esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data
## 2 Materials  
Rodent MRI data were obtained from the Biomedical Imaging and Instrumentation Group (Biig) of the Gregorio Maranon Hospital. The dataset consisted of 53 rat brain MRI images divided into two groups: healthy rats (N = 24) and pathological rats (N = 29). Pathology was induced by the administration of the viral mimic polyriboinosinic-polyribocytidilic acid (poly I:C) in gestational day 15 to pregnant Wistar rats, since maternal immune stimulation (MIS) is associated with increased risk of onset of schizophrenia in the offspring, with behavioral abnormalities as well as neurophysiological and morphological traits. Model details can be found elsewhere [25; 26; 27].  
All images were preprocessed following the standard preprocessing pipeline in neuroimaging research, using the processing toolbox of the Statistical Parametric mapping software (SPM12) [28], as shown in Figure 1. Output consisted of: White Matter (WM), Gray Matter (GM), and CerebroSpinal Fluid (CSF) regions, with 464,487, 582,467, and 30,702 voxels, respectively.  
Figure 1: MRI pipeline for data processing [28]. First, images were corrected for field homogeneity, resized by a factor of 10 and spatially normalized to create a custom template based on a Wistar rat brain template [29]. All images were resliced to this custom template and were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Later, all images were modulated using the Jacobian determinants and smoothed with a 10-mm FWHM Gaussian kernel. Finally, the segmented tissues were processed by the ML model to classify them into healthy and pathological subjects and to identify the brain areas relevant to this decision.
## 2 Related Work
### Data Augmentation  
In object detection, some methods are often used to enhance the data. Commonly used methods include image flipping, random scaling and mirroring, deformation, rotation, distortion, amplification, adding noise, and other means to increase the number of samples. Increasing the number of small samples by means of data augmentation helps improve the detection performance.  
To improve the practical application effect while analyzing and processing remote sensing images, many researchers have focused on the shortcomings of current deep learning-based object detection algorithms. Based on the characteristics of remote sensing technology, some structural refinements were made to the algorithm. In SAR target detection, [PERSON] et al. [8] used transfer learning and data augmentation due to insufficient labeled images for training. A new training image was generated by artificially extracting subimages and adding noise, filtering, flipping and other image processing methods to the original training image.  
To improve the contrast of ship targets to clutter, [PERSON] et al. [35] proposed an improved superresolution generative adversarial network (ISRGAN)-based blur suppression algorithm for SAR ship target contrast enhancement. The above data enhancement was mainly processed for the entire image. The amount of training data increased, but the ratio of positive to negative samples did not change.  
[PERSON] et al. [36] proposed a data augmentation strategy for small objects. Images containing small objects were oversampled, and small objects were enhanced to encourage the model to pay more attention to small objects. This enhancement was mainly aimed at visible light images, and there were many types of small targets. In the enhancement, only the number of small targets was increased, and the characteristics of the targets were not analyzed. The likelihood of a target appearing was enhanced.  
In future research, data enhancement will be carried out according to the characteristics of ship targets in SAR images, and the balance of positive and negative samples will be optimized by improving the enhancement method.
## 2 Microstructure of Laminated Shale Specim and Micro-Constitutive Models
### Micro-Constitutive Model for Bedding  
Bedding in shale formation is a special element. This study adopts the smooth joint model (SJM) to simulate the contacts of beddings [38]. The SJM can only deliver contact force. The tensile failure between adjacent discs in the SJM is similar to that in the PBM, but the SJM treats the shear slip between discs in a different way, as shown in Figure 3. The most distinguished feature of the SJM is that the discs along the beddings can overlap and slide across each other, while the discs can only move around one another in the PBM. Further, the dilatancy effect is considered in the shear force of the SJM if shear failure occurs. Therefore, the SJM is suitable for simulating the shear slip of beddings in rocks. The force and stress after failure in the SJM are calculated using the following fundamental algorithm:  
\[\overline{F}_{n}^{j}=\frac{\Delta\overline{F}_{s}^{j}}{\overline{k}_{s}^{ej}} \cdot\overline{k}_{n}^{ej}\cdot\tan\varphi \tag{6}\]  
\[\overline{F}_{s}^{ej}=-\mu f\overline{F}_{n}^{ej} \tag{7}\]  
\[\overline{\sigma}=\frac{\overline{F}_{n}^{sj}}{A} \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Micro-Parameter** & **Value** \\ \hline Particle density \(\rho\) (kg/m\({}^{3}\)) & 2330.0 \\ Young’s modulus of particle \(Ec\) (GPa) & 4.0 \\ Young’s modulus of matrix \(\overline{Ec}\) (GPa) & 4.0 \\ Moment contribution factor \(\beta\) & 0.2 \\ Ratio of normal to shear stiffness of particle \(k_{n}/k_{s}\) & 1.8 \\ Ratio of normal to shear stiffness of matrix \(\overline{k}_{n}^{pb}/\overline{P}_{s}^{pb}\) & 1.8 \\ Particle friction coefficient \(\mu_{f}^{\prime}\) & 0.7 \\ Tensile strength \(\overline{\sigma}_{c}^{pb}\) (MPa) & 26.0 \\ Shear strength \(\overline{\tau}_{c}^{pb}\) (MPa) & 32.5 \\ \hline \end{tabular}
\end{table}
Table 1: Micro-constitutive parameters calibrated for rock matrix.  
In the contact model of the SJM, the normal force \(\overline{F}_{n}^{ej}\) and shear force \(\overline{F}_{s}^{ej}\) are oriented to the normal and tangential directions of beddings rather than the original orientation of the contact. \(\overline{k}_{n}^{ej}\) and \(\overline{k}_{s}^{ej}\) are the normal and shear stiffness per unit contact area. \(\varphi\) is the dilation angle of beddings.  
Micro-tensile crack and micro-shear crack may occur in beddings. A micro-tensile crack of beddings (Sjt) occurs if the tensile stress \(\overline{\sigma}\) exceeds the tensile strength of beddings \(\overline{\sigma}_{c}^{ej}\). A micro-shear crack of beddings (Sjs) occurs if the shear stress \(\overline{\tau}\) exceeds the shear strength of beddings \(\overline{\tau}_{c}^{ej}\). Therefore, shale formation may have four failure modes.
# Meta-Learning with Graph Community Detection for Cold-Start User Clustering
## 4 Experimental Set-Up and Assessment Indicators
### Experimental Process  
We validated the methods in this paper in a variety of environments, including score prediction in the traditional, user cold-start scenarios, content cold-start scenarios, and user and content cold-start scenarios. This paper compared whether or not to front-load the use of graph community detection algorithms. This section presents the results of experiments conducted on two real-world datasets from publicly accessible repositories, Last.fm and MovieLens. For each dataset, this paper divided the data into two groups, existing users and content and new users and content, based on the time when users joined (or the time of the first user action) and the time of the content release, in order to evaluate the performance of the model's multiple metrics in both the cold-start condition and the traditional scenario. We then split each dataset into a training set and a test set. The training set contained a small quantity of user rating data, while 15% of the data were randomly sampled in this paper as the validation set for adjusting the structure of the model as well as the corresponding hyper-parameters, and the rest was divided into four recommendation scenarios according to the users and content: traditional scenario, user cold-start scenario, content cold-start scenario, and user and content cold-start scenarios for evaluating the final performance of the model. In the comparative experimental design, this paper focused on the influence of graph group detection (using the classic Louvain algorithm) as a meta-learning pre-module on user clustering. The meta-learning algorithm used was the Melu [33] algorithm. We used the Melu algorithm with or without graph group detection and compared the results of the two experiments.
## 6 Conclusions  
In this study, we evaluated the accuracy of the ROD extraction from Sentinel-1 data under different vegetation densities, analyzed the interannual variation in RODs in the study area, and explored the controlling effect of SWE on the ROD. Under different land cover types, the accuracy of the ROD extracted from Sentinel-1 data decreases with increasing vegetation density. The MAE for tree cover, shrubland, grassland, and bare land areas are 24, 18, 18, and 16 d, respectively. The ROD extracted from SWE data shows an advancing trend from 2017 to 2023, and in the areas with higher vegetation density, the advancing trend is more obvious. However, the ROD extracted from Sentinel-1 data, except in bare land areas, exhibits a delaying trend. The reasons for this difference include the influence of vegetation and an extreme snowfall event that occurred in the study area in 2023. The  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Satellite** & **Pass** & **HH-MM (UTC)** \\ \hline \multirow{2}{*}{Sentinel-1A} & Ascending & 01:59 \\ \cline{2-3}  & Descending & 14:07 \\ \hline \multirow{2}{*}{Sentinel-1B} & Ascending & 01:59 \\ \cline{2-3}  & Descending & 14:06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Orbit and time of transit of Sentinel-1 data used in the study.  
extreme snowfall event is also a major factor contributing to the non-significant trends (\(p>0.05\)) of either an advancement or delay in the ROD extracted by the two methods. Moreover, we found that the SWE and ROD are positively correlated, and the correlation is significant (\(p<0.05\)). When the SWE increases, the ROD shows a significant delay, with a greater delay rate observed in vegetated areas. These findings provide a reference for error analysis in extracting the ROD from SAR data and offer insights for improving subsequent ROD extraction models. In the future, we will try to use L-band SAR data to reduce the influence of vegetation on ROD extraction, and analyze the inter-annual changes in the ROD in other regions of the world.  
Conceptualization, B.G. and W.M.; methodology, W.M.; software, B.G.; validation, W.M.; formal analysis, B.G.; investigation, B.G.; data curation, B.G.; writing--original draft preparation, B.G.; writing--review and editing, W.M.; visualization, B.G.; supervision, W.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original data presented in the study are openly available. The URLs are referred to in the main text.  
The authors would like to thank Google and the National Water and Climate Center (NWCC) for free access to the Google Earth Engine and the SWE data.  
The authors declare no conflicts of interest.
## 3 Results
### ML Model Comparison  
The classification accuracy of pain severity was calculated using body temperature image data obtained using the ML analysis method, as follows: BA, F1 score, sensitivity, particularity, positive predictive value, and negative predictive value, sequentially. The respective obtained values were as follows: LR: 0.636, 0.682, 0.748, 0.524, 0.627, and 0.661; DT: 0.599, 0.620, 0.632, 0.566, 0.609, and 0.590; RF: 0.664, 0.699, 0.748, 0.579, 0.655, and 0.683; and SVM: 0.650, 0.616, 0.548, 0.752, 0.702, and 0.609 (Figure 1 and Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mild-to-Moderate Pain**} & \multicolumn{2}{c}{**Severe-to-Intractable Pain**} \\ \hline
**Age Group** & **\textless{}6 Months** & **\textgreater{}6 Months** & **\textless{}6 Months** & **\textgreater{}6 Months** \\ \hline
0–19 & 1 & 1 & 1 & 2 \\
20–29 & 10 & 17 & 20 & 13 \\
30–39 & 26 & 43 & 30 & 46 \\
40–49 & 20 & 36 & 43 & 65 \\
50–59 & 45 & 74 & 40 & 61 \\
60–69 & 30 & 76 & 60 & 70 \\
70 & 40 & 49 & 35 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of patients according to severity of pain, age, and duration of symptoms.  
Figure 1: Confusion matrix of each machine-learning model in the test set. LR, logistic regression; DT, decision tree; RF, random forest; SVM, support vector machine.  
Figure 2 presents the receiver operating characteristic (ROC) and precision-recall curves of each ML model. Each ML model had area-under-the-ROC-curve (AUC) values of: LR, 0.67; DT, 0.58; RF, 0.70; and SVM, 0.62. The respective AUC values for the precision-recall curves were: LR, 0.68; DT, 0.70; RF, 0.71; and SVM, 0.74.
## 3 Results
### RQ4: Preferred XAI Method  
When asked which type of explanation they would prefer in a CDSS, almost half (12, 48%) of participants preferred to see both explanations, as shown in Figure 7. For participants who preferred one over the other, there was a clear preference for explanation by feature contribution (10, 40%) over explanation by example (1, 4%). Only two (8%) participants preferred none of the explanations in a CDSS.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **No. of** & \multicolumn{4}{c}{**Feature Contribution**} & \multicolumn{4}{c}{**Example**} \\  & **Participants** & **WOA Median** & **V** & \(p\)**-Value** & **WOA Median** & **V** & \(p\)**-Value** \\  & & **Correct** & **Incorrect** & & & **Correct** & **Incorrect** & \\ \hline \hline \multicolumn{1}{c}{_Type of HCP 1_} & & & & & & & & \\ Obstetricians & 19 & 0.6 & 0.5 & 94.5 & 0.711 & 0.333 & 0.5 & 32 & 0.066 \\ Others (midwives, dietititians) & 6 & 0.45 & 0.333 & 12 & 0.281 & 0.236 & 0.271 & 12 & 0.844 \\ \multicolumn{1}{c}{_Years of experience_} & & & & & & & & \\ \multicolumn{1}{c}{\textgreater{}10 years} & 12 & 0.4 & 0.317 & 44 & 0.351 & 0.25 & 0.392 & 25 & 0.301 \\ \(\leqslant\)10 years & 13 & 0.5 & 0.5 & 39 & 1 & 0.333 & 0.429 & 22 & 0.610 \\ \hline \hline \end{tabular}  
* HCP: healthcare practitioner.  
\end{table}
Table 2: Comparison of WOA between correctly and incorrectly predicted cases for two XAI methods in subgroups of participants. Median is used as cutoff for continuous features (years of experience).  
Figure 6: WOA for explanation by feature contribution and explanation by example for correctly and incorrectly predicted cases.  
Among all participating obstetricians, more than half (10, 52.6%) preferred explanation by feature contribution. The rest preferred both explanations (7, 36.8%) or none of the explanations (2, 10.5%). Among the two participating midwives, one preferred explanation by example and one preferred both explanations. All participating dietitians preferred both explanations.  
One participant justified the preference for explanation by feature contribution as one similar example only was not helpful, and suggested the use of a large group of similar examples:  
\"I don't think comparing to one similar patient is helpful, this does not predict the risk to our patient. If comparing to other patients it should be a comparison to a large group of similar patients, not just one case. \" (Obstetrician)  
One participant commented that they preferred to see both explanations in a CDSS for better clarity:  
\"visually clearer\" (Dietitian)
## 3 Results and Discussion
### Solubility in Water of SDexF  
The starch devtrinization process leads to an increase in the solubility in water of the obtained product, because of disrupting the native granular structure of starch, breaking down the crystalline structure and exposing more sites for water interaction [51]. In general, resistant dextrins have good solubility in water [52; 53], which is a significant factor contributing to their functionality as dietary fibres [54]. The solubility in water at 20 \({}^{\circ}\)C of four SDexF batches is presented in Table 2. The average solubility in water of SDexF was 97.89 \(\pm\) 2.12%, where repeatable results were obtained for individual batches. Compared to native potato starch, an almost 100% increase in water solubility was observed for SDexF prepared on a semi-industrial scale. According to [PERSON] et al. [22], the solubility of SDexF obtained on a laboratory scale under the same conditions as SDexF on a semi-industrial scale in this study was lower, which indicates that application of a semi-industrial scale obtains a product that is completely soluble in water. The solubility in water of SDexF was similar to the solubility of pyrodextrins obtained from maize starch [55]. The authors achieved a solubility of over 90%, although using a much higher devtrinization temperature (180 \({}^{\circ}\)C) compared to this study. The solubility of SDexF in water increased due to the shortening of the chain lengths (starch depolymerization) because of dextrinization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **IDF [\%]** & **SDFP [\%]** & **SDFS [\%]** & **TDF [\%]** \\ \hline SDexF1 & 0.75 \(\pm\) 0.03 b  & 10.42 \(\pm\) 0.06 b  & 20.84 \(\pm\) 1.08 a  & 32.01 \(\pm\) 1.17 ab  \\ SDexF2 & 0.67 \(\pm\) 0.02 a  & 7.44 \(\pm\) 0.16 a  & 22.60 \(\pm\) 0.95 ab  & 30.07 \(\pm\) 0.78 a  \\ SDexF3 & 0.71 \(\pm\) 0.01 b  & 8.00 \(\pm\) 1.22 a  & 25.76 \(\pm\) 0.24 c  & 34.47 \(\pm\) 1.46 b  \\ SDexF4 & 0.77 \(\pm\) 0.05 b  & 6.48 \(\pm\) 1.30 a  & 23.54 \(\pm\) 0.02 b  & 30.78 \(\pm\) 1.27 a  \\ \hline \hline \end{tabular}
\end{table}
Table 1: TDF content of SDexF1-SDexF4 batches.
# Shaft Wall Damage to High-Depth Inclined Ore Passes under Impact Wear Behavior
## 2 Shaft Wall Damage Model for High-Depth Inclined Ore Passes
### Influence of Inclined Angle of the Clute \(\alpha\)  
To study the influence of the inclined angle of the chute on the shaft wall damage volume, the ore-pass dip angle \(\theta\), the shaft diameter \(D\), and the ore block size \(P\) were fixed at 70\({}^{\circ}\), 2 m, and 400 mm, respectively, and the inclined angle of the chute \(\alpha\) was set between 40\({}^{\circ}\) and 90\({}^{\circ}\). The chutes of the ore passes were divided into three groups according to their ore-pass depth (60, 120, and 180 m). Figure 11 presents the variation in total shaft wall damage volume determined from Equations (18) and (19) with respect to the inclined angle of the chute \(\alpha\) across ore-pass depths.  
Figure 10: Variation in total shaft wall damage volume \(Q_{\text{tol}}\) with respect to ore-pass dip angle \(\theta\) across ore-pass depths.  
It can be seen from Figure 11 that the ore-pass depth \(H\) plays an important role in the \(Q_{\text{tol}}\)-\(\alpha\) relation curve shape. As \(H\) increases, the collision frequency between the ore and the shaft wall increases, leading to a corresponding increase in \(Q_{\text{tol}}\). It can also be observed from Figure 11 that \(Q_{\text{tol}}\) increases first and then decreases with \(\alpha\). When \(\alpha<70^{\circ}\), \(Q_{\text{tol}}\) increases with \(\alpha\), and the increasing rate of the \(Q_{\text{tol}}\)-\(\alpha\) curve decreases. When \(\alpha>70^{\circ}\), \(Q_{\text{tol}}\) decreases with \(\alpha\), and the growth rate of this curve increases gradually. The maximum value of \(Q_{\text{tol}}\) appears when \(\alpha=70^{\circ}\).  
The reason is that \(\alpha\) determines the velocity direction of the ore hitting the shaft wall. Based on Equations (21) and (22), it is discernible that with an increase in \(\alpha\), the normal velocity of the ore impacting the shaft wall decreases, while the tangential velocity increases. Moreover, as derived from Equations (12) and (16), variations in the normal and tangential velocities lead to a decrease in the ore indentation depth \(\delta\) and an increase in the wear length \(l\). Hence, similar to the influence of \(\theta\) on \(Q_{\text{tol}}\), \(\alpha\) influences \(Q_{\text{tol}}\) by affecting \(\delta\) and \(l\), resulting in an initial increase followed by a subsequent decrease in the \(Q_{\text{tol}}\)-\(\alpha\) curve trend.  
Therefore, the aim of reducing damage to the shaft wall can be achieved, on the one hand, by minimizing the inclined angle of the chute to be significantly below \(70^{\circ}\) and, on the other hand, by employing an inclined angle of the chute close to \(90^{\circ}\).
# Enhancing the SRAM PUF with an XOR Gate
## 4 Results
### Enrollment
#### 4.1.2 Read Count  
During the enrollment process, multiple measurements are taken to determine the stability of each cell. Stable cells are consistently represented by either '0' or '1', indicating that they contain either exclusively '0's or '1's over the 1000 read collection.  
A crucial factor in BER and enrollment is the number of reads conducted. A read refers to a single measurement of each SRAM cell. Increasing the number of reads during enrollment lowers the chance of selecting a flaky cell in a final key, lowering the key's BER. However, each additional read gives diminishing returns and does not provide significant benefits concerning the time required for enrollment.  
By increasing the number of reads, the likelihood of identifying a cell as flaky increases, resulting in a reduction in the number of stable cells available. To assess the impact of the read count, we conducted enrollments with progressively higher read counts. These enrollments were then used to generate a key, and the corresponding BER was measured. The results of this analysis are presented in Figure 8.  
Figure 7: Flakiness vs. enrollment delay time over different temperatures. The two flaky chips shown have no major differences in temperature or enrollment time. The SRAM chips used in this study use a similar enrollment cycle time of 1, 3, or 21 s since there is very little variation from chip to chip.  
At a read count of 1000, the BER decreased to less than 1%. Given that error-correcting techniques such as error-correcting codes [32] and Response-Based Cryptography (RBC) [33] are capable of correcting such a low percentage of errors, a read count of 1000 was chosen for this research.