esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results  
### Item Analysis  
Using the data of the current sample we calculated the internal reliability of the original tool (MBI-HSS) using Cronbach's coefficient alpha, which yielded the estimates of the whole scale as 0.87, 0.85 for Emotional Exhaustion, 0.80 for Depressionalization, and 0.75 for Personal Accomplishment.  
Item-analysis was conducted for MBI-HSS and the results were as shown in Table 2. For normality assessment, the study refers to guidelines by [PERSON], [PERSON] (1975) which stated that skewness and kurtosis must be within \(\pm 3\) and \(\pm 10\) to show univariate normality of the variable [28]. Based on result, skewness ranged from \(-1.06\) to \(1.30\), and kurtosis ranged from \(-1.28\) to \(0.53\), clearly showing that the items exhibited normality. Next, item discrimination was utilized to examine whether the items can effectively gauge the differences among participants [32]. Results showed critical ratios (\(t\) values) for each item were greater than \(1.96\), ranging from \(21.28\) to \(46.145\). This finding indicates that all items showed discriminant power, i.e., they are able to effectively measure the differences among participants. The item-total correlations values (one factor/dimension) ranged from \(0.11\) to \(0.66\), while the values of item-subscale correlations (3 factors/dimensions) ranged from \(0.38\) to \(0.77\) (values became larger). Further, some of the item-subscale correlation were low, for instance, MBI 16 (0.439), MBI 18 (0.383), MBI 12 (0.398), and etc. This indicated that some of MBI-HSS items showed inconsistency with low correlation presented by the items, thus the study believes there is a need to revise MBI-HSS model for healthcare professionals in hospitals within Saudi Arabia.
## 2 Materials and Methods
### Lesion Detection  
To deploy our DL model, we utilized the Detectron2 Framework [49], monitoring the performance metrics during training with Weights & Biases ([[https://docs.wandb.ai/guides/](https://docs.wandb.ai/guides/)]([https://docs.wandb.ai/guides/](https://docs.wandb.ai/guides/)) (accessed on 29 January 2025)). After experimenting with several CNN-based models on our MRI dataset, we empirically selected the Fully Convolutional One-Stage Object Detection (FCOS) model [50], as it achieved the best results during validation and testing in terms of detection performance. Nevertheless, the FCOS model has demonstrated success in medical imaging tasks [51; 52], further supporting its suitability for this study. Additionally, FCOS has important features for our use case:  
* Anchor-Free Design: particularly suited for datasets with diverse object sizes, such as lesions of varying dimensions across slices, as it avoids the constraints of fixed anchors and generalizes better to irregularly sized objects.
* One-stage Architecture: offers greater computational efficiency during training and inference, making it ideal for applications with limited computational resources and real-time applications.
* Focal Loss: crucial for addressing the high class imbalance inherent in dense sampling. Dense sampling generates many predictions, most of which correspond to the background class. Focal loss mitigates this by reducing the weight of easy background samples and focusing on harder-to-classify examples, such as lesion regions.
* Feature Pyramid Network (FPN): essential for detecting lesions of various sizes, as the FPN enables multi-scale detection by learning features at different resolutions.  
During the training of our DL model, we monitored standard evaluation metrics commonly used for COCO datasets ([[https://cocodataset.org/#detection-eval](https://cocodataset.org/#detection-eval)]([https://cocodataset.org/#detection-eval](https://cocodataset.org/#detection-eval)) (accessed on 1 February 2025)). The primary metric, Average Precision (AP) or mean Average Precision (mAP), combines precision and recall, calculated as the area under the precision-recall curve over a range of Intersection Over Union (IoU) thresholds. This metric is highly informative but also among the most challenging to achieve. Another relevant metric is AP50, which considers a prediction correct if the IoU with the ground truth exceeds 50%. These metrics were tracked during training to ensure the model was learning effectively and to identify and rectify any erroneous configurations. At the testing phase, in addition to the standard detection metrics, we calculated a detection confusion matrix and its associated metrics (detailed in Section 2.3), considering two classes: lesion and non-lesion. This required specifying a predefined IoU value, which served as the threshold for determining whether predictions were accurate.
## 3 The Gain Parameter Adaption and Scheduling Control Method
### The Adaptive Principle of the Influence Coefficient and Gain  
The above algorithm shows that the gain scheduling control method is suitable for the kinetic parameters transformation with the working conditions, and the real influence coefficients do not need to be known when the gain scheduling control method is applied in rotor online dynamic balance. On the other hand, the gain scheduling control method belongs to the open loop control and no feedback and compensation mechanism for inaccurate gain. The dynamic balance practice shows that the influence coefficient of the high speed spindle system will transform even at the same speed with its working environment, working time, signal testing, and analysis accuracy. The gain, calculated in the offline state, cannot absolutely meet the requirement of online dynamic balance control. To improve the precision of online dynamic balance, the method of gain self-adaption is put forward in this paper based on the influence coefficient online estimation.
## 4 Proposed Method
### Dual-Stream Architecture for Multi-Domain Fusion  
We propose a multi-domain fusion approach for modulation classification by developing a dual-stream CNN-based architecture, which is characterized by the 1D-CNN branch and 2D-CNN branch for processing time-domain data and DD-domain data, as illustrated in Figure 4 (Note that the specific valuesblue, e.g., dropout rate of 0.6, adopted in the neural network architecture are based on numerical tests and comparisons carried out in the experiments. Please refer to Section 5 for more details). (empiracally considering \(N=32\), \(M=64\)).  
Figure 4: Dual-stream neural network architecture for multi-domain fusion.  
The 1D-CNN branch processes time-domain data (\(\Theta\)) with an input shape of \((2,2048)\). The first block employs 32 kernels of \(3\times 1\) convolutions, followed by batch normalization and ReLU activation, and then performs \(2\times\) downsampling (\(2048\to 1024\)). The second block contains 64 convolutional kernels with the same processing flow (\(1024\to 512\)) thereafter. The third block uses 128 convolutional kernels and incorporates a dropout of rate 0.6. Finally, after transforming features to the dimensions of 32 via adaptive average pooling and passing through a fully connected layer, the branch outputs a 64-dimensional feature (i.e., \(\Psi_{1}\)).  
The 2D-CNN branch processes delay-Doppler-domain data (\(\Gamma\)) with an input shape of \((2,32,64)\). The first block uses 32 kernels of \(3\times 3\) convolutions, followed by batch normalization, ReLU activation, and pooling. The second block and third block respectively employ 64 and 128 convolutional kernels. Finally, the data passes through a \(2\times 2\) adaptive average pooling layer and a fully connected layer, and results in an output with a dimension of 64 (i.e., \(\Psi_{2}\)).  
The features from 1D-CNN and 2D-CNN branches are combined, resulting in a 128-dimensional \(\Psi_{\text{com}}\), which is then fed into an attention module. This module generates adaptive weights that can be applied to \(\Psi_{\text{com}}\) to emphasize the most significant aspects in features. The final classifier comprises two fully connected layers, with a dropout rate of 0.6 applied between them to prevent overfitting.
## 3 Results and Discussion
### Experimental Data
ensure the good quality of tensile samples. Thus, broader wall structures should not only be tested with samples in the middle of the wall structure but with more samples over the whole wall thickness. Figure 10 also displays the elongation at break for the tensile tests. The measured elongations at break are in the same order of magnitude as stated in [22; 47; 53; 55]. The elongations at break increase from 31.3% up to 35.7%, with increasing t\({}_{8/5}\) cooling times from 8 s to 35.1 s, which is the inverse relation as between the t\({}_{8/5}\) cooling times and the mechanical strength and hardness. This is due to coarser grains and therefore easier movement of the Taylor dislocations [52]. The values show a good fit to the logarithmic regression equation, which means the elongations at break could be described and predicted with knowledge of the t\({}_{8/5}\) cooling times. Figure 10: Tensile, yield strength and elongation at break as a function of t\({}_{8/5}\) cooling time from measured data. Figure 9: Hardness as a function of t\({}_{8/5}\) cooling time from measured data. In Figure 11 the impact notch test values are displayed. With the correct upscaling of the measured values from a quarter sample to a full size sample, the impact work is in the same order of magnitude as stated in [PERSON] et al. (2018). Though, in this study, no relation between the impact work and the \(\mathfrak{t}_{8/5}\) cooling time was found. However, all impact work values are within margin of error of each other. This is outlined by the dashed lines, which resemble the average of all impact work values and their standard deviation.
## 4 Results and Discussion  
### Inputs for Modeling Fallout Microstructures  
The physical measured data from SEM and TEM of the amoeboids are reported in Table 1. These samples were higher than expected with respect to FeO content and exhibited lower levels of SiO\({}_{2}\) than typical fallout glasses. The compositions measured using TEM resulted in generally higher iron levels compared with SEM-EDS measurements, which may be due to analytical bias or because the particular amoeboids measured by TEM happened to be more iron rich than those selected for SEM analysis. The bulk values reported here were used as inputs for the CALPHAD modeling. As described above, two-phase liquid compositions for both LFe-dispersed and LSi-dispersed microstructures were only measurable using TEM, therefore the bulk TEM compositions in Table 1 are used as model inputs. These data are the overall amoeboid compositional measurements of each morphology, but are not the compositions of each of the two phases in the amoeboids. The compositions of each liquid were used as comparison for the equilibrium calculations presented in Section 4.3, see dashed lines in Figures 3b,c and 4b,c. We performed the CALPHAD calculations using the TAF-ID thermodynamic database [39] that contains data for the SiO\({}_{2}\)-FeO-Al\({}_{2}\)O\({}_{3}\)-CaO-MgO multicomponent system. While the samples also contain oxides from the alkali group (K\({}_{2}\)O, Na\({}_{2}\)O totaling \(\sim\)2-3 wt%) and transition metals (MnO, TiO\({}_{2}\) totaling \(<\)1 wt%), the compositions in Table 1 neglect these species and are normalized to account for components within the SiO\({}_{2}\)-FeO-Al\({}_{2}\)O\({}_{3}\)-CaO-MgO system. However, alkali oxides are generally predicted to partition in the silicate-rich liquids and behave similarly to Al\({}_{2}\)O\({}_{3}\) in that they likely further suppress the miscibility gap to lower temperature domains and constrained composition ranges [40; 41].
## 3 Results  
### General  
Each strength test was conducted on several specimens to obtain the compressive, flexural, and bond strengths under the various curing conditions. The test results were analyzed to determine the change in the strength depending on the test variables. Table 7 presents the test results, which show that the curing temperature and curing time significantly affect the strength of polysulfide polymer concrete. In particular, the results show that the concrete's strength decreases with an increase in temperature, particularly at high curing temperatures.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Strength**} & \multirow{2}{*}{**Curing**} & \multicolumn{6}{c}{**Specimen**} & \multicolumn{2}{c}{**Ave.**} & **C.V.** \\ \cline{3-11}  & & & \multicolumn{2}{c}{**No. 1**} & \multicolumn{2}{c}{**No. 2**} & \multicolumn{2}{c}{**No. 3**} & \\ \cline{3-11}  & & & **MPa** & **ksi** & **MPa** & **ksi** & **MPa** & **ksi** & **MPa** & **ksi** & **\%** \\ \hline \multirow{10}{*}{Comp.} & \multirow{4}{*}{Time (h)} & 3 & 9.41 & 1.36 & 9.80 & 1.42 & 9.64 & 1.40 & 9.62 & 1.39 & 2.0 \\  & & 6 &
## 5 Conclusions  
A new 3D numerical modeling methodology is suggested to simulate the dynamic soil-pile-structure interaction in dry sand, in addition, the applicability of the proposed numerical model was verified by comparing with results of dynamic centrifuge tests. Parametric studies for various conditions were also performed to examine important characteristics of the dynamic pile behavior in dry sand. Detailed conclusions are as follows.  
1. The proposed numerical model employed non-linear elastic, Mohr-Coulomb plastic as a soil constitutive model with a hysteretic damping model to simulate nonlinear behavior and energy dissipation in soil media. The initial shear modulus, other various dynamic soil properties were determined by verified empirical relations and repetitive preliminary analysis. Simplified continuum modeling was adopted to simulate a semi-infinite boundary and to enhance analysis efficiency. Interface model was also properly applied to predict various interactions occurred at soil-pile boundary.
2. Through a series of validation procedures, the applicability and viability of the proposed model were verified. The representative internal pile response profiles predicted by the proposed numerical model consistently show good agreement with those observed from the centrifuge model test both for various input earthquake conditions and pile sizes.
3. From parametric study, the effect of the inertial force induced by the superstructure is dominant, whereas the effect of the kinematic force induced by soil movement is relatively not significant in dry sand. Pile length strongly affected not only in pile dynamic response trend according to the depth but also in rotational depth and occurrence depth of the peak bending moment. The pile head fixity was another important factor for dynamic soil-pile-structure interaction. It governed the peak bending-moment profile of pile and affected the dynamic responses of the system in conjunction with other factors, such as pile rigidity.  
[PERSON] organized the paperwork, made a analysis plan, performed numerical analysis and suggested the numerical analysis method; [PERSON] supported the numerical model verification by comparing test results helped the data analysis; all authors contributed to the writing of paper.  
Figure 21: Peak bending moment profiles for different pile head fixity (input acceleration: 0.13 g). (a) Pile length: 23 m (12.6 T); (b) Pile length: 8 m (4.4 T).  
**Acknowledgments:** This research was supported by a grant from (1) R&D Program (PK1902A4) of the Korea Railroad Research Institute, Republic of Korea. (2) Review of Environmental Impact Assessment funded by Korea Environment Institute.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# FSOpt_k: Finding the Optimal Anonymization Level for a Social Network Graph
## 6 Experimental Results
### Information Loss Metrics  
Information loss metrics evaluate the utility of an anonymous social network graph. In this section, three types of these metrics are used to evaluate the proposed algorithm: the optimal value of k obtained, the metrics for general graph properties, and the metrics for the graph's community structure. The first one concerns the graph's properties, such as the length of the shortest paths, while the second one refers to the community structure of the graph. If the social network graph properties are similar to those of the original graph, the algorithm will be better at retaining the social network graph utility. To test the proposed algorithm in terms of anonymous graph utility, three algorithms are used for k-degree-anonymity, which are similar in characteristics to FSopt_k.  
The first one was presented by [PERSON] et al. [48] in 2013, called vertex addition (VA). The next algorithm, KDVEM [49], was presented by [PERSON] et al. in 2015, and the third algorithm is UMGA [44], proposed by [PERSON] in 2017 for k-degree anonymization. In this section, three datasets of the real world are used. Table 4 shows the characteristics of those datasets. The first one is Dolphins [50] which shows the dolphin associations in a community. The second one, netscience [51], is a netscience network of scientists working  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Sets** & **Characteristics** & **Nodes** & **Edges** \\ \hline Email-Enron & 36,692 & 183,831 \\ DBLP & 317,080 & 1,049,866 \\ Youtube & 1,134,890 & 2,987,624 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attributes of the benchmark datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \(\mathcal{L}\) & **FSopt\_k** & **UMGA** \\
**Data Set** & & **(Hours:Minutes:Seconds)** & **(Hours:Minutes:Seconds)** \\ \hline Email-Enron & 0.05 & 00:00:09 & 00:15:36 \\ Email-Enron & 0.1 & 00:00:15 & 00:25:24 \\  & 0.2 & 00:00:20 & 00:46:51 \\ \hline DBLP & 0.05 & 00:08:25 & 1:08:25 \\  & 0.1 & 00:09:42 & 1:35:28 \\  & 0.2 & 00:09:52 & 1:58:41 \\ \hline \hline \multirow{3}{*}{Youtube} & 0.05 & 1:13:32 & 2:39:12 \\  & 0.1 & 1:15:40 & 3:55:19 \\ \cline{1-1}  & 0.2 & 1:16:21 & 5:03:06 \\ \hline Mean & & 00:09:29 & 1:32:26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of FSopt_k and UMGA with respect to the runtime.  
on network theory and experiments, and the latter is an undirected graph called Power Grid [52].
## 3 Dataset and Evaluation  
### Dataset  
The data for this paper were acquired from the National Solar Radiation Database (NSRDB) [34] for solar irradiance values in Denver, Seattle, and Boston, as shown in Table 1. These were selected due to their different geographical and meteorological conditions. Thus, the forecasting algorithm would not be specific to one location.  
The datasets contained hourly data for 8 years (1998-2005), including global horizontal irradiance and extraterrestrial radiation on a horizontal surface. Extraterrestrial radiation on a horizontal surface is the amount of solar radiation received at the top of the atmosphere on a horizontal surface. This will be referred to as extraterrestrial radiation throughout this paper (this is not to be confused with the solar constant. Further reading on solar radiation can be found in [PERSON]'s book _Solar Energy Engineering_[35]. These datasets were used to predict hourly values for the global horizontal irradiance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**City** & **Station Name** & **ID** & **Latitude** & **Longitude** \\ \hline Denver & Denver/Centennial & 724666 & 39.742\({}^{\circ}\) & \(-\)105.179\({}^{\circ}\) \\ Boston & Boston Logan & 725090 & 42.367\({}^{\circ}\) & \(-\)71.017\({}^{\circ}\) \\ Seattle & Seattle Seattle-Tacoma & 727930 & 47.46\({}^{\circ}\) & 122.317\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets are from the National Solar Radiation Database [34].  
By averaging every hour of the day over the given 8 years, 1D and 2D plots were created and are shown in Figure 2, respectively. While the 1D plot only captures the seasonal trend, the 2D representation also displays the daily seasonality which depends on the latitude of the location.
## 4 Experimental Analysis and Results
### Contribution Rates  
In Table 2 a grouped distribution of the number of edits or contributions performed by all of the contributors in our case-study area is shown. Figure 2 shows a graphical representation of this distribution with \(\mu=4.54\) and \(\sigma=2.39\). The value of \(\mu=4.54\) is the mean number of unique contributors to the objects in our case-study with a standard deviation of \(2.39\). This indicates that there is collaborative editing occurring for the majority of objects in our case study. There are also some interesting observations from the distribution of contribution effort amongst the contributors to these objects. We see from Table 2 that approximately \(11\%\) (\(10.779\%\)) of contributors have contributed \(100\) or more edits (substantial editing efforts). This corresponds to \(275,744\) edits from a total of \(316,949\) or \(87\%\). Just under \(52\%\) of contributors contributed \(5\) edits or less (\(2128\) from \(4128\) users) with \(74\%\) providing less than \(20\) edits. Of course these users may have edited other features which were not included in this case study. In total \(1096\) users contributed only one edit corresponding to \(26\%\) of contributors. At the opposite end of the contribution scales just over \(2.5\%\) of users contributed large bodies of edits (over \(500\) edits) whilst \(3\) users stand out as very high volume contributors with over \(5000\) edits.
###### Abstract  
In order to assess the effectiveness of reconstructed energy dissipation facilities (EDFs) in open channels at hydropower stations, hydraulic prototype observation (HPO) tests are conducted to investigate the characteristics of discharge flow and the dynamic response of hydraulic structures during sluice opening periods. While hydraulic model tests (HMTs) are commonly utilized in laboratory settings to study these characteristics, experimental conditions cannot fully replicate the real-world operations of such structures. HPO tests are employed to examine flow patterns, free water surface fluctuations, and pulsating pressure changes in open channels under varying flood discharge conditions (FDCs). Flow patterns in open channels are recorded via video; free water surface fluctuations are measured using total-station and laser rangefinder instruments; and pulsating pressure is monitored with pressure sensors and data collection systems. Flow pattern observations concentrate on addressing adverse water flow phenomena, such as turbulence, surging, and backflow. The examination of free water surface fluctuations aims to verify whether the height of the guide wall along the open channel fulfills the necessary requirements and assess the effectiveness of energy dissipation of the EDF. To comprehend the variations in pulsating pressure within the continuous sill and the base slab, nine measurement points were established across three sections perpendicular to the continuous sill's axis on three distinct elevation levels. Additionally, three measurement points were positioned on the reinforced base slab along the open channel's axis. The findings indicate that the impact on the continuous sill caused by discharging water is more severe when the discharge rate of a single sluice gate reaches 500 m\({}^{3}\)/s than in other FDCs. To ensure the safe operation of open channels during flood discharge, the discharge rate for each sluice gate should be reduced to 250 m\({}^{3}\)/s. The dominant pulsation induced by discharge flow falls within the low-frequency range, resulting in minimal adverse effects on the stilling basin and guide wall. The flow pattern within the stilling basin remains stable under various FDCs, with no significant adverse hydraulic phenomena observed. Parameters, including free water surface fluctuations and pulsating pressure variations, lie within acceptable ranges. These observations suggest that the arrangement of the reconstructed energy dissipation facilities is generally effective following technical reconstruction.  
h  
hydropower; energy dissipation facilities; hydraulic prototype observation; flow patterns; sluice gates