esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Species distribution models (SDMs) based on fine-scale environmental data may reduce the uncertainty in predicting species distributions. However, many scientists have also projected the robust potential distributions of species using environmental data of different scales and found that the potential distributions modeled using SDMs are scale dependent. This may be due to the impact of the scale effect on species richness (as well as on multi-species distributions). To eliminate the impact of the scale effect, we aim to develop an improved method to integrate different scales into species distribution models. We use protected areas as the study regions and propose the hypothesis that there is a spatial element to the threat of invasive species for protected areas under climate change. We use Maxent to compute the current and future invasion ability and invasion inequality of invasive species for protected areas based on the potential distributions of species across different scales to evaluate the risk of invasive species. We find that an increase in the number of present records could reduce the accuracy of SDMs. There is a significant linear relationship between the fine-scale and coarse-scale risk of invasive species of alien plants in protected areas, and an appropriate scale should thus be selected to assess species risk based on this linear relationship of invasive risk. There is a significant relationship between the potential of LAPS to invade protected areas and the invasion inequality of IAPS in protected areas across all scales, and 5.0 arcminutes is the most appreciate scale to evaluate the risk of IAPS for protected areas under climate change based on principal component analysis. We provide new insights into the use of species distribution models coupled with different spatial scales to analyze the regional risks associated with species and to assess regional biodiversity.  
China; climate change; invasion risk; invasive plant species; Maxent; protected areas; principal component analysis; scale effect Article
# High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
## 1 Introduction  
In machine learning (ML), a hyperparameter is a parameter whose value is given by the user and used to control the learning process. This is in contrast to other parameters, whose values are obtained algorithmically via training.  
Hyperparameter tuning, or optimization, is often costly and software packages invariably provide hyperparameter defaults. Practitioners will often tune these--either manually or through some automated process--to gain better performance. They may resort to previously reported \"good\" values or perform some hyperparameter-tuning experiments.  
In recent years, there has been increased interest in software that performs automated hyperparameter tuning, such as Hyperopt [1] and Optuna [2]. The latter, for example, is a state-of-the-art hyperparameter tuner which formulates the hyperparameter optimization problem as a process of minimizing or maximizing an objective function that takes a set of hyperparameters as an input and returns its (validation) score. It also provides pruning, i.e., automatic early stopping of unpromising trials. Moreover, our experience has shown it to be fairly easy to set up, and indeed we used it successfully in our research [3, 4].  
A number of recent works, which we shall review, have tried to assess the importance of hyperparameters through experimentation. We propose herein to examine the issue of hyperparameter tuning through a significantly more extensive empirical study than has been performed to date, involving multicontinuous algorithms, datasets, metrics, and hyperparameters. Our aim is to assess just how much of a performance gain can be had per algorithm by employing a performant tuning method.  
The next section presents an account of relevant previous work. Section 3 describes the experimental setup, followed by results in Section 4. We discuss our findings in Section 5, and end with concluding remarks in Section 6.
## 3 Comparative Retrieval of Suspended Sediment
### Comparative Sediment Retrieval Using Synchronous Imagery  
Figure 4 presents the SSC retrieval results from the Hangzhou Bay images acquired on 12 December 2011: (a) the HJ CCD image at 10:39 (30 m resolution) and (b) the GOCI image at 10:29 (500 m resolution). The spatial distribution pattern of SSC is consistent between the two images.  
Figure 3: Regression relationship between measured suspended sediment concentration (SSC) and measured GOCI Rrs(b8)/Rrs(b6) at modeling points in Hangzhou Bay.  
Table 1 reports the minimum, maximum, mean, and standard deviation of SSC retrieved from the HJ CCD and GOCI images on 12 December 2011. The HJ CCD image yielded a mean SSC of 627.0 mg/L, whereas the GOCI image produced a mean of 566.4 mg/L, indicating that the SSC retrieved from the HJ CCD data is higher than that from the GOCI data.  
Given that the selected HJ CCD and GOCI images were acquired nearly simultaneously (with a time difference of roughly 10 min) and the development of both the atmospheric correction and the SSC inversion model has been validated using in situ data, the SSC results from the two sensors are expected to exhibit a certain degree of correlation. To analyze the relationship between the SSC from the HJ CCD and GOCI images, the 30 m resolution HJ CCD SSC data were resampled to 500 m. In the resampling process, the mean SSC was computed from all HJ CCD pixels within each 500 m \(\times\) 500 m area corresponding to a GOCI pixel. The resampled HJ CCD SSC was then compared with the 500 m GOCI SSC through correlation analysis. Figure 5 shows that the spatial distribution of SSC is consistent between the resampled HJ CCD image and the GOCI image.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Imagency**} & \multicolumn{2}{c}{**Spatial**} & \multicolumn{2}{c}{**Hangzhou**} & \multicolumn{1}{c}{**Bay SSC** (mg/L)} \\ \cline{3-6}  & **Resolution (m)** & **Minimum** & **Maximum** & **Mean** & **Standard Deviation** \\ \hline HJ CCD & 30 & 140.8 & 1533.4 & 627.0 & 215.3 \\ GOCI & 500 & 134.9 & 1512.9 & 566.4 & 206.7 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical values of suspended sediment concentration retrieval results from HJ CCD and GOCI imagery in Hangzhou Bay on 12 December 2011.  
Figure 4: Retrieval results of suspended sediment concentration in Hangzhou Bay on 12 December 2011: (**a**) HJ CCD image at 10:39 (30 m); (**b**) GOCI image at 10:29 (500 m).  
By precisely matching geographic locations, 30,145 paired SSC data points were obtained from the GOCI and HJ CCD images over Hangzhou Bay. Regression analysis of these extensive data pairs revealed a strong correlation between the HJ CCD-derived and GOCI-derived SSC, with a coefficient of determination (R\({}^{2}\)) of 0.8392, and an RMSE of 82.15 mg/L.
# Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments
## 3 NHPP Software Reliability Growth Model (SRGM)
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Model** & **MSE** & **PRR** & **PP** & **SAE** & **R\({}^{2}\)** & **AIC** & **BIC** & **Bias** & **PRV** & **RMSPE** & **MAE** & **MEOP** &
\begin{tabular}{c} **TS** \\ **(\%)** \\ \end{tabular} \\ \hline DPF1 & 2.8201 & 0.0276 & 0.0270 & 12.7389 & 0.9919 & 58.6958 & 60.6354 & 0.0103 & 1.4321 & 1.4321 & 1.5924 & 1.4154 & 3.6893 \\ \hline DPF2 & 2.7946 & 0.0283 & 0.0275 & 12.7515 & 0.9919 & 58.6274 & 60.5670 & 0.0078 & 1.4256 & 1.4256 & 1.5939 & 1.4168 & 3.6726 \\ \hline DS & 8.2096 & 7.3679 & 0.6177 & 20.9540 & 0.9704 & 69.6251 & 70.5950 & \(-\)0.7059 & 2.6305 & 2.7236 & 2.0954 & 1.9049 & 7.0377 \\ \hline GO & 4.0245 & 0.2932 & 0.1627 & 19.4170 & 0.9855 & 57.7076 & 58.6775 & \(-\)0.0522 & 1.9120 & 1.9127 & 1.9417 & 1.7652 & 4.9275 \\ \hline IS & 4.0555 & 0.4815 & 0.1905 & 17.0520 & 0.9868 & 60.1451 & 61.5998 & \(-\)0.1725 & 1.8126 & 1.8208 & 1.8947 & 1.7052 & 4.6926 \\ \hline YID & 7.7536 & 0.0893 & 0.1027 & 24.4096 & 0.9748 & 58.2593 & 59.7140 & 0.0000 & 2.5187 & 2.5187 & 2.7122 & 2.4410 & 6.4885 \\ \hline PNZ & 4.5632 & 0.4818 & 0.1906 & 17.0566 & 0.9868 & 62.1389 & 64.0786 & \(-\)0.1722 & 1.8128 & 1.8210 & 2.1321 & 1.8952 & 4.693 \\ \hline PZ & 5.2153 & 0.4890 & 0.1917 & 17.0459 & 0.9868 & 64.1689 & 66.5934 & \(-\)0.1758 & 1.8125 & 1.8210 & 2.4351 & 2.1307 & 4.6931 \\ \hline TC & 5.6420 & 0.4307 & 0.1888 & 18.3723 & 0.9857 & 64.2519 & 66.6765 & \(-\)0.1203 & 1.8906 & 1.8945 & 2.6246 & 2.2965 & 4.8813 \\ \hline VTUB & 2.9516 & 0.0320 & 0.0296 & 12.6030 & 0.9925 & 59.5514 & 61.9760 & \(-\)0.0664 & 1.3688 & 1.3704 & 1.8004 & 1.5754 & 3.5306 \\ \hline NEW & 2.7776 & 0.0606 & 0.0514 & 14.4914 & 0.9920 & 57.2427 & 59.1823 & \(-\)0.0070 & 1.4213 & 1.4213 & 1.8114 & 1.6102 & 3.6615 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Criteria values for model comparison of dataset 2. Figures 3 and 4 present the 95% confidence intervals of the datasets, the formula of which is given by  
\[\hat{\mathfrak{m}}(t)\pm\mathsf{z}_{\alpha}\sqrt{\hat{\mathfrak{m}}(t)} \tag{15}\]  
where \(z_{\alpha}\) is defined as the \(100(1-\alpha)/2\) percentile of the standard normal distribution [30]. Figure 1: Three criteria values of top three models for dataset 1. (a) MSE; (b) PRR; (c) PP. Figure 3: Confidence intervals (95%) of dataset 1. Figure 2: Three criteria values of top three models for dataset 2. (a) MSE; (b) AIC; (c) RMSPE.
# CP-SSD: Context Information Scene Perception Object Detection Based on SSD
## 3 Cp-Ssd  
CP-SSD (Context Perception SSD) is a single-shot object detection network based on SSD, which consists of three main parts,--the SSD model, context information scene perception module--which is used to capture local context information of different sizes, and the semantic activation module, which enriches the semantic information in a self-supervised manner. Please refer to Figure 1 for the structure of CP-SSD.  
In SSD, VGG16 is used as the backbone network to generate the low-level detection feature map **U**. Based on that, the feature map **U** is continuously downsampled through a series of convolutional layers with a stride of 2 (i.e., fc7 to conv9_2) by applying anchors of different sizes and aspect rates in a hierarchical manner, so as to detect objects of small to large sizes.  
In the context information scene perception module, we used multiple dilated rate convolution layers in parallel and each dilated convolution layer has different dilated rates. The larger the dilated rate is, the larger the receptive field of the convolution kernel is. The context information sensing module performs feature extraction on the feature map **U** through convolution kernels of different receptive fields so that the model can perceive changes in the context information between different scales and different sub-regions. In this way, the loss of feature information is reduced, and the image is understood more comprehensively.  
In the deeper detection layer, a higher level of detection feature map is enhanced using a semantic activation module. In order to detect objects of different sizes, the feature map is downsampled in the fc7 to conv9_2 layer, which reduces the resolution of feature maps and increases the receptive field of the model. However, semantic information and location information are lost in each downsampling. Therefore, the semantic activation module is used on fc7 to conv9_2 to learn the relationship between the channel and the object by self-supervised learning, so as to adjust and enrich the semantic information.
## 2 Membership Function Filter
### Description of the Filtering Method  
The membership function filter, as it was named, is similar to the weight moving average filter, which is counted from \(k\) measured values, but weights are calculated based on the membership function for belonging to a set of \(k\) measured values. This is the most important difference from a weighted moving average filter, because if a set of measured values has changed, weights also change due to their dependence on an average set of values. Mathematically, such a filter can be expressed as follows.  
The sample of the non-filtered signal \(u(t_{i})\) includes deterministic \(u_{d}(t_{i})\) and stochastic \(u_{s}(t_{i})\) elements:  
\[u(t_{i})=u_{d}(t_{i})+u_{s}(t_{i}) \tag{1}\]  
The stochastic part of the signal can be suppressed by the substitution for the counted moving weight average from the last \(k\) samples of the non-filtered signal:  
\[\overline{u}(t_{i})=\frac{\sum\limits_{j=1}^{k}u\left(t_{j}\right)\cdot w_{j}} {\sum\limits_{j=1}^{k}w_{j}} \tag{2}\]  
where \(j=1,\,\ldots,\,k\) and \(k\) is the chosen number of samples, \(u\left(t_{j}\right)\) are last \(k\) signal samples which contain the deterministic and stochastic element, and \(w_{j}\) are counted weights from interval (0-1) for \(k\) last obtained signal samples.  
The principle diagram of such a membership function filter for \(k\) samples of the non-filtered signal is in Figure 1. It consists of block 1 for sampling of the non-filtered signal, block 2 for sampling of the filtered signal, blocks 3 for weight assignment of non-filtered samples according to selected membership function by block 7, and blocks 4 (multiplication), 5 (summation) and 6 (division) for calculation of the filter output according to Equation (2). The input to the filter is the current sample of the non-filtered signal containing deterministic and stochastic parts. For this sample and also for previous \(k-1\) stored samples, weights are calculated according to selected membership function and multiplied by used samples. In the next step, the sum of products and the sum of weights are calculated in order to be divided, and the new filtered sample is obtained from the filter output. Finally, the output of the filter is saved for weight counting in time \(t_{i+1}\). Due to division in the output block of the filter, there is a condition for filter implementation that the sum of weights can be never equal to zero.  
Figure 1: Basic principle of filter operation for \(k\) samples of the non-filtered signal.
## 4 Results
### Petrographical Parameters
#### 4.1.2 Thin Section Study  
Some thin sections of the Belait and Temburong Formations from the L1, L3, L5, L6, and L8 outcrops were prepared and observed under a polarized microscope to identify the different petrographical parameters, such as the texture, facies, diagenesis, visual porosity, estimation, and pore size distribution analysis (Figures 10-17). The identification of the different sedimentology and mineralogy features is based on their optical properties. The clay types were later identified based on the Th/K ratio. The samples have shown various sedimentological and diagenetic features, including tight argillaceous features, moderately sorted sandstone with kaolinite filling textures, quartz overgrowth, illite pore-lining, and chlorite grain coating. The diagenetic features were studied from three thin sections, representing the three main facies. The thin sections were explicitly studied to identify the authigenic clay minerals. L1-01 represents the fine facies of the Belait Formation, L3-01 represents the Temburong tight sandstone facies, and L5-01 shows the coarse sandstone facies of the Belait Formation, depicted in Figures 15-17, respectively. The different morphologies of authigenic clay minerals were identified under a microscope,  
Figure 9: The sedimentary log of the L5 outcrop, including lithology thicknesses, texture, SGR values, and shale volume.  
including kaolinite cavity filling, illite pore lining, and chlorite grain coating. Sample L1-01, which is dominated by kaolinite and has a poor permeability, shows kaolinite pore filling domination under a microscope (Figure 12). Sample L3-01, which is characterized by the domination of mixed-layer clay and has a poor permeability, shows pore-filling kaolinite, quartz cement, quartz overgrowth, and illite pore lining clearly, as shown in Figure 13. Sample L5-01, which represents chlorite domination and has good permeability values, shows a high percentage of chlorite coats and a small amount of kaolinite filling, with clear pore spaces in the thin section (Figure 14).  
Figure 14: Textural and compositional features of L803 Belait.  
Figure 13: Textural and compositional features of L802 Belait.  
Figure 15: Textural and compositional features of L1-01. (Q) Quartz, (P) pore space, and (KF) kaolinite filling.
# Placebo or Nocebo Interventions as Affected by Hypnotic Susceptibility
## 5 Discussion  
Placebo and nocebo effects, produced in a two-step intervention (verbal expectation and conditioning), were studied under hypnotic analgesia in lowly (LH) and highly hypnotizable (HH) subjects. To the best of our knowledge, this is the only study in which placebo and nocebo effects were directly contrasted in LH and HH subjects under hypnotic induction. We examined placebo and nocebo effects under the four following settings: post-conditioning, hypnotic relaxation, hypnotic analgesia, and post-hypnosis. The VAS scores of placebo-produced analgesia differed significantly from nocebo-produced hyperalgesia at all experimental stages in the LH subjects (Figure 1 and Table 2). Placebo intervention combined with hypnotic analgesia in the LH subjects produced a significant elevation of analgesia, but nocebo did not reduce their hypnotic analgesia (Figure 3). Figure 3 demonstrates that the percentage of pain reduction in the LH subjects under placebo was similar to that achieved in the HH subjects under placebo or nocebo, as well as the HH subjects under no placebo/nocebo preconditioning.  
Surprisingly, no difference was detected between the placebo and the nocebo effects on the HH subjects, and the HH subjects seemed unresponsive to any of the placebo or nocebo interventions (Figures 2 and 3, Table 2).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Condition** & **LH** & **HH** & **\(p\)-Value** \\ \hline Hypnosis (at screening) & 4.45 \(\pm\) 0.67 & 6.91 \(\pm\) 0.34 & 0.004 \\ \hline Hypnosis (during session) & & & \\ Placebo & 4.25 \(\pm\) 0.66 & 5.79 \(\pm\) 0.59 & 0.094 \\ Nocebo & 4.70 \(\pm\) 0.65 & 6.20 \(\pm\) 0.55 & 0.084 \\ \hline Cream application & & & \\ Placebo & 7.54 \(\pm\) 0.49 & 5.54 \(\pm\) 0.46 & 0.007 \\ Nocebo & 6.95 \(\pm\) 0.72 & 5.95 \(\pm\) 0.65 & 0.315 \\ \hline Conditioning & & & \\ Placebo & 6.59 \(\pm\) 0.56 & 5.87 \(\pm\) 0.62 & 0.406 \\ Nocebo & 7.17 \(\pm\) 0.82 & 5.00 \(\pm\) 0.55 & 0.043 \\ \hline Cream + conditioning & & & \\ Placebo & 7.08 \(\pm\) 0.38 & 5.71 \(\pm\) 0.38 & 0.045 \\ Nocebo & 7.06 \(\pm\) 0.54 & 5.50 \(\pm\) 0.43 & 0.081 \\ \hline Placebo + nocebo & & & \\ Cream & 7.25 \(\pm\) 0.43 & 5.75 \(\pm\) 0.39 & 0.032 \\ Conditioning & 6.89 \(\pm\) 0.50 & 5.45 \(\pm\) 0.42 & 0.033 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average expectation rates \(\pm\) standard error for analgesia (for hypnotic and placebo conditions) or hyperalgesia (for nocebo condition) from the highly (HH) and lowly hypnotizable (LH) subjects.
## 3 Results and Discussions  
### Variation of Temperature on the Specim Surface  
Figure 2 shows the variation charts of the temperature measured on the current layer surface (layers 3 to 15) for the three printing scenarios in case of the black PLA. The following observations can be made:  
1. In case of the specimen printed individually (Figure 2a), the temperature of the deposited top layer continuously decreases, correlated with its distance to the build plate. The total variation from layers 3 to 15 is approx. 8 \({}^{\circ}\)C.
2. During the printing process, the temperature on the top layers has a cyclic variation, with average values located near the glass transition temperature of the PLA, indicated by the filament manufacturer to be equal 58 \({}^{\circ}\)C for all filament colors [24]. The amplitude of the temperature cycles increases with the number of specimens printed simultaneously, reaching maximum values, as follows: up to 1 \({}^{\circ}\)C for single printing (Figure 2a), up to 6 \({}^{\circ}\)C for simultaneous printing of three specimens (Figure 2b) and respectively up to 11 \({}^{\circ}\)C for simultaneous printing of five specimens (Figure 2c).
3. In case of simultaneous printing, the amplitude of the cycles increases slightly for the higher layers, which may be explained by the diminished influence of the build plate temperature.
4. The amplitude of the variation cycles is lower for the specimens positioned on the right side of the build plate, both in case of three simultaneously printed specimens (up to 3 \({}^{\circ}\)C for specimen 3.3, Figure 2b) and in case of five simultaneously printed specimens (up to 3.5 \({}^{\circ}\)C for specimens 5.4 and 5.5, Figure 2c), showing that the heat field is not evenly distributed, which may be caused by the air flow in the 3D printer workspace.
5. The differences between the temperatures on the top layers of the specimens during simultaneous printing are reaching up to 4 \({}^{\circ}\)C for three specimens and respectively up to 8 \({}^{\circ}\)C for five specimens printed simultaneous.  
It should be mentioned that the results are in accordance with previous research [13], where the deposition of successive layers was found to determine \"reheating peaks\" of the material in very similar temperature intervals, even though the authors printed only one line layers. As shown in [28], these temperature cycles, occurring near the glass transition temperature of the PLA, may cause variations of the mechanical properties and dimensional accuracy of the specimens.  
The analysis of the temperature variations should be correlated with the moments of measurements, defined by the position of the print head in relation to the position of the thermometer. As stated above, it was not possible to measure the temperature in the first seconds after deposition of the material.  
Figure 2: The variation of the temperature on the top surface of layers 3–15, black PLA: (**a**) individually printed specimens, (**b**) three specimens printed simultaneous, (**c**) five specimens printed simultaneous (after [25]).