boundary condition applied to the bedding surface, some distinctions need to be made. The modeling studies of CBP systems considered non-slip conditions and heat generation due to bedding composing activity, indicating constant [28] or variable temperature throughout the facility [29]. In future studies, it is also important to include the transfer of chemical species, such as moisture and water vapor generated via the accumulation of animal feces/urine and bedding composing, respectively. The inclusion of considerations related to the bedding composing process in CBP systems, including the generation of heat, water vapor, gases, and their inter-relationships with thermal environmental conditions is a field that still needs to be explored and improved in future CFD modeling studies. In animal facility modeling studies, the pressure-velocity coupling is usually calculated using the Semi-Implicit Method for Pressure-Linked Equations scheme (SIMPLE), and the spatial discretization is performed using the second-order upwind scheme [14; 15; 19; 29; 31; 44; 47; 77]. In the agricultural engineering field, specifically for modeling facilities for animal and vegetable production, the most commonly used approach to describe turbulent flows is the RANS via the standard k-\(\epsilon\), k-\(\epsilon\) RNG, k-\(\epsilon\) realizable, and Reynolds Stress Model--RSM [16; 39]. In facilities for poultry, cattle, and swine, the use of the standard k-\(\epsilon\) turbulence model is considered reliable for modeling turbulent flows due to achieving
## 2 Materials and Methods
### Treatment Conditions
#### 2.4.1 Modality + Exercise  
Patients received a 30-min simultaneous application of TENS and knee joint cooling on the involved leg. TENS device (TENS 3000 unit, Koalaty Products Inc., Tampa, FL, USA) and 4 self-adhesive square electrodes (5 \(\times\) 5 cm, Protens-tens electrodes 4848, Bio-Protech Inc., Gangwon-do, Korea; Figure 2A) were used to deliver a sensory level of asymmetric biphasic current (pulse duration: 150 \(\upmu\)s; pulse rate: 150 Hz [12; 14]. The intensity was set at the highest amplitude that could be reached without any muscle contraction or subjective uncomfortable sensations noted. For cryotherapy, 2 plastic bags (20 \(\times\) 40 cm) of crushed ice (1 L) were attached on the anterior and the posterior aspects of the knee joint, and secured using plastic wrap (Figure 2B) [28]. Afterwards, patients performed voluntary exercises  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **n/mean \(\pm\) 95\% CI** \\ \end{tabular} & \begin{tabular}{c} **Modality + Exercise** \\ **(_n = 15_)** \\ \end{tabular} & \
\begin{tabular}{c} **Exercise Alone** \\ **(_n = 15_)** \\ \end{tabular} &
\begin{tabular}{c} **\(p\)-Value** \\ \end{tabular} \\ \hline Age (year) & 20.6 \(\pm\) 1.2 & 21.8 \(\pm\) 1.6 & 0.25 \\ \hline Sex (M/F) & 9/6 & 10/5 & N/A \\ \hline Height (cm) & 170.7 \(\pm\) 4.6 & 173.0 \(\pm\) 3.0 & 0.42 \\ \hline Mass (kg) & 74.8 \(\pm\) 11.9 & 68.7 \(\pm\) 4.6 & 0.36 \\ \hline BMI (kg/m\({}^{2}\)) & 25.4 \(\pm\) 6.3 & 22.9 \(\pm\) 2.4 & 0.17 \\ \hline LBM (kg) & 56.0 \(\pm\) 6.7 & 53.7 \(\pm\) 4.5 & 0.58 \\ \hline Physical activity (hours per week) & 3.3 \(\pm\) 1.1 & 4.3 \(\pm\) 1.8 & 0.41 \\ \hline Pain period (months) & 46.1 \(\pm\) 19.4 & 45.0 \(\pm\) 16.8 & 0.94 \\ \hline Painful knee (L/R) & 7/8 & 8/7 & N/A \\ Injury type & & & \\ _Atraumatic_ & 9 & 8 & N/A \\ _Traumatic_ & 6 & 7 & N/A \\ History of knee surgery & 4 & 3 & N/A \\ Time since surgery (month) & 32.0 \(\pm\) 12.4 & 56.0 \(\pm\) 21.5 & N/A \\ Side-side ratio (\%) & & & \\ _MVIC_ & 92.2 \(\pm\) 10.9 & 95.1 \(\pm\) 12.5 & 0.69 \\ \hline \(CAR\) & 98.9 \(\pm\) 8.5 & 96.6 \(\pm\) 8.4 & 0.62 \\ \hline \(RTD\) & 87.0 \(\pm\) 10.1 & 101.2 \(\pm\) 12.8 & 0.26 \\ \hline \(PT\) & 93.3 \(\pm\) 6.9 & 97.2 \(\pm\) 7.6 & 0.37 \\ \hline \hline \end{tabular}  
* ADL: activities of daily living; BMI: body mass index; CAR: central activation ratio; CI: confidence intervals; F: female; L: left; IBM: lean body mass; M: male; MVIC: maximal voluntary isometric contraction; N/A: not applicable; PT: peak torque; R: right.  
\end{table}
Table 1: Descriptive statistics of the patients.  
for 30 min. The program contained warming-up, stretching, strengthening, and balancing exercises (Table 2). For the strengthening exercises, the exercise intensity (i.e., external load) was determined by the DAPRE technique [15].