esa-sceva/satcom-chunk-collection huggingface dataset:
# The Structure-Activity Relationship of the Antioxidant Peptides from Natural Proteins
## 2 The Mechanism of the Antioxidant and the Evaluating Methods of Antioxidant Capacity
In the literature [3,15], four antioxidant capacity assessment tests are commonly employed for the HAT-based reactions: the Oxygen Radical Absorbance Capacity (ORAC) assay; the Total Radical Trapping Antioxidant Parameter (TRAP) assay; the Crocin Bleaching Assay (CBA) and the Lipid Peroxidation Assay (LPA). For EM-based redox reactions, several antioxidant assays are commonly used such as Trolox Equivalent Antioxidant Capacity (TEAC); 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and Copper II Reduction Capacity (CRC) assay. Moreover, transient metal ion chelation is another contribution of natural antioxidants. The chemical mechanism involved is inhibition of the Fenton reaction, which is responsible for hydroxyl radical formation and subsequently leads to radical chain reactions. The transient metal ion chelating capacity of antioxidants is evaluated with the aid of two assays: EDTA Equivalent Iron Chelation Capacity (EECC) and Carnosine Equivalent Iron Chelation Capacity (CECC) [28]. In fact, when EDTA chelating capacity is too high compared to peptide chelating properties to investigate the iron chelating capacity of peptides, the use of carnosine as reference is advised. Some methods that have been applied in evaluating antioxidant properties of peptides are listed in Table 1.  
The EC\({}_{50}\) values, defined as the sample effective concentration required to achieve 50% activity was adopted by many researchers and allows the comparison of experimental results obtained from various studies. Some problems may occur that could cause deviation from the true results of antioxidant capacity of peptides during assessment. For example, in the ABTS assay, Tyr, Trp and their derivative peptides were strongly dependent on pH, while those of Cys and Cys-containing peptides were unaffected by pH. The former usually fail to reach equilibrium over the short incubation period of 6-10 min typically at low pH [32]. Furthermore, TEAC and FRAP are conducted at different pH values of 7.4 and 3.6, respectively. A significant distinction in structure of the same peptide may emerge due to the huge difference of pH conditions in the two reaction systems, especially for those peptides with acidic and non-neutral amino acid residues [33]. In addition, due to the different underlying mechanism between the evaluation assays, the values or variation trends obtained by different assays, such as ABTS radical scavenging assays, the metal chelating and hydroxyl radical scavenging assays, were found to be different, even for the same amino sequence and composition. This indicates that appropriately selecting the assays may provide a shortcut to accurately and effectively evaluating the antioxidant capacity, while deeply understanding the structure-activity relationships of the peptides may also give useful information for the selection of an appropriate assay.
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 2 Materials and Methods
### The Loss Function  
In practical applications of CD, the number of unchanged pixels is often much greater than that of changed ones. At the suggestion of [11], one way to alleviate the impact of sample imbalance is to introduce a hybrid and weighted loss function. Here, we consider the combination of Focal loss and Dice loss [1].  
The hybrid loss function (\(L\)) can be formulated as follows:  
\[L=L_{dice}+L_{focal} \tag{9}\]  
Dice loss (\(L_{dice}\), see below) attaches similar importance to false negatives and false positives, and it is effectively immune to the sample-imbalance issue [33].  
\[L_{dice}=1-\frac{I+\varepsilon}{U-I+\varepsilon} \tag{10}\]  
\[I=\sum_{1}^{N}t_{i}y_{i}\text{ and }U=\sum_{1}^{N}\left(t_{i}^{2}+y_{i}^{2}\right) \tag{11}\]  
where the smoothing factor \(\varepsilon\) is an extremely small number (here, \(\varepsilon\) is set to 1e-7); \(0\leq y_{i}\leq 1\) is the predicted value processed using the sigmoid/softmax activation function; and \(t_{i}\) is the ground-truth value, which is either 0 or 1.  
\(L_{focal}\) loss is defined as:  
\[L_{focal}(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\log(p_{t}) \tag{12}\]  
where \(p_{t}\) is the probability that the model predicts for the ground-truth object. Focal loss adds \((1-p_{t})^{\gamma}\) as a modulating term to the standard cross-entropy loss so that it can focus on learning hard/misclassified samples. Setting \(\gamma>0\) reduces the relative loss for well-classified samples (\(p_{t}>0.50\)), putting more focus on hard, misclassified samples. At the same time, \(L_{focal}\) gives high weights to the rare class and small weights to the dominating or common class. These weights are referred to as \(\alpha_{t}\). In this study, we empirically set \(\gamma=2\), and \(\alpha_{t}\) = 0.45.  
Because the sigmoid/softmax function, which maps any real values to the range [0, 1], has already been embedded in the loss functions \(L_{focal}\) and \(L_{dice}\), we did not end our algorithmic program with the sigmoid or softmax statement in order to avoid the vanishing gradients caused by repetitive normalization [1]. However, we made the sigmoid/softmax function available again when calculating the evaluation metrics in order to scale all of the predicting data between 0 and 1.
## 2 Details and Performance Analysis of QODELFA
### The QODELFA  
The main procedures mentioned in the previous subsection are uniquely combined to construct the QODELFA as depicted in the flowchart given in Figure 1. The stochastic parameters of the proposed technique are varied in a step-wise fashion to select the optimal parameter values able to provide the best algorithmic performance. First, an initial population of random solutions is created and then enhanced by using the QOBL concept. Thereafter, the DE is used to improve the initial population using mutation, crossover and selection. After that, the LF perturbation is executed along with the DE's crossover and selection operations. The greedy selection is utilized as a selection mechanism along the stages of the algorithm. Finally, the iterations are terminated when the stopping criteria are satisfied.
## 2 Model Establishment and Analysis
### Selection of Materials
#### 2.2.2 S-N Curve of Material  
In the simulation computation of the fatigue life of the fifth wheel, the fitted Stress-Number of cycles to failure curves (S-N curves) are required to be imported into the software for fatigue calculation. Therefore, the S-N curves of two materials are necessary.  
The S-N curve of the material delineates the correlation between the applied stress and the actual number of cycles to failure. Herein, S signifies the magnitude of stress experienced under an external load, while N denotes the fatigue life of the workpiece. Typically, the S-N curve of the material is derived from experimental data, specifically through a full inversion rotation bending experiment.  
This paper assesses the approximate fatigue characteristics of materials in accordance with a methodology for estimating the S-N curve proposed by [PERSON] [31]. The formula is as follows:  
\[S_{a}=10^{C}N^{b} \tag{1}\]  
The relation between parameters \(C\) and \(b\) in Equation (1) is as follows:  
\[\left\{\begin{array}{l}C=\lg\frac{\sigma_{000}^{2}}{\sigma_{-1}}\\ b=-\frac{1}{3}\lg\frac{\sigma_{100}}{\sigma_{-1}}\end{array}\right. \tag{2}\]  
In Equation (2), \(\sigma_{1000}=0.9\sigma_{b}\), \(\sigma_{b}\) is the tensile limit, when \(\sigma_{b}\leq\) 1379.2 MPa, \(\sigma_{-1}=0.5\sigma_{b}\).  
The S-N curve parameters of Q345 and SD600 calculated according to the above formula are shown in Table 5.
## 3 Results and Discussion
### Response Profile of E-Nose for Unfermented Specimens
#### 3.2.2 PCA of E-Nose Dataset of Unfermented Specimens  
PCA is a linear categorization of the essential information after dimensionality reduction of the E-nose dataset [2,35]. The PCA loading map of four groups of unfermented specimens is shown in Figure 3. As shown in the figure, three PCs were answerable for 64.5% (PC1), 80.9% (PC2), and 90.5% (PC3) of the total contribution rates. The total difference in contribution was higher than 80%, fundamentally overlaying the overarching features of the specimens. The findings revealed that the four groups of unfermented specimens possessed different volatile substances, whereas the distribution in space kept certain regularity. The variations among specimens without the addition of yarn chickpea millks (CP, CPB) and with the addition of yarn chickpea millks (CPY, CPBY) can be portrayed in PC1. The samples CP-0 h and CPB-0 h were located in the negative areas of PC1 and moved along the negative axis. Both location points were superimposed and near to each other, revealing resemblances in volatile aroma substances. The location of specimens without the addition of yarn milk was far from those of samples with the addition of yarn milk. The sample CPY-0 h was located in the positive area of PC1 and moved from the positive to the negative area of PC2. The sample CPYB-0 h was distributed in the positive area of PC1 and the negative portion of PC2. Moreover, CPY-0 h and CPYB-0 h were located in the positive areas of PC1, implying that the aroma of samples was dissimilar from those of samples without yarn addition and enzymolysis. The above information implied that E-nose could differentiate the flavor of chickpea milk with enzymolysis and yarn addition to some degree.  
Figure 2: Radar plot of four groups of unfermented specimens. CP-0 h, unfermented chickpea milk; CPB-0 h, unfermented chickpea milk with papain treatment; CPY-0 h, unfermented chickpea milk with yarn addition; CPBY-0 h, unfermented chickpea milk with papain treatment plus yarn addition.
## 2 Materials and Methods  
### Chemicals, Bacterial Strains, and Cultivation  
Isopropyl-\(\beta\)-d-thiogalactoside (IPTG), kanamycin (Km), methanol (HPLC grade) and ectoine used in the experiment were supplied by Macklin Biochemical Co., Ltd. (Shanghai, China). All other chemicals were purchased from Sinopharm Group Co. Ltd. (Shanghai, China). The strains and plasmids constructed and used in this study are listed in Table 1. The host strains for cloning, protein expression and ectoine production, were cultivated in LB medium containing 1.0% tryptone, 0.5% yeast extract, and 1.0% NaCl. The recombinant  
Figure 1: The engineered ectoine biosynthetic pathway in _E. coli_ using _ectABC_S. salarius_ cluster from _Salinicola salarius_ 1A01339. EcAsk: L-aspartate-4-phosphotransferase in _Escherichia coli_; EcAsd: L-2, 4-diaminobutyrate:2-oxoglutarate-4-aminotransferase in _Escherichia coli_; Asp: L-aspartate; ASA: L-aspartate-beta-semialdehyde; DABA: L-2, 4-diaminobutyric acid; NADA: N-\(\gamma\)-acetylldiaminobutyric acid.  
_E. coli_ harboring ectoine production genes were cultured and expressed in LB media with 50 \(\upmu\)g/mL Km. For overproduction of ectoine synthetic enzymes, fermentation medium (FM) was used for fed-batch cultures which contained per liter: 0.8% yeast extract, 1.2% tryptone, 0.7% K\({}_{2}\)HPO\({}_{4}\), 0.5% NaCl, 0.3% (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\), 0.05% MgSO\({}_{4}\) 7H\({}_{2}\)O, 0.5% glycerol and 0.5% defoamer. The feeding solution for fed-batch cultures contained 10% yeast extract, 2.5% tryptone and 40% glycerol.  
Gene Cloning, and Expression for EctA\({}_{S.\ salarius}\), EctB\({}_{S.\ salarius}\), EctC\({}_{S.\ salarius}\)  
The ectoine biosynthetic cluster used for this study was obtained by bioinformatic analysis of the genome sequence from _S. salarius_ 1A01339. Firstly, the gene-mining and prediction of the novel _ectABC_ cluster were performed by Basic Local Alignment Search Tool (BLAST, [[https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi)]([https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi)) (accessed on 30 June 2021)) using typical _ectABC_ sequence from _Halomonas elongata_ DSM 2581 [21]. According to the whole genome shotgun sequence of _S. salarius_ 1A01339 (GenBank assembly accession NZ_NHOU01000014), the predicted ectoine synthetic cluster, named as _ectABC\({}_{S.\ salarius}\)_, was synthesized artificially by GenScript Biotech Corporation (Nanjing, China). Then, the DNA fragments were ligated into plasmid pDK6 to construct the plasmid pDK-_ectABC\({}_{S.\ salarius}\)_ and was further transformed into the competent cells of _E. coli_ BL21(DE3) for protein expression. The plasmid DNA extraction, PCR, ligation, plasmid construction, and transformation were conducted following the method described previously by [PERSON] et al. [25].
## 2 Methods
### Algorithm Implementation  
Here, we present a summary of the above content in Algorithm 2, which consists of two stages: the first stage inputs the time series data \(x(t)\) and \(x(t-l)\), and the output is the causal relationship diagram and the optimal causal path. First, the Granger causal coefficient is solved using LSTM; then, the causal relationship diagram is drawn according to the coefficient; finally, the weight is calculated to obtain the optimal causal relationship path. The second stage inputs the causal path of the abnormal variable, and the output is the probability of abnormality of each underlying factor. First, the data are converted into a binary vector; then, the model is trained with the maximum evidence lower bound as the indicator; finally, the probabilities for each factor's abnormality are output.  
```
1 Input: Time series data of process variables \(x(t)\) and \(x(t-l),l=1,\ldots,q\); Output: Each abnormal factor \(z_{j}\) and probability \(p_{j}\); Initialization: Trained LSTM model and BNN model;
2I: Solve the optimal causal path
3 Input \(x(t)\) and \(x(t-l)\) data into LSTM to obtain Granger causal coefficient \(a_{ij}\);
4 Standardize Granger causal coefficient \(\alpha_{ij}=\frac{a_{ij}}{|a_{i}|!}\);
5 Calculate the frequency of each process variable in the causal path \(\mathcal{G}_{i}=\frac{\Gamma_{lk}}{\sum_{k}}\);
6 Calculate the weight of each causal path \(\mathcal{W}=\mathcal{A}\odot\mathcal{R}\);
7 According to the weight \(\mathcal{W}\), combined with the causal relationship diagram, the optimal causal path is obtained;
8 II: Solve abnormal factors and their probabilities
9 According to the optimal causal path, calculate the weights \(c_{k}\) and \(c_{i}\) of each variable, and construct the input vector;
10 Input the vector into the BNN to calculate each abnormal factor \(z_{j}\) and its probability \(p_{j}\).
```  
**Algorithm 2**Root cause tracing based on Adam variational inference Bayesian neural network.
# A New Method for Haul Road Design in Open-Pit Mines to Support Efficient Truck Haulage Operations
## 3 Methods
### Modification of Road Layout Considering the Radius of Curvature Constraints  
The radius of curvature must be considered when determining the horizontal layout of the haul road. If a sufficient radius of curvature is not considered, the travel time and cost could be increased due to a decrease in the operational stability of a truck. If a sufficient radius of curvature is obtained, a stable truck speed can be maintained and the wear of truck wheels can be reduced, thereby enabling an efficient haulage operation.  
The radius of curvature should be designed in such a manner that the centrifugal force on the truck during rotation and the friction between the truck tire and road surface are balanced (see Figure 8a).  
Figure 7: Procedure of line simplification using the Douglas-Peucker algorithm.  
The equation for the minimum radius of curvature that should be considered when designing a road is as follows [2]:  
\[\text{R}=\ \frac{V^{2}}{127(e+f)} \tag{5}\]  
where R is the radius of curvature (m), \(V\) is the vehicle speed (km/h), \(e\) is the super-elevation rate (m/m), and \(f\) is the friction coefficient between the tire and road surface. The vehicle speed means the maximum speed when the truck runs on a downward slope without a load. Super-elevation refers to the degree of banking along one edge of a road (see Figure 8b). Applying the difference in altitude at both edges of the road decreases the centrifugal force on the truck during rotation and allows the truck to rotate stably. The super-elevation should be designed not to exceed 5%-7% (approximately 3\({}^{\circ}\)-4\({}^{\circ}\)) [5]. [PERSON] and [PERSON] [2] proposed super-elevation on a haul road according to the radius of curvature and vehicle speed (see Table 1). The friction coefficient varies according to the road surface and is assumed as 0.3 if the surface is sandy and soft or muddy or as 0.45 if the road surface partially consists of gravel [5].
## 4 The Proposed Method
### DGNDP (Synthetic Directed Graph Based on Node Differential Privacy) Algorithm
#### 4.2.3. GGM (Generating Synthetic Graph Based on Graph Modification)
Therefore, the GSEM algorithm satisfies differential privacy. The ADPRA algorithm satisfies \(\varepsilon\)-differential privacy. In this algorithm, the important task is to add the Laplace noise to the degree sequence by using differential privacy with micro-aggregation. In a graph \(Sg\), let \(S\) be a query function : \(Sg\to Ds_{1}\), where \(Ds_{1}\) is a degree sequence from SDEM algorithm. Given that there is only one node difference between \(Sg\) and \(Sg^{\prime}\), the sensitivity of \(S\) is

\[\Delta S =\max_{Sg,Sg^{\prime}}\bigl{|}S(Sg)-S\bigl{(}Sg^{\prime}\bigr{)} \bigr{|}_{1}\] \[=\max_{Sg,Sg^{\prime}}\bigl{|}Ds1-Ds1^{\prime}\bigr{|}_{1}\] \[=d_{\max}\]

After the micro-aggregation degree sequence \(Dms_{1}\) is obtained from \(Ds_{1}\), given a query function \(Sm\): \(Sg_{1}\to Dms_{1}\), when there is only one node difference between \(Sg\) and \(Sg^{\prime}\), there is \(Sm(Sg)=Dms_{1}\), \(Sm(Sg)=Dms_{2}\). The sensitivity of \(Sm\) is shown as follows. \[\Delta Sm =\max_{Sg,Sg^{\prime}}\bigl{|}Sm(Sg)-Sm\bigl{(}Sg^{\prime}\bigr{)} \bigr{|}_{1}\] \[\frac{p_{r}(E(Sg,t))}{p_{r}(E(Sg,t))}=\frac{\frac{\exp\left(-\frac{ \varepsilon U(Sg,t)}{2\Delta U}\right)}{\exp\left(-\frac{\varepsilon U(Sg,t^{ {}^{\prime}})}{2\Delta U}\right)}}{\frac{\exp\left(-\frac{\varepsilon U(Sg,t^{ {}^{\prime}})}{2\Delta U}\right)}{\sum_{t^{{}^{\prime}}\in\mathcal{O}}\exp \left(-\frac{\varepsilon U(Sg,t^{{}^{\prime}})}{2\Delta U}\right)}}\]0

According to the previous analysis, if the difference between two degree sequences is \(dmax\), the difference between two micro-aggregation degree sequences is \(\frac{dmax}{k}\), \(k\) is the number of entities contained in a cluster. Thus, the sensitivity of \(Sm\) is \(\frac{dmax}{k}\). Because the sensitivity is reduced, less noise is added on the \(Dms_{1}\), which improves the data utility of this algorithm. In summary, the ADPRA algorithm satisfies differential privacy, which is proved as follows. Let \(Pr[Sg]\) represents the probability density function of \(LA(Sg,Sm,e)\), and \(Pr[Sg^{\prime}]\) indicates the probability density function of \(LA(Sg^{\prime},Sm,e)\).
## 1 Introduction
To summarize, the objective of this adaption is to not only decrease the delay, but also to improve the percentage of successful real-time packets in a guaranteed WSN lifetime. Simulation results are given to prove the effectiveness of the proposed scheme in the aforementioned performance metrics. The rest of this paper is organized as follows: In Section 2, we discuss the related works. The proposed scheme and analysis of the algorithms for a guaranteed network lifetime and real-time communications are explained in Sections 3 and 4. In Section 5, the simulation experiments that evaluate this scheme are presented. Finally, Section 5 concludes with a critical summary and some direction for future work.
# Study on Low Temperature Drying Characteristics of Chicken Manure in a Humid Environment
## 3 Results and Discussion
### Drying Characteristics at Different Air Velocities  
Figure 4 presents the drying curves and drying rate curves of chicken manure at different air velocities of 0.6, 1.2, and 1.8 m/s. In this experimental group, the thickness of the manure layer was maintained at 40 mm, the drying temperature was maintained at 25 \({}^{\circ}\)C, and the relative humidity was maintained at 85%. As can be observed from Figure 4, air velocity had the same specific effect on the drying rate as did temperature. It took 54, 48, and 40 h to reduce the wet basis moisture content of the chicken manure from the initial 76.4% to an equilibrium moisture ratio, respectively. Increasing the air velocity can effectively reduce the drying time of the low-temperature drying process. This was because the increase in air flow over the surface of the chicken manure will result in a decrease in the thickness of the flowing boundary layer and a decrease in the resistance of the water vapor on the surface of the chicken manure to the air, which is conducive to the evaporation of water in the chicken manure.  
Figure 4b shows the drying rate curve for different air velocities. At the early drying stage, the air velocity of 1.8 m/s had the highest drying rate. This was because the surface water evaporation resistance was greater than the internal water migration resistance, and the dehydration rate was mainly affected by the surface water diffusion rate of the chicken manure. At the middle drying stage, there was no significant difference in drying rate between 1.2 m/s and 1.8 m/s (\(p\) > 0.05). This result was related to the amount of chicken manure used in the experiment and the drying ability of wet air. In the late drying period, the water evaporation resistance on the surface of the chicken manure continued to decrease with the drying process, and the drying rates were close to each other under different air velocities. The reason was that the water evaporation rate was mainly determined by  
Figure 4: (**a**) Drying curves of chicken manure at different air velocities; (**b**) drying rate curves of chicken manure at different air velocities. Note: The dotted line in the figure indicates that the wet basis moisture content is 30%.  
the water diffusion rate inside the chicken manure when the surface water evaporation resistance was less than the internal water migration resistance.
###### Abstract  
This study explores the efficacy of a sacrificial anode cathodic protection (SACP) system with an activated carbon-based conductive mortar in bridge structures. In the previous Part 1 study, various admixtures were compared to identify a conductive mortar for enhancing the performance of the SACP system, assessed through electrical conductivity, resistivity, cathodic protection (CP) potential and current, and 4 h depolarization potential. Part 2 extends the investigation by applying the developed conductive mortar containing activated carbon to an SACP system on an actual bridge structure in which corrosion has already been initiated. Before CP installation, the physical properties of the conductive mortar were evaluated to satisfy the standard requirements for concrete structure maintenance. Subsequently, zinc mesh and bulk anodes were installed on the bridge pier, followed by the application of a conductive mortar with an admixture ratio of 5%. Over a four-year period, performance was measured through regular 4 h depolarization potential checks and visual inspections. The SACP system with the conductive mortar demonstrated superior CP performance compared to the general mortar, confirming the effectiveness of the developed conductive mortar. Visual inspection after four years confirmed the workability of the SACP system with conductive mortar.  
conductive mortar; activated carbon; sacrificial anode cathodic protection; corrosion; depolarization; bridge