esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and \(t\)-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland.  
ommuting time paradox; transportation and land use; mode share; travel time budget +
Footnote †: journal: Applied sciences
## 4 Problem Formulation
### Mathematical Model
#### 4.2.1 Variables  
\(C_{core}\): the capacity of each core.  
\(C_{FS}\): the capacity of each FS.  
\(\mathbf{P}_{\mathbf{r}_{k}}=\{p_{1}^{r_{k}},p_{2}^{r_{k}},\ldots,p_{K}^{r_{k}}\}\): the set of \(K\) candidate paths in ascending order of path length for each request \(r_{k}\).  
\(p\): the selected path of request \(r_{k}\), and \(p\in\mathbf{P}_{\mathbf{r}_{k}}\).  
\(c_{r_{k}}^{p_{k}l_{ij}}\): the core index selected by request \(r_{k}\) if it passes through a link \(l_{ij}\) on the selected path \(p\).  
\(f_{r_{k},start}^{p,l_{ij}c_{n}}\): the start FS index on core \(c_{n}\) occupied by the request \(r_{k}\) as it passes through a link \(l_{ij}\) of the selected path \(p\).  
\(\alpha_{r_{k}}^{p}\): the Boolean variable equals \(1\) if the path \(p\) bears the request \(r_{k}\), and \(0\) otherwise.  
\(\gamma_{r_{k}l_{ij}}^{r_{k}l_{ij}}\): the Boolean variable equals \(1\) if request \(r_{k}\) occupies the core \(c_{n}\) on a link \(l_{ij}\), and \(0\) otherwise.  
\(\delta_{r_{k}/r_{k}}^{s_{k}d_{k}l_{ij}c_{n}}\): the Boolean variable equals \(1\) if request \(r_{k}\) occupies the FS \(f_{x}\) on the core \(c_{n}\) after passing through a link \(l_{ij}\) from the source node \(s_{k}\) to the destination node \(d_{k}\), and \(0\) otherwise.  
\(\mu_{l_{ij}c_{n}}^{f_{x}}\): the Boolean variable equals \(1\) if the FS \(f_{x}\) on a core \(c_{n}\) of link \(l_{ij}\) is occupied, and \(0\) otherwise.  
\(\beta_{l_{ij}c_{n}s_{n}}^{l_{ij}c_{n}s_{n}}\): the Boolean variable equals \(1\) if request \(r_{k}\) is to be allocated the FS \(f_{x}\) on a core \(c_{n}\) of link \(l_{ij}\), while \(f_{x}\) on adjacent \(s_{n}\) to core \(c_{n}\) is occupied at the same time, and \(0\) otherwise.  
\(GF\): the number of FSs needed for guardband.  
\(XT_{T_{k}}\): the crosstalk value of a single request \(r_{k}\) in a network.  
\(N_{R}^{blocking}\): the total number of blocked connection requests in a network.  
\(N_{fs}^{XT}\): the total number of crosstalk FSs generated in a network.  
\(N_{fs}^{occupied}\): the total number of occupied FSs in a network.  
\(\Theta_{m}\): the crosstalk threshold for a given modulation format.
## 3 Results and Discussions
### Chemical Properties  
Figure 6a-c reports the FTIR spectra of the neat constituents and of the prepared blends. In particular, Figure 6b highlights the functional groups region of single bonds stretch (signals between 4000 cm\({}^{-1}\) and 2500 cm\({}^{-1}\)), while Figure 6c shows the functional groups region of double bonds stretch and fingerprint region (signals between 2000 cm\({}^{-1}\) and 650 cm\({}^{-1}\)). The spectrum of PA6 shows a signal at 3302 cm\({}^{-1}\), which can be assigned to the N-H bending vibration of the amine [56]. The signal at 1646 cm\({}^{-1}\) represents C=O amide stretching while the signal at 1551 cm\({}^{-1}\) is associated with the combination of N-H and C-H stretching [57]. Moreover, the characteristic signal of polyamides corresponding to the N-H Fermi resonance can be identified at 3086 cm\({}^{-1}\)[58]. The two signals at 2930 cm\({}^{-1}\) and at 2862 cm\({}^{-1}\) are associated with asymmetric and symmetric C\(-\)H stretching vibrations present in both polymers, respectively [59]. Considering the spectra of neat PCL, it shows a very sharp signal at 1750 cm\({}^{-1}\), which corresponds to the ester group [60]. Regarding the spectra of PA6/PCL blends, most signals of the blend constituents are present with different intensity, generally proportional to their relative concentration in the blends. However, there are no signals which cannot be associated with either the PA6 or PCL phase in the blend samples, and no peak shifts due to physical (or chemical) interaction between the constituents can be detected. This is a further confirmation of the immiscibility of the blends over the whole range of investigated compositions.  
Figure 5: FESEM micrographs of the prepared PA6/PCL blends. (**a**) PA6, (**b**) PA6_5 PCL, (**c**) PA6_10 PCL, (**d**) PA6_20 PCL, (**e**) PA6_30 PCL, (**f**) PCL.
## 5 Conclusions  
In this study, the potential NPP, actual NPP, and forage harvest NPP were employed to define the HAI index to analyze the spatiotemporal trends of human activities on terrestrial NPP in a typical grasslands ecosystem in eastern Inner Mongolia, and to further explore the relationship between the HAI and grassland degradation. The results showed that the HAI exhibited a clear decreasing trend during 2000-2017, especially from 2007 to 2015, with the annual mean values ranging from 0.75 to 0.47. The decreasing trend of the HAI indicates that the NPP loss induced by human activities is weakening, and this trend is mainly due to the difference between the potential NPP and actual NPP. Among them, the total average PNPP and ANPP were 187.2 Tg C yr\({}^{-1}\) and 152.3 Tg C yr\({}^{-1}\), respectively, during the period of 2000 to 2017, and the difference between the averaged PNPP and ANPP was 34.9 Tg C yr\({}^{-1}\), which was significantly higher than the average HNPP (8.9 Tg C yr\({}^{-1}\)).  
Based on the GDI index, the grassland degradation situation in eastern Inner Mongolia was improved from moderately degraded to lightly degraded. The non-degraded grasslands accounted for about 42.4% of the total area, significantly larger than that of lightly degraded (22.0%), moderately (28.8%), and seriously (6.8%) degraded grasslands. A positive relationship was found between the HAI and GDI, and this relationship was more significant in Xilingol League which was a typical ecologically fragile area than that in Xing'an League and Hulunbuir City.  
The following are available online at [[https://www.mdpi.com/article/10.3390/biology12010038/s1](https://www.mdpi.com/article/10.3390/biology12010038/s1)]([https://www.mdpi.com/article/10.3390/biology12010038/s1](https://www.mdpi.com/article/10.3390/biology12010038/s1)), Table S1: Proportion of the grasslands area of different degradation levels to the total area.  
Conceptualization, Q.H., F.Z. and Y.J.; methodology, Q.H., Q.Z. and F.Z.; software, X.L. ([PERSON]) and J.L.; validation, Q.H. and X.L. ([PERSON]); formal analysis, Q.H., X.L. ([PERSON]) and Q.Z.; resources, Q.H. and Y.J.; data curation, X.L. ([PERSON]), X.L. ([PERSON]) and J.L.; writing--original draft preparation, Q.H. and F.Z.; writing--review and editing, Q.H., F.Z., Q.Z., X.L. ([PERSON]) and Y.J.; visualization, Q.H.; funding acquisition, Q.H. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (Grant no. 41871334, Grant number. 42071392, and Grant number. 32230065), and the Natural Science Foundation of Nanjing Xiaozhuang University (Grant number. 2020 NXY15).  
Not applicable.  
We really appreciate the editors and anonymous reviewers for their meaningful comments for improving our manuscript.  
The authors declare no conflict of interest.
## 3 Results  
### UHPC's Workability and Entrapped Air Content  
The results of the slump flow experiments for various UHPC mixtures are presented in Figure 3. The slump flow of UHPC mixtures gradually decreases when using SF as a replacement for cement, and the reduction corresponds to the increased content of SF. This outcome also aligns with findings from other studies [43; 44; 45]. The ultrafine particle size of SF necessitates a higher water content for surface lubrication.  
Conversely, as observed in Figure 3, the slump flow of UHPC mixtures tends to increase when using RHA as a replacement for SF. It is attributed to RHA's smaller characteristic surface area compared to SF, which reduces the need to lubricate with water.  
The experimental results indicate that the utilization of SF can lead to a reduction in the entrapped air content of UHPC mixtures. When using 20% SF, the entrapped air content decreased by more than 40% compared to the control sample SF10. Similarly, when incorporating 5-10% rice hus ash (RHA), in comparison to the SF20, the entrapped air content tends to decrease. It can be attributed to the capability of SF and RHA to fill voids. The diverse combination of particle sizes among SF, RHA, cement, and quartz sand contributes to the overall compactness of the mixture. Figure 4 shows the result of the trapped air content of the UHPC mixture.  
Figure 2: Test specimens and setup (ASTM standard): (**a**). compressed sample; (**b**). tensile sample; (**c**). bending sample; (**d**). compression test; (**e**). tensile test.  
However, using RHA at 15-20% increases the entrapped air content. It is primarily due to the insufficient presence of SF to facilitate the void-filling effect, especially within micro-scale voids. This finding aligns with the research results [46]. Moreover, the elevated presence of RHA particles in the mix, particularly at higher percentages, contributes to a less effective void-filling mechanism than SF. The larger particle size of RHA, in conjunction with its limited ability to fill micro-scale voids, leads to increased entrapped air. This observation underscores the importance of carefully optimizing the proportions of RHA in UHPC mixtures to strike a balance between the benefits of void-filling and the potential drawbacks associated with entrapped air. It also further supports the notion that a nuanced approach is required when incorporating RHA into UHPC formulations to achieve optimal performance [46].
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 2 Requirement Analysis
### Role Analysis
#### 2.1.1 Equipment Users  
The users of equipment are usually some industrial enterprises, who are the most important supervision objects [37]. The safety management of an enterprise plays a vital role in preventing accidents. Every enterprise has a specific safety management plan, the most typical is that they need to record basic information about the safety status of special equipment into the supervision system regularly. Numerous analysis results based on statistical data show that safety management is the most important accident factor, and lack of commitment of safety supervision and overall safety unawareness have caused slackness in safety management of equipment users, which has resulted in an exacerbated accident rate [17].
## 5 Conclusions  
To investigate the influence of different ore fragment sizes on release and flow characteristics during block caving draw, this study employed ore draw experiments and PFC3D numerical simulations. The evolution of the shape and cross-sectional size of the extraction zone from a single drawbell under varying particle gradations was analyzed. Furthermore, numerical simulations were utilized to examine differences in ore flow behavior across these gradations. The principal conclusions are as follows:  
1. Based on in situ fragmentation testing, three distinct particle gradations were established. From Gradation I to Gradation III, the average ore fragment size increased progressively. Experimental draw tests for these three gradations demonstrate that the extraction zone exhibits an ellipsoidal shape in the vertical direction, with elliptical cross-sections at different heights.
2. The semi-major and semi-minor axis dimensions of the extraction zone cross-section increase with the draw height, stabilizing when a draw height of 80 cm is reached. The ore fragment size significantly impacts the size of the extraction zone. Gradation I, with the smallest average particle size, yields the largest extraction zone. Conversely, Gradation III, characterized by the largest average particle size, produces the smallest extraction zone.
3. The numerical simulation results show excellent agreement with the experimental findings. Flow characteristics of ore particles under different gradations were evaluated using multiple monitoring points within the model. The results indicate that Gradation I exhibits the best particle flowability, attributable to its smallest average particle size. As the particle size increases, the spatial extent of the movement surface derived from monitoring points diminishes. This signifies that smaller particle sizes correspond to larger flow ranges within different regions of the model.
## 2 Related Technologies
### Convolutional Autoencoder  
The neural network architecture that simplifies and reproduces data through convolution operations is called the convolutional autoencoder [30], including the convolutional encoding part responsible for compressing the data to a low-dimensional space and the convolutional decoding part responsible for restoring these data to the original state. This model is particularly good at capturing the spatial layout and characteristics of the data, making it very suitable for processing high-dimensional content such as images and videos. In terms of specific applications, convolutional autoencoders perform well in tasks such as image denoising, image creation, and feature extraction.
## 3 Multi-Node Collaborative Compound Jamming Recognition Based on Time-Frequency Graph and MAML  
### Basic Principles of MAML  
Model-Agnostic Meta-Learning (MAML) is an optimization-based algorithm [17]. It can update model parameters iteratively through related tasks so that the training model can quickly adapt to new tasks. The MAML dataset includes a training set and test set; the training set and test set can be divided into the support set and query set. The dataset categories and corresponding functions in the MAML algorithm are shown in Figure 4.  
The core idea of MAML is to train a set of highly generalized initialization parameters, so that only a small amount of data would be required to complete one or more steps of gradient adjustment for a new task. As shown in Figure 5, the objective of MAML is to find a set of initialization model parameters \(\theta\) that is sensitive to task changes; consequently, when the parameters start to change along with the gradient of the loss function \(\mathcal{L}\), a parameter \(\theta\) that is also sensitive to new tasks will be generated.  
As shown in Figure 6, there are two parameter update processes in MAML, namely two gradient descent processes. One is the parameter update that takes place during each task. The other is the parameter update after each set of tasks is completed.  
Figure 4: MAML datasets and corresponding functions.  
Figure 5: Diagram of MAML adapting to a new task.  
Figure 3: Time–frequency diagram of four compound jamming signals.  
Assuming that the task set \(T_{X}\) has a total of \(S_{n}\) group tasks, for the \(y\)th task of the group \(v\) task, its update formula is as follows:  
\[\hat{\theta}_{v}^{y}=\theta_{v}^{y}-\alpha\
abla_{\theta_{v}^{y}}\mathcal{L}^{ y}\Big{(}f_{\theta_{v}^{y}}^{y}\Big{)} \tag{3}\]  
where \(\alpha\) represents the learning rate of the first gradient descent. When the group \(v\) task training is completed, the second parameter update will be carried out. Its update formula is as follows:  
\[\theta_{v+1}=\theta_{v}-\lambda\
abla_{\theta_{v}}\mathcal{L}(f_ {\theta_{v}}) \tag{4}\] \[\mathcal{L}(f_{\theta_{v}})=\sum_{y=1}^{S_{n}}\mathcal{L}^{y} \Big{(}f_{\theta_{v}^{y}}^{y}\Big{)} \tag{5}\]  
where \(\lambda\) represents the learning rate of the second gradient descent, and \(S_{v}\) represents the total number of tasks in the \(v\)th sample group.
## 3 Proposed Methodology
### Description of the Proposed Approach  
In the Optimized Link State Routing (OLSR) protocol, \(\text{MPR}(S)\), \(N(S)\), and \(N2(S)\) represent the MPR, 1-hop neighbors, and 2-hop neighbors of node 5, respectively. These sets are calculated following the standard OLSR protocol. The mobile ad hoc network (MANET) is modeled as a directed graph \(G(U,E)\), where \(U\) is the set of nodes and \(E\) is the set of links. A link \(l=(i,j)\) exists if node \(j\) is within the transmission range of node \(i\).  
At each time interval \(\Delta T\), the changes in the \(x\)- and \(y\)-coordinates, \(\Delta xT\) and \(\Delta yT\), of a node can be computed as follows:  
\[\Delta xT_{i} = (x_{ti}-x_{Ti})\] \[\Delta yT_{i} = (y_{ti}-y_{Ti})\]  
\begin{table}
\begin{tabular}{l l l} \hline
**Terminology** & **Description** & **Unit of Measure** \\ \hline \(D\) & Distance & [m] \\ \hline \(S\) & Node’s speed & [m/s] \\ \hline \(\theta\) & Node’s direction & [\({}^{\circ}\)] \\ \hline \(V\) & Velocity & [m/s] \\ \hline \(A\) & Acceleration & [m/s\({}^{2}\)] \\ \hline \(\Delta T\) & Time interval & [s] \\ \hline \(t\) & Current time & [s] \\ \hline \(RS(i,j)\) & Relative speed & - \\ \hline \(RA(i,j)\) & Relative acceleration & - \\ \hline \(RD(i,j)\) & Relative direction & - \\ \hline \(SD\) & Spatial dependency & - \\ \hline \(E(i,j)\) & Residual energy & - \\ \hline \(EARE\) & Average residual energy & - \\ \hline \(LE\) & Energy level & - \\ \hline \(PS\) & Packets sent & - \\ \hline \(PR\) & Packets received & - \\ \hline \(TM\) & Trust measure & - \\ \hline \(MCWMPR\) & Multicriteria Weighted MPR & - \\ \hline \end{tabular}
\end{table}
Table 2: Terminology of the improvement.  
where \(t\) is the current time, and \(x_{it},y_{ti}\), \(x_{Ti}\), and \(y_{Ti}\) are the spatial coordinates of node \(i\) at times \(t\) and \(T\).  
The Euclidean distance \(D\) between the current and previous positions of a node is calculated as  
\[D=\sqrt{(\Delta xT)^{2}+(\Delta yT)^{2}}=\sqrt{(x_{ti}-x_{Ti})^{2}+(y_{ti}-y_{Ti })^{2}}\]  
Consequently, the speed \(S\) of the node over the time interval \(\Delta T\) is determined by  
\[S=\frac{D}{\Delta T}=\frac{\sqrt{(x_{ti}-x_{Ti})^{2}+(y_{ti}-y_{Ti})^{2}}}{T-t}\]  
This calculation helps in determining the mobility pattern of the nodes, which plays a critical role in the proposed multicriteria MPR selection approach.
## 5 Results and Discussion
### Intra-Cluster Communication
#### 5.1.3 Throughput to the BS  
The effectiveness of a heterogeneous WSN-based IoT system is evaluated by the amount of data collected and delivered to the base station. Figure 15 compares the throughput achieved in various cluster-head rotation schemes, for clusters with both three and five nodes.  
The results in Figure 15a show that the proposed cluster-head rotation method significantly outperforms both the fixed cluster-head and traditional cluster-head rotation schemes. Specifically, it delivers a 50% increase in throughput compared to the fixed cluster-head scheme and a 7.1% increase over the traditional cluster-head rotation method when \(n=3\). For \(n=5\), the proposed method achieves an even more substantial improvement, yielding a 125% and 13% increase in throughput compared to the fixed and traditional rotation methods, respectively as shown in Figure 16.  
Figure 14: Average energy consumption of nodes within a cluster; (**a**) Number of nodes \(n=3\); (**b**) Number of nodes \(n=5\).  
Figure 15: Performance comparison in terms of throughput to BS against energy consumed; (**a**) Number of nodes \(n=3\); (**b**) Number of nodes \(n=5\).  
These results clearly demonstrate the superiority of the proposed cluster-head rotation scheme in terms of data transmission efficiency to the base station.
## 2 Materials and Methods
### CT Image Segmentation  
Lung segmentation is a critical step in CT scan analysis, focusing on identifying and analyzing the specific region of interest within the lungs. By eliminating surrounding tissue, this technique enhances analysis accuracy and efficiency, as illustrated in Figure 2. However, it is crucial to avoid losing any part of the ROI, such as lung nodules near the thoracic wall and structures such as bronchi within the lung area, during segmentation [18]. In CT scans, the lung area typically appears darker than neighboring sections, while brighter regions within the lungs may indicate air, blood vessels, or pathologically altered tissue. The following steps are involved in lung segmentation [19]:  
* Conversion of the image with RGB channels into a grayscale image;
* Binarization of the CT slice using a user-defined threshold value;
* Removal of objects outside the ROI but still connected to the image border;
* Labeling of regions within the image;
* Measurement of the properties of labeled image regions and elimination of everything except for the two largest entities (right and left lung);
* Conducting binary erosion with a disk of a chosen radius to separate the lung nodules attached to the blood vessels from surrounding tissue;
* Removal of blood vessels from the image;
* Filling in the holes created during the previous operation while preserving the nodules attached to the pleura;
* Detection of lung edges using the Roberts operator to estimate and highlight the spatial gradient zones in the image;
* Filling in any remaining holes within the ROI;
* Superimposing both binary and infected lung masks on the original chest CT scan.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset Used**} & \multicolumn{3}{c}{**Number of Patients**} \\ \cline{2-4}  & **COVID-19-Infected** & **Healthy** & **CAP** \\ \hline COVID-19 Lung CT Scans & 216 & 133 & \\ Large COVID-19 CT scan slice dataset & 466 & 604 & 60 \\ COVID-19 CT scans & 20 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the contents of the databases used for model fitting and subsequent training.