esa-sceva/satcom-chunk-collection huggingface dataset:
# Amplitude Distribution of Partial Discharge Signals on Tunnel-Installed High-Voltage Cables
## 4 Experimental Verification
The advantage of the time-domain peak-to-peak is that it is simpler and more intuitive, and is suitable for applications  
Figure 8: Signals under different laying configurations. (**a**) Triangle arrangement, on the ground. (**b**) Triangle arrangement, 0.5m above the ground. (**c**) Horizontal arrangement, on the ground. (**d**) Horizontal arrangement, 0.5m above the ground. Figure 7: Laying configurations of cables. (**a**) Triangle arrangement, on the ground. (**b**) Triangle arrangement, 0.5m above the ground. (**c**) Horizontal arrangement, on the ground. (**d**) Horizontal arrangement, 0.5m above the ground. where the requirement for accuracy is not very strict. One of the reasons for the error is that the RG223 coaxial cables used are semi-flexible cables with a small radius, so it is difficult for them to be tightly arranged. In the experiment, the cable center spacing, \(d\) in mode a and b was in fact slightly larger than 2r, and the distance \(R\)-\(b\) in modes a and c was slightly larger than \(r\), so the results were generally smaller than the theoretical values. In order to verify the effect of the cable center spacing, \(d\) on the amplitude distribution, we compared the average amplitude spectra of modes a and c (or modes b and d) in Table 3. It can be seen that the \(I_{\text{major}}\)/\(I_{\text{minor}}\) ratio decreases from 4.04 to 2.52 when the equivalent center spacing, \(d\) increases from 5.35 mm to 126 mm. This change is consistent with the decreasing trend of the characteristic admittance ratio \(\alpha\) in Figure 5c. In order to verify the influence of R-b on the amplitude distribution, the average amplitude spectra of modes a and b (or mode c and d) in Table 3 were compared. The \(I_{\text{major}}\)/\(I_{\text{minor}}\) ratio decreases from 4.04 to 2.38 when R-b increases from 2.675 mm to 500 mm. This change also corresponds to the decreasing trend of the characteristic admittance ratio \(\alpha\) in Figure 5d. It is known from Equation (17) that there is a proportional relationship between \(\alpha\) and \(I_{\text{major}}\)/\(I_{\text{minor}}\). Therefore, the trend of the amplitude distribution caused by the change of the laying parameters is consistent with the analysis based on the characteristic admittance model, which verifies the accuracy of the model and its conclusion.
## 4 Simulation Results and Performance Analysis
### Simulation Results  
A large number of initial populations can speed up signal search time, but is also requires a higher hardware cost. The initial population size, crossover and mutation probability value are set, based on an experience rule to be successfully utilized in the GC process to acquire desired signals. Figure 3 shows the examples for the evolution values of two parameters (Doppler frequency and code delay) for C/A-code. The protruding \"\(\bigcirc\)\" in Figure 3 is caused by mutation in GC. The two figures (Figure 3a,b) show that in utilizing the adaptive scaling scheme, the signal search space will gradually narrow down its space after each iteration to ensure the estimated signal parameter to converge to global optimal value and also reduce iteration times. Contrariwise, a longer acquisition time is required without using the scheme. Figure 4 illustrates the 2D signal search path using the proposed method in application to different signal types, which include C/A-code, P-code and BOC. These diagrams show that the use of GC can effectively reduce search times. On the other hand, Figure 5 analyzes the relation between the number of initial population and search times regarding C/A-code. The result demonstrates that when the number of initial population adopts a roughly 1.5 times code period length of C/A-code (1023 chip), the signal can be accurately acquired within fewer search times under C/No = 45 dB-Hz. When the number of the initial population is below 0.5 times code period length, the proposed method cannot acquire the signal. Such a result is the same with P-code and BOC. Figure 2: _Cont._Figure 3: Evolution curve of signal acquisition for C/A-code; (**a**) Doppler frequency; (**b**) code delay. Figure 4: Number of initial population _versus_ iteration (C/A-code; C/No = 45 dB-Hz). Figure 5: Signal acquisition results of proposed method; (**a**) C/A-code; (**b**) BOC-code; (**c**) P-code. Figure 5: Cont.
## 6 Material and Method
### Single Source Shortest Path Algorithm
```
1:Initialize all \(F\) to false, all \(C\) to \(\infty\) and all \(U\) to \(\infty\)
2:Set \(F[s]=\) true, \(C[s]=0\), \(U[s]=0\), \(sum=1\)
3:while\(sum>0\)do
4: kernel 1 (\(G\),\(F\),\(C\),\(U\)) with 1 thread per node
5: kernel 2 (\(F\),\(C\),\(U\)) with 1 thread per node
6:\(sum=\) reduce_sum (\(F\))
7:Build path backward starting at vertex \(e\) using \(G\) and \(C\)
```  
**Algorithm 3** Harish SSSP (\(G\), \(s\), \(e\)) [28]  
```
1:for all node _tid_ in parallel do
2:if\(F[tid]\)then
3:\(F[tid]=\) false
4:for all\(to\)-neighbors \(i\) of _tid_ do
5:if\(U[i]>C[tid]+W[i\), \(tid]\)then
6:\(U[i]=C[tid]+W[i\), \(tid]\)
7:\(F[tid]=\) true
```  
**Algorithm 4** kernel 1 (\(G\), \(F\), \(C\), \(U\)) [proposed implementation]  
Two optimizations were done in the proposed CUDA implementation to further improve its performance. First, as an SSSP instance is solved for each pair of DPs, the SSSP instances are solved in batch. This improves the level of parallelism exploited, but also increases the amount of memory needed on the GPU so care must be taken not to use a batch size that is too large. It was found through experimentation that solving 10 SSSPs simultaneously was a good compromise. To keep track of which batches are active and have not converged yet, an array of indices that is compacted at every iteration of the while loop is kept. This compaction is done on the GPU using the CUDA Cub library (Cub, 2017). Because multiple batches are processed concurrently, the sum reduction operation in Algorithm 3 must be converted to a segmented sum, also provided by the CUDA Cub library. Although performed in parallel, the compaction operation and the segmented sum operation are quite costly. A second optimization is to do multiple iterations of the while loop in Algorithm 3 before doing the compaction and the segmented sum. The while loop executes extra few iterations at the very end, but the benefit of skipping the compaction and the segment sum is far superior. The performance improvement of those two optimizations is measured in Section 7.2.
## 3 Results and Discussion
### Analysis of the Aerodynamic Load on the Impeller
Because of the blowing flow, the TEB model showed lower amplitude at \(f_{\rm i}\) in contrast to the UGV model, particularly in the shroud region, wherein the decrease in the amplitude was up to 60%. The SIS model showed lower amplitude in the shroud region in contrast to the UGV model, wherein the amplitude decrease was about 18%. The flow phenomenon in the self-induced slot improved the wake style from the pure wake to the weak wake, although it could not compensate for the wake deficit enough as with the trailing edge blowing method. Furthermore, it reduced the wake intensity with the advantage of not requiring energy from the outside. However, the SIS model showed higher amplitude in the hub region, which was the price for reducing the amplitude at \(f_{\rm i}\) in the shroud region. From hub to shroud, Figure 14 shows the comparison of the aerodynamic load distribution of the amplitude at the leading edge of the impeller at \(f_{\rm m}\) and \(f_{\rm i}\), respectively. As shown in Figure 14, the amplitude distribution at \(f_{\rm i}\) had little difference in three radial inlets, reflecting that the trailing edge blowing and the self-induced slot methods had no or little effect on the uniform flow field along the circumference direction. Moreover, as shown in Figure 14, from hub to shroud, the amplitude at \(f_{\rm i}\) showed an increasing trend in the UGV model. In the TEB model, because of the trailing edge blowing, the amplitude was lower compared with that of the UGV model. In the SIS model, the flow phenomenon in the self-induced slot changed the amplitude distribution. On the one hand, the self-induced slot decreased the amplitude near the shroud region, with an average reduction in the amplitude of about 15% compared with the UGV model, indicating a positive effect on impeller safety. On the other hand, the self-induced slot increased the amplitude near the hub region compared with the UGV model, which is a negative effect. As the active control method needs extra power, the passive control method needs to sacrifice the performance, and the price in the SIS model was an increase in the amplitude at \(f_{\rm i}\) near the hub region. Relatively, the amplitude at \(f_{\rm i}\) was higher near the shroud region, which is more of a threat to the impeller safety. The wake of the guide vane had little influence on the impeller hub region in this compressor. Therefore, decreasing the amplitude near the shroud region was more meaningful, and appropriately increasing the amplitude near the hub region was acceptable. Figure 14: The aerodynamic load distribution from hub to shroud at the leading edge, including the comparison of (**a**) the amplitude at \(f_{\rm m}\) and (**b**) the amplitude at \(f_{\rm i}\) in different radial inlet models.
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
## 2 Materials and Methods
### Environmental Loading Factors  
The loading of the crust due to environmental factors, such as varying atmospheric and hydrological conditions as well as non-tidal ocean variations are well known, but not implemented in the GNSS computation. The loading factors have generally amplitudes of a few millimetres up to a few centimetres in the Antarctic continent, but as the phenomena show a clear annual variability it may distort especially campaign measurements as well as long term trends.  
We compared pre-computed loading time series from three service providers as shown in Table 3. These services provide pre-computed time series (EOST and IML) and gridded data (GFZ) of hydrological loading, non-tidal atmospheric loading and non-tidal ocean loading. All services provide data in both centre-of-the-mass (CM) and centre-of-the-solid-earth (CE/CF) frame. All three services have similarities and differences. They all provide data starting from approximately 1980 and update their products continuously. There are altogether 20 different products available, but only two non-tidal ocean and two atmospheric loading products cover the whole time period (2003-2017). We decided not to use the hydrological loading computed from hydrologic models as the models have severe problems on ice covered regions and cannot reliably present the annual variability.
# Comparison of Diagnostic Accuracy of Physical Examination and MRI in the Most Common Knee Injuries
## 2 Materials and Methods
### Magnetic Resonance Imaging of the Knee Joint  
All patients included in the study provided MRI images with a radiologist description. All MRI was performed on 1.5 T coils; however, there was no standard protocol of examination. The sequences used in the examination were selected by a supervising radiologist at the site of examination. MRI images were available to the orthopaedic surgeon only after having performed a standardized physical examination, in order not to bias the diagnosis. The evaluation of articular cartilage, menisci and ACL were made by the radiologist at the time of the MRI examination. All cartilage MRI findings were described with the use of the International Cartilage Repair Society (ICRS) grading system [20]. All MRI findings stated in the radiologist report of the examination were then transferred to the study protocol for later analysis. Moreover, it is worth mentioning that the average time between MRI and arthroscopy was 3.6 months (SD = 3.33), which is representative of the actual time that patients await surgery. Such a short time between MRI and arthroscopy should not influence the MRI's diagnostic accuracy.
# Aging Performance and an Improved Evaluation Method for PE80 and PE100 Pipelines for Urban Gas
## 2 Accelerated Aging Experiment  
### Sample Tube Preparation  
At present, PE pipes are mainly used for medium- and low-pressure gas transport [14; 15; 16; 17]. Commonly, the design pressure of these PE pipes is 0.4 MPa and the operating pressure is 0.2 MPa. SDR11 DN63 PE80, SDR11 DN110 PE80, and SDR11 DN63 PE100 pipes produced by the same company were used in the thermal oxygen accelerated aging experiments mentioned in this paper (Figure 1). In the aging experiments, the DN63 pipes should be longer than 320 mm and the DN110 pipes should be longer than 490 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Aging Type** & **Mechanism** & **Description** \\ \hline \multirow{2}{*}{Photo-oxidative} & \multirow{2}{*}{Photodegradation} & The polymer chains will break down under prolonged exposure to ultraviolet (UV) light, causing surface embrittlement and discoloration and impacting resistance reduction. \\ \hline \multirow{2}{*}{Thermo-oxidative} & Chain Scission and Crosslinking & The polymer chains will break or crosslink in an environment with oxygen, which reduces the flexibility and strength of the PE pipe. \\ \hline \multirow{2}{*}{Stress Cracking} & Crack Initiation and Propagation & Micro-cracks on a PE pipe may occur in service, which will grow under mechanical stress. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Aging types for polyethylene pipelines.  
Figure 1: PE pipes used for aging test: (**a**) PE80 SDR11 DN63 (melt mass flow rate (190 \({}^{\circ}\)C/5 kg): 0.83 g/10 min); (**b**) PE100 SDR11 DN63 (melt mass flow rate (190 \({}^{\circ}\)C/5 kg): 0.23 g/10 min); and (**c**) PE80 SDR11 DN110 (melt mass flow rate (190 \({}^{\circ}\)C/5 kg): 0.83 g/10 min).
## 3 Results
### Effect of Dipping on TSS  
Maintaining the original TSS content is a key requisite in storage to preserve fresh-cut apples sensory qualities. Indeed, sweetness is a fruit quality trait which is crucial for consumer acceptance [52]. In particular, sweetness in apples is related to sucrose, glucose and fructose content, with 50% of sugar present being fructose [53].  
Just after cutting, Golden Delicious apple fresh-cuts were characterized by a TSS content of about 12 \({}^{\circ}\)Brix; at this commercial stage, the changes associated with ripening were already accomplished, obtaining the maximum soluble levels.  
During the storage at 20 \({}^{\circ}\)C (after 2 and 4 h) the water control (DW) maintained the initial TSS values while a reduction was observed in all the other treatments (Figure 3).  
This occurrence could be due to the highest water loss recorded in DW samples (about 0.4% fresh-weight reduction) in comparison with losses observed in the other treatments (about 0.2% fresh-weight reduction). The TSS changes related to the water loss rate were in agreement with studies carried out during the storage of apple fresh cuts [2; 54]. Both potato PE and standard compounds used in this experiment demonstrated a protective effect in minimizing the water loss in comparison with DW. In particular, the role of AA in lowering physiological loss in tissue weight was observed in different fruit species [55; 56]. The reduction of TSS in PE and PW treated apple cubes could be related with the enhanced respiration activity during storage at room temperature, as observed by [PERSON] and co-workers [57]. It is important to stress that using PE and PW extracts, TSS values remained at acceptable levels within a changing range of 8%, similarly to what obtained in other studies, even in _'Golden Delicious'_ apple slices where the TSS of samples did not substantially change during storage [57; 58].  
In contrast to the effect observed at 20 \({}^{\circ}\)C, in which a decrease in the TSS values was recorded, a weaker TSS decrement was measured in samples stored at 4 \({}^{\circ}\)C (Figure 3). This could be attributed to the reduction in the respiratory rate, as observed by several authors in analogous conditions. The lowest temperature was more efficacious to maintain the TSS degree, in accordance with several researches reporting no substantial changes in apple slices: [59] found values that ranged from 14.6  
Figure 2: Visual appearance of browning in fresh-cut apples treated with different preservative media (sample codes are described in Table 1). Samples were stored at 20 \(\pm\) 1 \({}^{\circ}\)C (2 and 4 h) and 4 \(\pm\) 1 \({}^{\circ}\)C (1 and 3 days).  
to 12.8 \({}^{\circ}\)Brix for cv. Gala apples coated with alginate and stored at 5 \({}^{\circ}\)C. [PERSON] et al. [60] obtained similar results treating apples (cv. Gala) with a solution of ascorbic acid (1%), citric acid (0.5%), calcium chloride (0.25%), and sodium chloride (0.7%) stored at 2 \({}^{\circ}\)C [61].
## 2 Materials and Methods
### Data Analysis
#### 2.5.2 Soybean Grain Yield Estimation Using Machine Learning
Iteratively developing this hyperplane allows for the lowest possible misclassification error while forecasting continuous outputs [18; 43; 44]. SVM only considers training samples that are nearest to the ideal class boundary in feature space. In this study, Sequential Minimal Optimization (SMO) algorithm was used for SVM optimization. ANN repeatedly modifies its neural weights and thresholds to adapt to the computing environment. The network's training is considered successful when the output error reaches the expected value. An input layer, one or more hidden layers, and an output layer, each having nodes (or neurons), make up the layers-node combinations of ANN. The neurons or nodes receive inputs from the nodes of previous layers and provide output to the nodes in the next layer [42; 45; 46]. With this configuration, ANN has the ability to robustly compute complex relationships between inputs and outputs. This study utilized a Fate of 0.01, backward propagation and two hidden layers with ten and three nodes, respectively, as deemed suitable for optimization problems. For all the ML and statistical models, ten splits of training-testing datasets and two groups of input variables, (1) REFs and (2) REFs+VIs, were adopted. Using a 5% increment of the training dataset, these training-testing datasets ranged from 50:50 to 95:5 for ten split ratios. The train-test splits were developed to determine and assess the ideal training-testing data size for optimal model performance, particularly for small- to medium-sized datasets like the 108 data points used in this study. The trained models were applied to the training dataset, the testing dataset, and the entire dataset to assess the model estimation performances. \[\text{rRMSE }(\%)=100\times\frac{\sqrt{\frac{\sum_{\text{m=1}}^{n}(\text{ SY}_{\text{E}}-\text{SY}_{\text{m}})^{2}}{n}}}{\text{mean}(\text{SY}_{\text{m}})} \tag{1}\]  
where \(\text{SY}_{\text{m}}\) is the measured and \(\text{SY}_{\text{E}}\) is the estimated SY. The R statistical computing software (version 4.3.1; RStudio, Inc. Boston, MA, USA) was used for all ML and statistical modeling, metrics (r and rRMSE) computations, and other analyses at 5% significance. The model's performance and the accuracy of the SY estimation were assessed using the Pearson correlation (r) and relative root mean square error (rRMSE, %, Equation (1)).
## 4 CT Scan and Image Processing
### Image Processing  
Each sample's images were used without the top and bottom 10 mm to obtain stable void data. The scanned images consisted of voxels (volume elements) with different gray scales. The resolution of the images was 0.11 mm \(\times\) 0.11 mm, and the distance between adjacent images was 0.11 mm. Image enhancement was the first step for the scanned images to make unclear details clearer and to reduce uninteresting regions. CT scanning of the sliced images was conducted, and median filter and grey level transformation were used [29]. The second step was image segmentation, which was based on ring region and Otsu method [30]. The circle image was cut into 6 different rings to reduce the brightness change in the radial direction, and the Otsu method was used for each ring for the segmentation and then combined back into one picture [31]. For example, the image segmentation results for AC- 20 is shown in Figure 3; from left to right is the scanned image, voids, mortar and aggregate, which are plotted in white at the same position as in the scanned image.  
Then, post-processing was applied, and the particle diameters and void diameters were calculated by assuming that all of the particles in the picture were circles [24; 29; 32]. After a 2D void analysis, the 2D slice images were integrated and 3D models were constructed using industrial CT software; the different materials were identified as coarse aggregate, mortar and voids. In this section, mortar with NMPS 2.36 mm and no air voids was used, since this NMPS is widely selected. Then, the voids were classified by volume and characterized using Equations (4) and (5).  
The 2D equivalent diameters of the particles were as in Equation (4):  
\[\text{Feremeter2}=2\sqrt{\text{Area}/\pi} \tag{4}\]  
where Feretmeter2 is the equivalent diameter (mm), and Area (mm\({}^{2}\)) is the area of the voids in the image.  
The 3D equivalent diameters of the particles were as in Equation (5):  
\[\text{Feremeter3}=2\sqrt[3]{(3\times\text{Volume})/(4\times\pi)} \tag{5}\]  
where Feretmeter3 is the equivalent diameter (mm), and Volume (mm\({}^{3}\)) is the area of the voids in the image.
## 3 Methodology  
### Overall Architecture  
The current mainstream domain adaptation frameworks often require a retraining of the deep learning models with large amounts of unlabeled data in the target domain and large amounts of labeled data in the source domain. That is, retraining with large amounts of unlabeled and labeled data is required for each change in the target domain. This is difficult to apply for target domains with an insufficient amount of data. To tackle this problem, our AD-DAF framework fuses a deep semantic segmentation model with an anomaly detection idea, emphasizing the combination of knowledge in both the source and target domain. The introduction of the anomaly detection idea allows the AD-DAF framework to be applied to multiple target domains simultaneously with only one pre-training. This drastically reduces the need for retraining with large amounts of data. The overall architecture of our anomaly detection-based domain adaptation framework is shown in Figure 1.  
It can be seen from Figure 1 that our AD-DAF framework mainly consists of three processes: source domain-based pre-training, anomaly detection for the target domain, and local region reclassification. The source domain-based pre-training process is responsible for learning the building extraction model from the source domain in order to obtain the initial results of the building extraction for the target domain. The process of the anomaly detection for the target domain is responsible for detecting misclassification from the initial results of the building extraction. The local region reclassification process is designed to re-extract buildings from the above remaining results. The details are given in the following Section.