esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The deformation and failure of underground engineering are usually caused by unloading. In this work, triaxial unloading confining pressure tests are carried out to simulate the failure process of rock mass caused by unloading, analyze the crack-characteristic stress, and study the energy evolution of rock under unloading and the pre-peak and post-peak energy characteristics combined with the energy theory. The results show that, when the confining pressure increases from 5 MPa to 20 MPa, crack closure stress \(\sigma_{\text{cc}}\), crack initiation stress \(\sigma_{\text{cl}}\), dilatancy stress \(\sigma_{\text{cl}}\), and peak stress \(\sigma_{p}\) are 6.34 times, 2.75 times, 1.93 times, and 1.66 times higher than the original, respectively. By comparing the increase in crack-characteristic stress, it can be found that the confining pressure has a large effect on the crack closure stress and crack initiation stress, while the dilatation stress and peak stress have relatively little influence. From the perspective of energy evolution, the pre-peak axial absorption energy \(U_{1}\) increases exponentially, the elastic energy \(U^{\epsilon}\) is similar to \(U_{1}\), and the circumferential consumption energy \(U_{3}\) and dissipation energy \(U^{\epsilon}\) are small. After reaching the peak stress, the growth rate of \(U_{1}\) decreases slightly, \(U^{\epsilon}\) decreases rapidly, and \(U_{3}\) increases rapidly but only as a small fraction of the total energy, while \(U^{\epsilon}\) grows almost exponentially and rapidly becomes the main part of the energy. Under each crack-characteristic stress state, the energy characteristic parameters gradually increase with the increase in confining pressure, which is manifested by the increase in slope in the linear fitting formula of energy characteristic parameters. The release process of the releasable elastic energy after the peak stress can be divided into three stages of \"slow-fast-slow\", and the energy release process shows an obvious confining pressure effect.  
Keywords:triaxial unloading confining pressure test; mechanical properties; crack-characteristic stress; energy evolution; energy release +
Footnote †: journal: Applied
# Biological Production of 3-Hydroxpropionic Acid: An Update on the Current Status
## 3 Production of 3 HP from Sugars
### Pathways for the Conversion of Sugars (Mainly Glucose) to 3 HP
#### 3.1.2 The \(\beta\)-Alanine Pathway  
The main precursor of this pathway is aspartate, which is produced from fumarate (an intermediate of the citrate cycle). For this reason, it is advantageous to use a strain capable of overproduction of fumarate and of expressing a highly active aspartate, the enzyme responsible for the transformation of fumarate to aspartate. The next step is the transformation of aspartate to \(\beta\)-alanine by the action of aspartate-\(\alpha\)-decarboxylase, followed by cleavage of the amino group of \(\beta\)-alanine by \(\beta\)-alanine-pyruvate aminotransferase or \(\gamma\)-aminobutyrate transaminase, resulting in malonic semialdehyde formation [48]. The last step is the conversion of malonic semialdehyde to 3 HP by the action of malonic semialdehyde reductase, 3-hydroxypropionate dehydrogenase, or 3-hydroxyisobutyrate dehydrogenase [49] (Figure 4). Theoretically, the yields achieved through the \(\beta\)-alanine intermediate pathway should be higher than the yields obtained through the malonic intermediate pathway, as the malonic pathway is highly oxygen-dependent and requires high levels of ATP for acetyl-CoA synthesis [49].  
Figure 4: Conversion of glucose to 3 HP through the \(\beta\)-alanine pathway.  
Figure 3: Conversion of glucose to 3 HP through the malonyl-CoA pathway.
# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 3 Modeling, Methods, and Design
### Overview of Network Structure  
Swin-PANet consists of a prior attention network and a hybrid Transformer network. The illustration of the proposed Swin-PANet can be shown in Figure 1. Prior attention network assisted by Swin Transformer performs intermediate supervision learning. Hybrid Transformer network with enhanced attention blocks performs direct supervision learning. The dual supervision strategy can enhance the performance and interpretability of the Transformer attention mechanism, and provide a humanly interpretable way to guide the attentional learning in Transformer. In the prior attention network, Swin Transformer block and attention guiding decoder are cascaded for receiving multi-scale features to shifted-window attention learning and feature fusion. Attention prediction from the out of prior attention network will be involved in enhanced attention block to guide the subsequent direct supervision learning. The hybrid Transformer network is basically modified based on the U-Net [9] structure, which adopts a U-shaped topology with skip-connections between encoder and decoder, and enhanced attention block to guiding the information filtration and cross attention of the Transformer attention features along channel-wise. Enhanced attention block at each decoder layer will be involved with the multi-scale features of the previous block, attention prediction generated by prior attention network and the features from corresponding skip-connections, which can ensure the consistency between encoder and decoder and recover the discarded information caused by convolution operations. Enhanced attention block can also utilize a feature fusion between global and local contexts along channel-wise for achieving better performance of attention learning. The coarse process of Swin Transformer and attention guiding decoder, and the finer process of the enhanced attention blocks are integrated into the proposed coarse-to-fine strategy.  
Figure 1: The illustration of the proposed Swin-PANet in the task of skin lesion segmentation. Swin-PANet follows the coarse-to-fine strategy and dual supervision strategy. It consists of two components: a prior attention network assisted by Swin Transformer performs intermediate supervision learning, and a hybrid Transformer network with enhanced attention blocks performs direct learning.
# Robust Sampling Frequency Offset Estimation for OFDM over Frequency Selective Fading Channels
## 3 Conventional Estimation Scheme
### Conventional Scheme \(D\)  
In [22], the \(g\)-lag correlation is adopted to estimate frequency errors \(\phi(k)=\Delta_{f}+k\Delta_{\rm s}\). A SFO estimate is get upon observation of \(N_{\rm s}\) OFDM blocks with indices \(l=1,\,2,\,\cdots,\,N_{\rm s}\). Based on the \(N_{\rm s}\) observed OFDM blocks, the \(g\)-lag correlation is defined as  
\[\Omega_{g}(k)=\sum_{l=g+1}^{N_{\rm s}}R_{l}(k)R_{l-g}^{*}(k),\,\,\,1\leq g\leq N _{\rm s} \tag{16}\]  
By using the \(g\)-lag correlations, an estimate of \(\phi(k)\) can be obtained as  
\[\hat{\phi}(k)=\frac{N}{2\pi N_{u}}\sum_{g=1}^{N_{\rm s}/2}p(g)\arg\left\{ \Omega_{g}(k)\Omega_{g-1}^{*}(k)\right\} \tag{17}\]  
where \(\{p(g)\}\) denotes the scaling coefficients [22]. Finally, the quantity \(\hat{\phi}(k)\) is now used to get estimate of \(\Delta_{\rm s}\) as follows  
\[\hat{\Delta}_{\rm s}=\frac{J(1)Q(2)-J(2)Q(1)}{J(1)J(3)-J^{2}(2)} \tag{18}\]  
where  
\[J(n)=\frac{1}{N_{p}}\sum_{m=1}^{N_{p}/2}\left\{(k_{m})^{n-1}|H_{l}(k_{m})|^{2 }+(-k_{m})^{n-1}|H_{l}(-k_{m})|^{2}\right\} \tag{19}\]  
and  
\[Q(n)=\frac{1}{N_{p}}\sum_{m=1}^{N_{p}/2}\left\{(k_{m})^{n-1}|H_{l}(k_{m})|^{2 }\hat{\phi}(k_{m})+(-k_{m})^{n-1}|H_{l}(-k_{m})|^{2}\hat{\phi}(-k_{m})\right\} \tag{20}\]  
These quantities (19) and (20) rely on the channel coefficients \(|H_{l}(k)|^{2}\). Since the GRC pattern is periodical every \(D_{t}+1\) OFDM symbol in time direction, (18) can not be directly applied to the DRM system. In the sequel, we assume that (18) uses the pilot symbols inserted into every OFDM symbol.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 2 Background
### Related Work
#### 2.1.2 Trust-Based DSR Protocol Approaches  
In [8], a trust scheme is presented to extend DSR routing with detection and isolation of misbehaving nodes. When non-cooperative behavior is detected, the misbehaving node is excluded from routing.  
Authors in [17] evaluate trust based on the similarity of nodes. The similarity is a weighted sum of different node attributes, such as velocity or moving direction. The decision to forward the data is based on the similarity degree.  
In [9], the trust mechanism is integrated into the route discovery process. Trust is a cumulative sum of the normalized values for different categories of behavior. In the DSR protocol, nodes add their IP addresses in the RREQ message. During the propagation of the RREP message, each node adds the trust of the preceding node.  
SCOTRES system [10] was created for wireless ad hoc networks deployed for monitoring environmental parameters. The routing protocol uses three metrics to evaluate the node trust. Based on the information from the routing table, a topological metric of a node is evaluated, and the rating system will tolerate failures of significant nodes. Energy and channel-health are the other two metrics. Paths that contain malicious nodes are excluded. The method was integrated into the DSR protocol.
## 3 The Implementation of the Novel ARPD-RTK/INS Algorithm
### Grid Creation  
After obtaining the INS derived position and the error covariance matrix, we must establish the spatial search region centered on the current position. A simplified approach is to draw a set of fixed-distance grid points, and each point corresponds to a set of integer ambiguities. Considering the integer property of ambiguities, the weight of a grid point is negatively correlated with the residual between its float ambiguity solution and the nearest integer vector. For wide-lane observations, the appropriate distance between the two nearest grid points is determined by the satellite elevation distribution, which has been discussed in detail in earlier work [18]. Thus, we still choose 0.2 m as the constant distance.  
Since the error distribution of the vehicle position in the ECEF frame is obtained by the previous step, and the ground vehicle moves relatively slowly in the height direction, using such a priori information can make the grid point allocation more efficient. The error distribution needs to be converted from the ECEF frame to the ENU frame. In order to avoid the influence of position error covariance, we refer to the idea of \"decorrelation\" in the ambiguity domain when determining the distribution directions of the grid points. In the horizontal plane, grid points are created along the major and minor axes of the error ellipse. A typical example for the grid creation is depicted in Figure 5.
## Materials and Methods
### Antioxidant Capacities  
Four complementary assays were used for determination of antioxidant capacities:  
2, 2-Azino-bis (3-ethyl-benzothazoline-6-sulfonic acid) diammonium salt (ABTS\({}^{+}\)) was generated by the interaction of ABTS (7 mM) and K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) (2.45 mM). The mixture was kept in the dark at room temperature for 12-16 h before use. This solution was diluted until the absorbance reached 0.7 at 734 nm and equilibrated at 30 \({}^{\circ}\)C. After addition of 1.0 mL of diluted ABTS\({}^{+}\) solution to 10 \(\mu\)L of extract or Trolox standards, the absorbance reading was taken 1 min after initial mixing and up to 6 min. The percentage decrease of the absorbance was calculated and plotted as a function of the concentration of the extracts and Trolox for the standard reference data [33].  
Ferric-reducing/antioxidant power (FRAP) assay based on FRAP reagent (2.5 mL of a 10 mM ferric-tripridyltriazine solution in 40 mmol HCl plus 2.5 mL of 20 mmol FeCl\({}_{3}\)-H\({}_{2}\)O and 25 mL of 0.3 M/L acetate buffer, pH 3.6) of 900 \(\mu\)L was mixed with 90 \(\mu\)L of distilled water and 30 \(\mu\)L of kiwifruitand persimmon extract samples as the appropriate reagent blank. The absorbance was measured at 595 nm after 30 min [34].  
Cupric reducing antioxidant capacity (CUPRAC) is utilizing the copper (II)-neocuproine [Cu (II)-Nc] reagent as the chromogenic oxidizing agent. To the mixture of 1 mL of [Cu (II)-Nc] and NH\({}_{4}\)Ac buffer solution, acidified and non acidified extracts of fruits (or standard) solution (x, in mL) and H\({}_{2}\)O ((1.1-x) mL) were added to make the final volume of 4.1 mL. The absorbance at 450 nm was recorded against a reagent blank [35].  
1-Diphenyl-2-picrylhydrazyl method (DPPH) based on DPPH solution (3.9 mL, 25 mg/L) which was mixed with the samples extracts (0.1 mL). The reaction progress was monitored at 515 nm until the absorbance was stable [36].  
The units for all antioxidant capacities were \(\upmu\)M TE (Trolox equivalent) per g DW. The absorbances of all investigated resulted mixtures were measured on Hewlett-Packard, model 8452A spectrophotometer.
## 3 Results and Discussion
### Pore Evolution
#### 3.4.2 Pore Size Distribution Changes  
The proportions of different types of internal pores in granite under various thermal shock conditions are shown in Figure 15. The distribution of different pore sizes in granite under high-temperature cold-shock conditions is observed to vary with heat treatment conditions. In particular, the pore structure of coarse granite is dominated by medium pores and large pores, whereas that of fine-grained granite is dominated by small pores and medium pores. It is evident that within the temperature range of 25 \({}^{\circ}\)C to 400 \({}^{\circ}\)C, the pore structure of both coarse- and fine-grained granite exhibits negligible changes in response to water-cooling shock. However, a pronounced alteration in pore structure is observed under liquid-nitrogen-cooling shock conditions. Specifically, the proportion of large pores in coarse granite increases from 15.64% to 38.02%, while the proportion of medium pores in fine granite rises from 12.88% to 28.4%. This observation indicates that liquid-nitrogen-cooling shock significantly increases the proportion of medium pores and large pores within the granite [44], which macroscopically weakens the shear mechanical properties of the granite. This finding is consistent with the results reported in Section 3.2.2. Within the range of 400 \({}^{\circ}\)C to 600 \({}^{\circ}\)C, the proportion of large pores and medium pores in both coarse- and fine-grained granite increases dramatically under both cooling methods, indicating that high temperature severely damages the internal pore structure of granite. It is noteworthy that under high-temperature cold shock, the proportion of large pores in coarse-grained granite increases with heat treatment temperature, while the proportion of medium pores in fine-grained granite increases with heat treatment temperature. This finding indicates that the deterioration of shear mechanical properties in coarse-grained granite is predominantly attributable to alterations in large pores induced by high-temperature cold shock, while the degradation observed in fine-grained granite is attributed to changes in medium pores.
# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 3 Methodology
### Model Evaluation  
The study evaluated the performance of the classification model using accuracy, RMSE, precision, recall, and area under the ROC curve based on the confusion matrix. A low value for RMSE indicates that the predicted model can be considered, whereas a higher accuracy value indicates outstanding performance [55]. Formulas for calculating the evaluation metrics are shown in Equations (2)-(6):  
The accuracy evaluation metrics in Equation (1) are calculated using the following: true positive (_TP_), true negative (_TN_), false negative (_FN_), and false positive (_FP_). The metrics correspond to the instances that are correctly classified [56].  
\[Accuracy=\frac{TP+TN}{TP+TN+FN+FP} \tag{2}\]  
Precision and recall represent the ratio of positive instances (_TP_) that are correct in the RTA dataset. High values of precision and recall indicate that the returned results are significant. The computation formulas are captured in Equations (3) and (4):  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
Model validation was carried out using the area under the ROC curve, as defined by Equation (5). The ROC curve can assist in determining the best threshold values produced by plotting sensitivity (_TP_--true positive rate) against the specificity (_FPR_--false positive rate), indicating the proportion of RTAs. The study computed the AUC, the purpose of which is to deal with problems that contain a skewed data distribution to avoid over-fitting to a single class. An outstanding model will achieve an AUC near 1, which means good performance; a poor model will achieve an AUC near 0.5, which means poor performance [57]. The AUC can be defined using Equation (5), representing the average overall sensitivity values of _FPR_ and _TPR_.  
\[FPR=\frac{FP}{TN+FP}\text{ }and\text{ }TPR=\frac{TP}{TP+FN} \tag{5}\]  
Equation (6) shows how the _RMSE_ formulation, which determines the difference between the predicted and actual values, is computed as \(X_{obs,i}-X_{model,i}\), with \(X_{obs,i}\) being the observed value for \(ith\) and \(X_{model,i}\) being the model's predicted value.  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(X_{obs,i}-X_{model,i})^{2}}{n}} \tag{6}\]
## 2 Materials and Methods
### Imaging Modalities and Software  
OCT was performed by a commercial 50 kHz IVS 2000 swept source system (Santec, 5823 Ohkusa-Nenjozaka, Komaki-City, Aichi, Japan), an FDA-approved and CE-marked system, using a centre wavelength of \(\sim\)1300 nm. Measurements were performed as previously described [14]. Data analysis was performed using custom-made scripts written in MATLAB (Mathworks, Natick, MA, USA) [14].  
SDF is a hand-held, in-contact, green light (530 nm) imaging system that visualises microcirculation. Image contrast is based on the absorption of green light by oxyhaemoglobin in red blood cells (RBCs) [15]. Therefore, RBCs will appear as dark spots in the image. SDF measurements were performed by the Microscan (Microvision Medical, Amsterdam, The Netherlands), an FDA-approved system to monitor the microcirculation of critically ill patients. To stabilise the image, a custom-made stabiliser based on the design by [PERSON] et al. [16] was used to adhere the tissue to the imaging system. The SDF obtained images were analysed using the AVA3.2 software (Microvision Medical BV, Amsterdam, The Netherlands).  
LSCI is a near infrared imaging system with a laser operating at 785 nm and measures speckle fluctuations induced by the motion and change in number of RBCs. The LSCI was used as previously described [17]. Imaging was performed by the MoorFLPI-1(tm) (Moor instruments, Devon, UK), an FDA-approved and CE-marked system, positioned at 40 cm above the gastric conduit, as measured by a laser distance meter (Leica Geosystems D110, Leica Camera, Wetzlar, Germany).  
FA is a near infrared imaging modality that images the spatial distribution of the contrast agent indocyanine green (ICG), detecting at an emission wavelength of approximately 800 nm. FA  
Figure 1: Qualitative images by all four imaging techniques: upper left Optical Coherence Tomography (OCT), lower left Sidestream Darkfield Imaging (SDF), upper right Laser Speckle Contrast Imaging (LSCI), and lower right Fluorescence Angiography (FA). Perfusion measurements were performed at four locations (Loc1–4): 3 cm proximal of the level of (Loc1), at the level of (Loc2), 3 cm distal to (Loc3) the level of the watershed between the right and left gastroepiploic artery, and at the level of the gastric fundus (Loc4).  
was performed by the Artemis system(r) (Quest Medical Imaging, Middenmeer, The Netherlands), an FDA-approved and CE-marked system, after the intravenous administration of 2.5 mg of ICG. The Artemis system was placed under a 90-degree angle and also at a distance of 40 cm above the gastric conduit, as measured with the laser distance meter. A metric ruler was placed in the field of view for image calibration. The anaesthesiologist administered 2.5 mg of ICG intravenously. The perfusion was imaged in continuous video recordings of 2-3 min. In-house software was developed to plot the ICG in- and outflow in a fluorescence-time curve. A ruler in the field of view was used for calibration, and for every location a circular region of interest (ROI) with a diameter of 1 cm was used to measure the ICG signal.
## 3 Results
### Characterization and Screening of Isolated Strains  
To evaluate the potential candidates that could contribute to fermentation, their environmental tolerance and capability of certain enzyme production were tested. A total of 76 isolates was among the LAB isolates; _L. buchneri_ (Lbu1, Lbu3), _L. brevis_ (Lbr8, Lbr9, Lbr23), _L. casei_ (Lca27, Lca28, Lca29), _L. paracasei_ (Lpa34, Lpa35, Lpa36), and _L. hilgardii_ (Lhi46, Lhi47) exhibited better ethanol tolerance. Most LAB strains showed relatively poor tolerance to pH 3, whereas the majority of them could survive in a pH 4 environment. Regarding yeast strains, most of them displayed strong tolerance to the environment, except for _Sa. fibuligera_ (Sf6). _P. fermentans_ (Pf1), _Sa. fibuligera_ (Sf5), _S. cerevisiae_ (Sc8, Sc20), and _W. anomalus_ (Wa26) showed strong tolerance to both pH and ethanol. Generally, LAB strains exhibited more noticeable intraspecific phenotypic diversity compared to yeasts (Figure 1).  
In terms of enzyme activities, LAB Lbr9 showed the highest protease, amylase, and esterase activity, with values of 12.70, 0.25, and 2329.83 U/mL. Lbr17 also exhibited relatively high esterase activity (2316.67 U/mL). Most LAB strains displayed low amylase activities, ranging from 0.11 to 0.24 U/mL. Among yeast isolates, Sf6 was predominant in enzyme production, with the strongest enzyme activities. The highest protease, amylase, and esterase activities observed were 12.82, 2.38, and 59.4 U/mL, respectively. Additionally, Sc12 showed relatively high esterase activity with a value of 27.98 U/mL (Figure 2).  
Therefore, Lbr9, Lbr17, Sf6, and Sc12 were selected as candidate strains.
# Predictive Path Following and Collision Avoidance of Autonomous Connected Vehicles
## 3 Controller Synthesis
### Road Corridor Respect  
In this subsection, we develop the sufficient constraints that could be added to the optimization Problem (11) to guarantee our vehicle respect the driving corridors, generated from digital maps, in all the maneuvering. As for the reference path, it is assumed that we get two Bezier curves from the map; one for the left boundary and the other for the right one. Explicit representations of Bezier Curves (7) are not suitable for formulating the nonlinear constraints, and therefore we want to turn (7) into an implicit curve equation, that is one where we have no dependence on the parameter \(\theta\), i.e., \(F(x,y)=0\). This can be done using the resultant (or the Bezout's determinant). The advantage is that \(F(x,y)>0\) holds for points on one side of the Bezier curve, and \(F(x,y)<0\) for those on the other side. As a result, we get the following representation for quadratic curves [20]:  
\[F(x,y)=((a_{0}-x)b_{2}-a_{2}(b_{0}-y))^{2}-((a_{0}-x)b_{1}-a_{1}(b_{0}-y))\,( a_{1}b_{2}-a_{2}b_{1})\]  
where \(x(\theta)=a_{2}\theta^{2}+a_{1}\theta+a_{0}\), and \(y(\theta)=b_{2}\theta^{2}+b_{1}\theta+b_{0}\). The right and left boundary constraints are \(F_{R}(x,y)>0\) and \(F_{L}(x,y)<0\), respectively.