esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Myofibrillar Proteins
#### 3.1.3 UV Absorption Spectra  
Protein molecules contain some chromophores, tyrosine (Tyr) and tryptophan (Trp) residues, among others. The side chain groups of Trp and Tyr residues have an absorption effect on ultraviolet light, generating corresponding UV absorption spectra [26]. UV spectra measurement is an effective instrument used for identifying protein changes in conformation, hydration, dissociation or denaturation during frozen storage [27].  
Figure 2 shows the evolution of the UV absorption spectra of MPs during frozen storage. The maximum absorption wavelength of MPs was near 275 nm, and the absorption spectra of the samples in each group were red-shifted after 90 days of frozen storage. The fluctuation of the maximum absorption wavelength may have been caused by the alteration in the Tyr microenvironment [28].  
As the frozen storage progressed, the UV absorption intensity of the MPs gradually decreased. The UV absorption intensity of the control (0%) significantly declined. This was due to the formation of MP aggregates during the freezing process, which caused the chromogenic amino acid groups exposed to the outside to be hidden inside again, resulting in a decrease in UV absorption intensity [29]. Meanwhile, the aggregation and denaturation of MPs were among the reason leading to a lower MP gel-forming ability. Moreover, the OVA/\(\beta\)CD treatment showed a smoother decline in UV absorption intensity. Therefore, it could be speculated that the OVA/\(\beta\)CD treatment prevented the MPs from aggregation as well as denaturation, and maintained stability of the MPs tertiary structures during  
Figure 1: Surface hydrophobicity of myofibrillar proteins treated with different concentrations of OVA/\(\beta\)CD during 90 days of frozen storage at \(-18\)\({}^{\circ}\)C. a–d mean significant differences (\(p<0.05\)) within the similar treatments with different storage times. A–D mean significant differences (\(p<0.05\)) within the same storage period of different concentrations.  
frozen storage. The above-mentioned results are in accordance with the conclusion on the changes of the SH group mentioned in Table 1. The SH group belongs to a weak secondary bond which helps to stabilize the tertiary structure of the protein. The results showed that the lower SH oxidation process observed was in the MPs treated with OVA/\(\beta\)CD, which maintained the integrity of the tertiary structure of MPs.
## 5 Results and Discussion  
Based on actual engineering practice and theoretical analysis, this study proposed a method of three-hole simultaneous blasting for open-pit mine bench blasting and conducted onsite blasting experiments. The onsite blasting tests highlighted that the three-hole simultaneous blasting approach could effectively overcome the problem of rear row strain in the blasting area. At the same time, the obtained blasting piles were relatively concentrated, with a uniform size and no large blocks generated. To further analyze the advantages of the three-hole simultaneous blasting method and to examine the evolution process of rock fractures, a comparative analysis was conducted for the three-hole simultaneous blasting and the hole-by-hole blasting, employing numerical simulations. It was found that under the three-hole simultaneous blasting method, obvious damage was reported in the form of macroscopic cracks, formed in the direction of the connection line at the center of the blast hole. This was because the shock wave generated by the explosion met between two blast holes. In addition, the rock on the connection line was subjected to compressive stress, resulting in vertical tensile stress. Generally, the tensile strength of rocks was much smaller than the compressive strength, and cracks occurred along the connection between the two holes. This finding was consistent with the principle of smooth blasting studied by many researchers in the literature [35]. At the same time, compared to the hole-by-hole blasting method, the effective stress and displacement generated in front of the blast hole were relatively large under the three-hole simultaneous blasting method. In this case, a maximum effective stress of 67.9 GPa and a maximum displacement of 31.9 cm were reported. This was because the single-stage charge amount of the three-hole simultaneous blasting was three times that of the hole-by-hole blasting, improving the blasting power significantly.  
This paper presented a three-hole simultaneous blasting technology that combined the stepwise blasting technology and the smooth blasting theory. The proposed approach yielded more advantages in terms of the blasting effect [19; 36]. To the best of the authors' knowledge, this is the first time this technology has been proposed, and there have been  
Figure 19: Histogram of maximum displacement at the measurement points of the model.  
no similar reports in the relevant literature. In this regard, this technology is of great significance and provides a reference for similar mine bench blasting.  
However, the mechanism of rock fragmentation by blasting is complex. Next, it is necessary to further strengthen the on-site testing and numerical simulation research of the three-hole step-by-step detonation technology in different mines.
## 4 Aggregate Analysis
### Decomposing the Overall Commuting Time  
As discussed above, the changes in overall commuting time were significant, but most changes in commuting time by mode were insignificant; so, we decomposed the overall commuting time. We calculated the average travel time of all linked trips by each mode and used the mode shares as the weights to calculate the weighted average travel time. For each survey, the sum of the weighted commuting time of each mode was equal to the overall commuting time. We used the mode share as a weight to aggregate the average commuting time, shown in Table 5. For the driving trips, the average commuting time weighted by mode share in 1994 was about 19 min; it was calculated by multiplying the average travel time of 22.0 min by the mode share of 86.9%. Though the average commuting time weighted by the mode share of the driving trips decreased, the average commuting time weighted by the mode share of the trips by all other modes increased. The bar plot (Figure 2) shows the changes between the two years.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Mode** & **1994 All** & **2011 All** & **Difference** & **\% Change** & **1994 CBD** & **2011 CBD** & **Difference** & **\% Change** & **1994** & **2011** & **2011** & **Difference** \\  & **Trips** & **Trips** & & & & & & & & & **Trips** & **Trips** & \\ \hline Driving & 22.0 & 22.6 & 0.6 & 2.65 & 24.9 & 26.3 & 1.4 & 5.90 & 21.8 & 22.3 & 0.5 \\ Passenger & 24.1 & 20.6 & \(-\)3.5 **\({}^{**}\)** & \(-\)14.59 & 24.9 & 19.5 & \(-\)5.4 & \(-\)21.78 & 23.9 & 20.7 & \(-\)3.2 \\ Bus & 42.0 & 41.6 & \(-\)0.4 & \(-\)0.11 & 38.2 & 36.4 & \(-\)1.8 & \(-\)4.78 & 47.7 & 46.6 & \(-\)1.1 \\ Rail & 56.8 & 51.0 & \(-\)5.8 & \(-\)10.18 & 57.7 & 46.8 & 10.9 & \(-\)18.89 & 55.8 & 55.2 & \(-\)0.6 \\ Walk & 11.7 & 12.6 & 0.9 & 7.43 & 16.5 & 20.9 & 4.4 & 26.69 & 10.3 & 11.5 & 1.2 \\ Bike & 26.6 & 25.8 & \(-\)0.8 & \(-\)2.99 & 29.5 & 28.3 & \(-\)1.2 & \(-\)4.06 & 25.9 & 25.1 & \(-\)0.8 \\ Overall & 22.8 & 24.8 & 20.\({}^{*}\) & 84.0 & 28.8 & 32.4 & 3.6 \({}^{*}\) & 12.39 & 22.1 & 23.7 & 1.6 \({}^{*}\) \\ \hline \hline \end{tabular} Notes: * indicates \(p\leq 0.05\); ** indicates \(p\leq 0.0083\).  
\end{table}
Table 4: Weighted average HBW trip travel time in minutes by mode and area.  
of the CBD bus trips increased slightly. This revealed the commuters' preference for rail over bus for long trips. When bus and rail were both available, the commuters tended to choose the rail for long trips.  
Table 6 also shows the results of the difference in means tests for commuting distance by mode and area. Bonferroni correction was used for multiple comparisons (weighted \(t\)-test, \(p<0.0125\)). When considering the commuting trips by all modes, the travel distance difference was significant (weighted \(t\)-test, \(p<0.05\)) for CBD and non-CBD trips. For the commuting distance by individual mode, the travel distance difference was only significant for the driving and rail trips.
## 6 Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic** & **Name of Dataset** & **Description** \\ \hline Smart maintenance & MetroPT [186] & Consists of samples of analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed). & \\  & Alarm Logs in Packaging Industry (ALPI) [187] & Contains a sequence of alarms logged by packaging equipment in an industrial environment. The collection includes data logged by 20 machines, deployed in different plants around the world, from 21 February 2019 to 17 June 2020. & \\ Quality inspection & UCI Machine Learning Repository [119] & A UCI collection of databases, domain theories, and data generators. There are several datasets from the manufacturing domain that are used for algorithm validation, including the semi-conductor domain. & \\  & Outlier Detection DataSets [130] & OMDS provide access to a large collection of outlier detection datasets with ground truth (if available). The focus of the repository is to provide datasets from different domains including several manufacturing domains (wafer map). & \\  & & The dataset used for The Third International Knowledge Discovery and Data Mining Tools Competition, the competition task was to build a network intrusion detector algorithm. & \\  & The dataset includes seven different attack scenarios, namely Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside. The attacking infrastructure includes 50 machines and the victim organization has 5 departments including 420 PCs and 30 servers. & \\  & The dataset contains different modern reflective DDoS attacks such as CIC DDoS attack dataset [67] & PortMap, NetBIOS, LDAP, MSSQL, UDP, UDP-Lag, SYN, NTP, DNS and SNMP. & \\  & Intrusion detection and privacy attack dataset [68; 69] & Dataset for developing and evaluating different IEEE 802.11 & \\  & The University of Arizona datasets [70] & Wi-Fi algorithms. & \\  & UTIL: An Ultra-wideband Time-difference-of-arrival & An Ultra-wideband Time-difference-of-arrival & \\ Localization & Time-difference-of-arrival & Indoor Localization Dataset [194] & \\  & CSI Dataset towards 5G NR & & \\  & High-Precision & & \\  & Positioning [195] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of major and typical datasets for IIoT machine learning applications  
**Author Contributions:** Abstract, Introduction and Conclusions, P.V. ; Safety and Security, Asset Localization, G.H. ; Quality Control, D.F. ; Maintenance, A.F. and D.F. ; conceptualization, D.F., G.H., A.F. and P.V. ; methodology, P.V. ; investigation, D.F., G.H., A.F. and P.V. ; resources, P.V. ; writing--original draft preparation, D.F., G.H., A.F. and P.V. ; supervision, P.V. ; funding acquisition, D.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflict of interest.
# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 4 Experiment
### Implementation Details  
We implement the proposed method based on the Pytorch framework and the Ubuntu 18.04 system. The NVIDIA GeForce GTX 3090 graphics cards are used to accelerate the training process, which has 24 Gigabyte memory. The batch size and total iteration number are set to 8 and 120,000, respectively, for the experiments on the LVN dataset. The polynomial learning rate decay schedule and SGD optimizer are employed. The initial learning rate and the weight decay are set to 0.008 and 0.9, respectively. All the images perform data augmentation, including random angle rotation, random translation, random zooming, random shearing, and horizontal flipping with ratios between 0.5 and 2. In addition, we employ a sliding window approach [40] for both the training and testing phases. Each image was cropped into patches of size 256 \(\times\) 256 with a stride of 128 \(\times\) 128. The undersized regions are filled with a pixel value of 255.  
Figure 4: Ground truth in the LVN dataset (Corresponding with Figure 3).  
For the first stage of the transformer encoder, we select the base version as our backbone. Specifically, the ViT consists of twelve layers and sets the hidden size as 768, MLP size as 3072, and head numbers as twelve. The patch size is set to 16 \(\times\) 16. The outputs of the third, sixth, ninth, and final layers of ViT are saved and used for fine-grained feature recognition in the second stage. In the second stage, the downsampling operation is used to reduce the resolution by half by setting the stride of convolution as 2. In addition, the bilinear interpolation (the ratio is 2) is adopted to upsample the resolutions of the decoder. Finally, by utilizing 1 \(\times\) 1 convolution, the final binary results are generated.
## 2 Materials and Methods
### Human Health Risk Assessment  
Human health risk assessment is a hypothetical basis for determining the probability of risks occurring to individuals after exposure to various hazardous elements [12]. Understanding the pathways and risks of trace metal exposure in contaminated soils, the distribution of their sources, and the physical differences between exposed individuals are fundamental prerequisites for preventing and controlling soil contamination [27]. This study considers three pathways of trace metal exposure: ingestion, dermal absorption, and inhalation, as well as whether the subjects are adults or children. Therefore, it is necessary to start by determining human intake through ingestion (DEing), dermal absorption (DEder), and inhalation (DEinh). Following the health risk assessment model proposed by USEPA [14] and USEPA [15], non-carcinogenic risk (HQ) and carcinogenic risk (CR) were evaluated for the elements As, Cu, Pb, and Zn. Copper toxicity is generally not an important concern for human health due to homeostatic mechanisms that control Cu excretion [16; 17]. However, [PERSON] et al. [19] argued that there are still unresolved issues regarding understanding the effects of Cu exposure on human health. It is important to note that HQ values \(<\) 1 indicate no significant risk of non-carcinogenic effects, while HQ values \(>\) 1 suggest that adverse effects may occur [18; 28]. For cancer risk, values indicating no risk fall within the range of 10\({}^{-6}\) to 10\({}^{-4}\)[23], meaning that CR \(>\) 1.0 \(\times\) 10\({}^{-4}\) indicates a significant cancer risk to human health, while values below 1.0 \(\times\) 10\({}^{-6}\) suggest that the exposure is not harmful [24; 25; 26; 29]. Only cancer risks associated with As and Pb have been evaluated in this study since USEPA has updated the slope factor values for these elements. USEPA [21] has not established slope factors for Cu and Zn, as standard values are still unavailable. The risks are evaluated and calculated according to [PERSON] et al. [25] as shown in the equations in Equation S2 and Table S3.  
Noncarcinogenic Risk Characterization  
The noncarcinogenic risk for each element in the sample can be obtained according to USEPA [12] following the equation given in Equation S3. Cancer risk (\(CR_{SI}\)) is given by the equation in Equation S4 and Table S4, which also considers the three exposure pathways according to the USEPA [21].
# Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks
## 5 Performance Evaluation and Results
### Protocol Overhead  
Figure 9 shows the average overhead for different protocols and geocast groups shown in Table 3. Since results can vary significantly, a logarithmic scale is used for the overhead. The results for MC-GeoDirectDelivery are not presented, since this protocol, similarly to Direct Delivery, only delivers messages to interested nodes, resulting in an overhead of zero.  
The network overhead tends to increase for all protocols as the geocast groups' penetration rates drop. For group one, which has 100% interested nodes, the overhead is the lowest, as most message replications are delivered to nodes interested in receiving them. For group three, which only has 25% interested nodes, the overhead is the highest, as nodes will also be replicating messages for many other nodes that do not want to receive those messages.  
Looking at the results for geocast group with ID = 1, which includes all nodes deployed in the network, it is possible to see that the performance of the GeoS&W and V-GRADIENT protocols is similar and tends to significantly improve as the density level increases. Naturally, this enhancement is justifiable not only by the use of the same spraying mechanism on both strategies; on the other side, for the remaining geocast groups, the GeoS&W protocol yields better results since it imposes a limit on the number of copies to be transmitted, unlike the V-GRADIENT protocol. In turn, at the cost of jeopardizing the performance in terms of overhead, the V-GRADIENT algorithm exploits node  
Figure 9: Overhead as a function of node density, protocol, and geocast group.  
resources located inside the flooding range, \(R_{floading}\), to increase the delivery probability of geocast groups with low penetration rates, which was considered more important than the corresponding overhead cost.
## 2 System and Channel Model  
### System Model  
RIS-assisted FSO communication is shown in Figure 1. There is no direct LoS between the source (S) and the destination (D) due to the obstruction caused by some building and obstacles. The source is equipped with a LD which transmits symbols employing intensity modulation and direct detection (IM/DD) and on-off shift keying (OOK) modulation schemes. A malicious jammer, which UAVs are equipped with, is randomly present, attempting to disrupt the legitimate transmission link by opportunistically sending an optical signal. Thus, at any given instant, the jammer can jam either the received signal at the RIS or the received signal at the destination. Therefore, we consider two jamming scenarios: Scenario 1 involves a malicious jammer causing jamming at the destination, as depicted in Figure 1a, and Scenario 2 involves a malicious jammer causing jamming at the RIS, as depicted in Figure 1b.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 2 Related Works
### Autoregressive (AR) Model  
An autoregressive (AR) model is a statistical model that represents a time series process. The AR model refers to the order (p) element in the autoregressive integrated moving average (ARIMA), which has the order (p, d, q) and autoregressive moving average (ARMA) with the order (p, q) [43]. The AR, MA, ARMA, and ARIMA models are commonly used in time series forecasting studies [44]. AR models are composed of three steps, the first of which is used to find correlations in time data. The second step defines the model parameters, and the third step forecasts the time series future points.  
Numerous studies have been conducted on traffic modeling using AR, MA, ARMA, and ARIMA statistical models. A suggested methodology integrating ARIMA with generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) [45] was applied for traffic flow prediction in short-term time series; however, the methodology failed to achieve significant improvement over the standard ARIMA, where the parameters model in ARIMA is easily interpretable, unlike the GARCH model [43].  
Although all these time series methods are widely used in traffic prediction, the majority of the research found in the literature focuses on applying these methods on large time windows [46]. A study by [PERSON] et al. on short-term traffic speed prediction provides a comparison between four prediction methods with different data collected in a varying time window ranging from 1 min up to 30 mins [47]. The study proposes a seasonal discrete grey model (SDGM) and compares the prediction accuracy with the seasonal autoregressive integrated moving average (SARIMA) model, artificial neural network (ANN) model, and support vector regression (SVR) model. The findings of this study show that the prediction accuracy increases when the target time window is more than 10 min, whilst the prediction of the time window that is fewer than 10 min suffers from instability. Additionally, the study shows that the SARIMA performance has the highest error indicator in the prediction results. A probable explanation regarding these results is that SARIMA cannot capture the variation characteristics of the traffic data in a small time window [48].
# Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO
## 4 Aeroengine Wear Prediction Case
### Multi-Step Prediction  
In this study, the initial space of the particle position vector was set to \(C\in[0.1,\,100]\), \(\gamma\in[0.01,\,1000]\), \(\varepsilon\in[0.01,\,100]\), and \(D\in[4,\,20]\). Other parameters were set as follows: the population size was 40, and the total iteration number was 400, \(k\)-CV was 10. The last five wear data were used as the testing data, so we were able to obtain five testing samples. Multi-step prediction was used to verify the performance of the proposed method.  
After the iterative operation of PSO, the optimization result outputted by GMPSO was (\(C\), \(\gamma\), \(\varepsilon\), \(D_{\text{best}}=\) (0.1, 13.5812, 0.001, 13). This means the wear time serial can be organized for training samples with an optimal embedding dimension \(D=\) 13, so we can obtain 70 trained samples, then the SVR with the optimal parameters (\(C\), \(\gamma\), \(\varepsilon\))\({}_{\text{best}}=\) (0.1, 13.5812, 0.001) can be trained using the 70 trained samples. Finally, we can obtain 38 support vectors--the support vector number \(n=\) 38, the weight vector \(\alpha=\) [0.1000,..., \(-\)0.0574,..., \(-\)0.0228]\({}_{38\times 1}\), and the bias \(b=\) 0.8594. Therefore, the prediction function can be constructed as follows in Equation (9):  
\[f(\mathbf{x})=\sum_{i=1}^{38}\alpha_{i}\exp\left(13.5887\cdot\|\mathbf{x}-\mathbf{x}_{i}^ {*}\|^{2}\right)+0.8594 \tag{9}\]  
where \(\mathbf{x}\) is the samples to be predicted, and \(\mathbf{x}_{i}\) is the \(i\)th support vector. Thus, the five testing samples can be brought into Equation (9). Meanwhile, the influence of randomness on the PSO is discussed in details in [20]. GMPSO and traditional PSO were operated several times, and we have verified the influence of the optimization results on the prediction accuracy. Table 3 shows the comparison of the multi-step prediction results of any two operations.  
In Table 3, \(RE\) denotes the parameter of relative errors. Table 3 proves that the optimization performance has an obvious impact on the prediction accuracy, SVR optimized by GMPSO shows higher accuracy and a more consistent output, which can be used in the application of aeroengine wear prediction. Figure 3 shows the influence of \(D\) on the average relative errors. The average relative error is still obtained through several calculations under the identical conditions. When \(D\) = 13, the average relative error is the lowest. Figure 3 thus demonstrates the necessity of optimizing the embedding dimension and the optimization capability of GMPSO.