esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study presents a hydration model for a binary mixture of cement and chemically inert quartz filler. The model distinguishes between the dilution and nucleation effects of quartz filler, clarifying how different quartz filler contents (10% and 20%) and water-to-(cement + quartz) ratios (0.5 and 0.2) influence the cement hydration reaction rate.  
Model Development and Validation: First, the model parameters for cement hydration and the impact of quartz filler were determined through regression analysis, using the accumulated hydration heat data of a cement slurry with a water-to-(cement + quartz) ratio of 0.5 over the first 3 days. The model was then used to calculate the production of chemically combined water and portlandite from mixing for up to 28 days. The predicted results aligned well with the experimental data. Next, hydration product measurements, including chemically combined water and portlandite, were used to determine the water availability coefficient for cement slurry with a water-to-(cement + quartz) ratio of 0.2. As the water-to-(cement + quartz) ratio decreased, this coefficient exceeded 1, leading to a decline in the hydration rate. Incorporating the water availability coefficient into the model significantly improved prediction accuracy, bringing the results into close agreement with the experimental data. Without this coefficient, the predicted trends remained similar, but the values were generally higher than the measured values.  
Strength Prediction: A linear equation was developed to predict strength development based on the mix ratio and reaction level. This equation applied to specimens with varying water-to-(cement + quartz) ratios (0.5 and 0.2), quartz filler contents (0%, 10%, and 20%), and curing ages (1, 3, 7, and 28 days). When the 1-day strength data for concrete with a water-to-(cement + quartz) ratio of 0.2 were excluded, the correlation coefficient between the predicted and tested values was 0.91. However, including the 1-day data reduced this coefficient to 0.77; this was likely due to the model not fully accounting for the influence of superplasticizers on early hydration rates.  
CO\({}_{2}\) Emission Analysis: The CO\({}_{2}\) emission analysis revealed that for concrete with a water/binder ratio of 0.5, the CO\({}_{2}\) emissions per unit strength remained relatively unchanged as the quartz powder content increased. However, for concrete with a water/binder ratio of 0.2, the CO\({}_{2}\) emissions per unit strength decreased significantly with the higher quartz powder content. This is because the dilution effect of quartz powder enhances both hydration and strength at a lower water/binder ratio, leading to lower CO\({}_{2}\) emissions per unit strength.  
Final remarks: In summary, the proposed hydration model is straightforward, adaptable to various mix ratios, and capable of predicting key properties, such as hydration heat, hydration product content, and strength development. These findings have important engineering implications for the optimization of the use of chemically inert quartz filler in cementitious materials.
## 4 QoS Provisioning Techniques for Future FiWi Networks
### FiWi Architectures
the WMN and perform route computation taking the state of wireless links and average traffic rates into account. When the traffic demands surpass the available PON capacity, some of the TDM PONs may be upgraded to WDM PONs. If some PONs are heavily loaded and others have less traffic, some heavy-loaded ONUs may be assigned to a lightly-loaded PON by tuning their optical transceivers to the wavelength assigned to the lightly-loaded PON. In [2], the proposed SuperMAN architecture leverages on the similarities of EPON and WiMAX in that the two technologies are more likely to target the same network segment rather than being cascaded to cover different network segments. In other words, SuperMAN provides the network operators with the ability to make a choice between EPON and WiMAX depending on a number of factors, e.g., right-of-way and techno-economic justification [47]. It seems somewhat impractical to deploy a metropolitan-reach wireless technology such as WiMAX for realizing wireless drop lines of rather short length to or inside offices and homes. Instead, using next-generation low-cost WiFi technologies in conjunction with WDM-enhanced EPON access networks while integrating WiMAX with RPR appears to be a more promising approach. Fig. 6 depicts the network architecture of SuperMAN [2]. It builds on an all-optically integrated Ethernet-based access-metro network, described at length in [48], extended by optical-wireless interfaces with next-generation WiFi and WiMAX networks. More specifically, the optical part of SuperMAN consists of an RPR metro network that interconnects multiple WDM EPON access networks attached to a subset of RPR nodes. Each of the attached WDM EPONs has a tree topology with the OLT at the root tree being collocated with one of the COs. No particular WDM architecture is imposed on the  
Figure 5: Unidirectional ring/PON architecture integrated with a WiFi-based WMN [46]. ONUs, thus allowing the decision to be dictated by economics, state-of-the-art transceiver manufacturing technology, traffic demands, and service provider preferences. The recommended WDM extensions to the IEEE 802.3 ah MPCP, described in greater detail in [49], guarantee backward compatibility with legacy TDM EPONs and enable the OLT to schedule transmissions to and receptions from ONUs on any supported wavelength channel. The optical access-metro network lets low-cost PON technologies follow low-cost Ethernet technologies from access networks into metro networks by interconnecting the \(P\) collocated OLTs/COs with a passive optical star subnetwork whose hub consists of an athermal wavelength-routing \(P\times P\) arrayed waveguide grating (AWG) in parallel with a wavelength-broadcasting \(P\times P\) passive star coupler (PSC). The aforementioned optical part of SuperMAN interfaces with next-generation WiFi and WiMAX networks, as shown in Fig. 6.
## 3 System Models
### Network Model
Therefore, the computation can be implemented as  
\[t_{i}^{cmpt-time}=\mu\log(\frac{1}{\Theta})\frac{D_{i}\big{|}S_{i}=(x_{k},y_{k})_{k =1}^{|S_{i}|}\big{|}}{f_{n}}. \tag{6}\]  
Similarly, the computational energy cost is calculated as follows:  
\[e_{i}^{cmpt-cost}=Q(\Theta)\frac{\alpha_{i}}{2}f_{n}2D_{i}|S_{i}|, \tag{7}\]  
\[e_{i}^{cmpt-cost}=\mu\log(\frac{1}{\Theta})\frac{\alpha_{i}}{2}f_{n}2D_{i} \big{|}\big{|}S_{i}=(x_{k},y_{k})_{k=1}^{|S_{i}|}\big{|}. \tag{8}\]  
When all the \(L\) ILC devices complete their local iterations, each device \(L_{i}\) uploads the model parameters \(\omega\) of the ILC to the associated intermediate local client \(M_{j}\). The intermediate \(M_{j}\) LC receives the updates from a connected set of active \(A_{i}\) devices and performs the aggregation process as follows:  
\[\overline{\omega_{i}}=\frac{\sum i\epsilon A_{Ai}|S_{i}|\omega_{i}^{c}}{ \big{|}S_{A_{i}}\big{|}}, \tag{9}\]  
where the aggregated dataset of \(A_{i}\) connected to the \(M_{j}\) intermediate LC is  
\[Q(\Theta)=\mu\log(\frac{1}{\Theta}). \tag{2}\]0  
The intermediate LC \(M_{j}\) multicasts \(\overline{\omega_{i}}\) to \(A_{i}\) for the computation process in the next communication round \(R\). In particular, \(M_{j}\) repeats the process and guides the ILC devices iteratively to compute the model for better accuracy. The aggregation process of \(M_{j}\) continues until it reaches the accuracy level of all the other LC devices connected to their \(M\). The required model accuracy of the general convex machine learning tasks and the overall number of iterations on \(M_{j}\) are  
\[Q(\Theta)=\mu\log(\frac{1}{\Theta}). \tag{2}\]1  
where \(\delta\) is a predefined constant of the learning tasks. [PERSON] shows that the intermediate LCs access only the parameters instead of the actual data of the end devices, which significantly improves the security and privacy of personal data. After \(I(\epsilon,\Theta)\) iterations over \(M_{j}\) devices, the total energy cost of dealing with \(A_{i}\) ILC can be computed as  
\[Q(\Theta)=\mu\log(\frac{1}{\Theta}). \tag{2}\]2  
Likewise, the computation and communication delay for \(M_{j}\) can be calculated as  
\[Q(\Theta)=\mu\log(\frac{1}{\Theta}). \tag{2}\]3  
In the last step, the cloud server gathers all the updated models received from the edge servers as follows:  
\[Q(\Theta)=\mu\log(\frac{1}{\Theta}). \tag{2}\]4  
where the aggregated dataset of \(M_{j}\) connected to the global cloud server \(G\) is  
\[Q(\Theta)=\mu\log(\frac{1}{\Theta}). \tag{2}\]5Therefore, we may derive the system-wide energy and latency under a single global iteration, disregarding the cloud's substantially shorter aggregation time than that of mobile devices, as shown below. \[Q(\Theta)=\mu\log(\frac{1}{\Theta}). \tag{2}\]6  
\[Q(\Theta)=\mu\log(\frac{1}{\Theta}). \tag{2}\]7  
To meet the convergence conditions, the local and global-level aggregation can be repeated consistently to maximize the accuracy and reduce the loss.
## 1 Introduction
In this paper, a time-dependent green vehicle routing problem with soft time windows (TDGVRPSTW) for fresh products distribution is established. The TDGVRPSTW problem is an extension of the traditional GVRP model, which adds time-dependent road network conditions and soft time windows. Time-dependent means that the speed of a vehicle on each road section is related to its time period, and the speed is different in different time periods. Adding time-dependent conditions to the model can make the model closer to reality. The soft time windows are relative to the traditional hard time windows. In the soft time windows model, the time when the vehicle arrives at the customer is not strictly limited. The vehicle can start service at any time when it arrives at the customer, but it needs to pay a penalty cost to start service outside the service time windows. A hybrid algorithm combining the genetic algorithm and variable neighborhood descent algorithm is used to solve the model. It also analyzes the relationship between distribution economic cost and environmental cost, hoping to provide fresh products and a decision-making reference for distribution. The innovations and contributions of this paper are described as follows. 1. A hybrid algorithm combining adaptive genetic algorithm and neighborhood search algorithm is designed, which considers both the search breadth and the search depth. The chromosomes in the population are disturbed by the crossover and mutation operation of the genetic algorithm, and the excellent chromosomes in the population are deeply searched by the neighborhood search algorithm. 2. Different fresh agricultural products have different perishability. Does the difference in perishability of fresh agricultural products have an impact on driving routes and customer assignment schemes? This paper will clarify the problem through experiments. 3. In order to improve the quality and diversity of the initial population, three different methods were used to generate the initial population in this paper. The three methods are, respectively, the CW saving algorithm, nearest neighbor insertion algorithm, and random method. The remainder of this paper is organized as follows. In Section 2, we give a detailed description of the TDGVRPSTW model formulated in this paper. Section 3 presents the proposed variable neighborhood adaptive genetic algorithm. Experimental results and analyses are given in Section 4. Finally, conclusions are given in Section 5.
## 3 Mathematical Modeling
### Stackelberg Game Model  
Stackelberg game, also known as master-slave game, is a non-cooperative dynamic game, and the status of the players is different. One is the leader, who makes the decision first, and the other is the follower, who needs to make the optimal response to the leader's decision. The optimal response strategy of the follower is predictable to the leader, therefore, the leader will consider the possible response of the follower and make the decision which is most beneficial to his own interests. The cooperative relationship between RE company and BSS described in the previous section constitutes a typical Stackelberg game.  
The RE company and BSS cannot directly control each other's decisions, but can indirectly affect the other party's payoff through their own strategies and ultimately affect each other's strategy. For example, on the one hand, the RE company sets charge/discharge price to guide the charging and discharging behavior of the BSS, on the other hand, the charging and discharging strategies of the BSS can affect the revenue of the RE company, and that will prompt RE company to adjust his charge/discharge price accordingly. Therefore, the pursuit of maximizing their respective interests can be modeled as a Stackelberg game model.  
The RE company and BSS do not make the decision at the same time. In fact, the RE company, as the leader, sets the charge/discharge price first, and its payoff function is to maximize its own benefits. In order to maximize its own revenue, as the follower, the strategy of BSS is to optimize the charging and discharging power according to the charge/discharge price set by the RE company. In addition, since the RE company can predict the possible optimal response strategy of BSS, the strategy of the RE company will be revised dynamically accordingly to maximize its own payoff.  
According to the above analysis, the Stackelberg game model of the RE company and BSS can be modeled as Equation (14).  
\[\begin{split}\text{Leader}\left\{\begin{array}{c}\max\sum \limits_{t=1}^{N_{t}}{(P_{\rm c}^{t}-P_{\rm d}^{t})}\rho^{t}\Delta t-\sum \limits_{t=1}^{N_{t}}{(P_{\rm c}^{t}-P_{\rm d}^{t})}\rho_{RE}^{t}\Delta t\\ \text{s.t. }(3)\,\sim(4)\end{array}\right.\\ \text{Follower}\left\{\begin{array}{c}\left\{P_{\rm c}^{t},P_{\rm d}^{t} \right\}\in\arg\!\max\limits_{t=1}^{N_{t}}{(P_{\rm d}^{t}-P_{\rm c}^{t})}\rho^ {t}\Delta t\\ \text{s.t. }(7)\,\sim(13)\end{array}\right.\end{split} \tag{14}\]  
As can be seen from Equation (14), the follower's game model acts as a constraint of the leader's game model.
# Predictive Analysis for Optimizing Port Operations
###### Abstract  
Maritime transport is a pivotal logistics mode for the long-distance and bulk transportation of goods. However, the intricate planning involved in this mode is often hindered by uncertainties, including weather conditions, cargo diversity, and port dynamics, leading to increased costs. Consequently, accurate estimation of the total (stay) time of the vessel and any delays at the port are essential for efficient planning and scheduling of port operations. This study aims to develop predictive analytics to address the shortcomings in the previous works of port operations for a vessel's Stay Time and Delay Time, offering a valuable contribution to the field of maritime logistics. The proposed solution is designed to assist decision-making in port environments and predict service delays. This is demonstrated through a case study on Brazil's ports, where the best performance is observed for tree-based methods. Additionally, feature analysis is used to understand and interpret key factors impacting maritime logistics, enhancing the overall understanding of the complexities involved in port operations.  
port analysis; supervised learning; feature importance; SHAP; supply chain; maritime logistics Article
## 3 Results
### EFAST Results  
Considering that the EFAST analysis method improved and was developed from FAST, the sampling method and sample number of the EFAST were the same as those of FAST. After calculation with the EFSAT analysis method, the first-order sensitivity (\(S\)) and total sensitivity (\(S_{T}\)) of the SWAT model parameters are shown in Figures 10 and 11. Among all the selected parameters, the first-order sensitivity (\(S\)) of parameter SMTMP was 0.5333, which was much greater than that of other parameters, indicating that SMTMP was a significantly sensitive parameter. In addition to SMTMP, the first-order sensitivity (\(S\)) levels of parameters SFTMP, SOL_BD, RCHRG_DP, SOL_K, HRU_SLP, SOL_Z and SOL_AWC were also high, at 0.0713, 0.0361, 0.0326, 0.0265, 0.0241, 0.0237, and 0.0224, respectively. Calculations show that they were also sensitive parameters that played a non-negligible role and influenced the model results.  
Figure 12 shows that the total sensitivity (\(S_{T}\)) of parameter SMTMP was as high as 0.3577, indicating that the interaction between SMTMP and other SWAT model parameters significantly impacted the model output. The total sensitivity of other parameters RCHRG_DP, SOL_AWC, SFTMP, and SOL_BD was 0.1372, 0.1359, 0.1196, and 0.1140, respectively, which also indicates that the interaction of parameters influenced the model results.  
Figure 11: First-order sensitivity (\(S\)) of each parameter of EFAST.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 5 Experimental Results
### Results on iLIDS-VID Dataset
#### 5.1.2 Score-Level Fusion
These results show the benefits of threshold-based conditional fusion in person Re-ID tasks. The integration of gait features with appearance data proved to be a key factor in improving the performance of diverse backbone architectures, thereby enhancing their effectiveness in complex re-identification scenarios.
# Improvement of Surface Roughness and Cracking Study of Nb\({}_{3}\)Sn SRF Films
## 4 Study on Strain Limits of Nb\({}_{3}\)Sn Film Coated by Tin Vapor Diffusion Method
### Strain Limit of Curved Nb\({}_{3}\)Sn Film Samples  
To verify the reliability of the safe strain limits, we carried out reproductive experiments on curved Nb\({}_{3}\)Sn film samples. Four niobium samples were fabricated using the same niobium material used in previous experiments. According to the shape of 1.3 GHz Tesla cavity, which is shown in Figure 13a, the radiuses of samples are 38.0 mm and 17.0 mm, which is close to R\({}_{\rm c}\) and vertical half-axis b. The thicknesses of these samples are approximately 2.8 mm.  
Figure 12: SEM picture of cracks on samples with 0.99% and 2.54% tense residual strains (**a**,**b**); transversal and longitudinal cracks on samples with large residual strains (**c**,**d**).  
Figure 13: (**a**) Schematic diagram of a 1.3 GHz Tesla-type superconducting cavity; (**b**) curved samples after BEP.  
The samples received heavy BCP and light BEP, and they were subsequently subjected to the same procedures with that of flat samples. Figure 13b displayed the photos of those samples, and Figure 14 shows the test platform and the cut sample to be tested. Simulations were first carried out based on the actual dimensions of the samples after coating. We calculated the displacements of the end of the sample to produce 1.1% and 0.9% effective strain at the midpoint of the sample. Figure 15 gives the calculation results in multiphysics simulation software. The 3D distribution shows that only the residual strain at the center region reaches 1.1%, and it gets lower at two ends. Finally, tensile tests were carried out, and the center parts of these samples were cut out and measured by SEM. The SEM results show that short cracks only occurred on the area with 1.1% effective strain and did not occur on the other area with a lower effective strain, which verified the previous experiments and simulations well. The SEM pictures of the normal region and crack region on one sample are shown in Figure 16. Briefly, we determined a safe strain range of (\(-2.3\%\), 0.9%).
## 3 Results and Discussion
### Source Identification of Heavy Metals
#### 3.3.3 Cluster Analysis  
Z-score was used to standardize the heavy metal content data, and [PERSON]'s clustering algorithm was used to cluster analysis the nine kinds of heavy metals (Figure 4). The nine heavy metals were divided into three clusters: cluster 1 was Cd-Cu-Pb-Zn, cluster 2 was Cr-Ni, and cluster 3 was Co-V-Mn. However, there was still a moderate correlation between the clusters. Cd-Cu-Pb-Zn cluster and Cr-Ni cluster were connected, indicating that their sources may be the same. The maximum content of Cd-Cu-Zn and Cr-Ni appeared in CND, while CND was in the stage of infrastructure construction with higher traffic flow, construction, and demolition activities [62; 63; 64].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Elements**} & \multicolumn{8}{c}{**Correlation Coefficient**} \\ \cline{2-9}  & **Cd** & **Co** & **Cr** & **Cu** & **Mn** & **Ni** & **V** & **Pb** & **Zn** \\ \hline Cd & 1 & & & & & & & & \\ Co & 0.059 & 1 & & & & & & & \\ Cr & 0.245 & 0.53 ** & 1 & & & & & & \\ Cu & 0.558 ** & 0.177 & 0.312 ** & 1 & & & & & \\ Mn & 0.019 & 0.576 ** & 0.431 ** & 0.265 & 1 & & & & \\ Ni & 0.534 ** & 0.303 * & 0.535 ** & 0.65 ** & 0.236 & 1 & & & \\ V & \(-\)0.014 & 0.623 ** & 0.126 & 0.349 * & 0.617 ** & 0.08 & 1 & & \\ Pb & 0.619 ** & 0.008 & 0.159 & 0.728 ** & 0.019 & 0.477 ** & 0.122 & 1 & \\ Zn & 0.379 ** & 0.003 & 0.225 & 0.639 ** & 0 & 0.472 ** & 0.051 & 0.662 ** & 1 \\ \hline \hline \end{tabular}  
* Reflects a significance level of 0.01.
* Significance level of 0.05.  
\end{table}
Table 3: Correlation coefficients for heavy metals in road dust in Yangxin County.
## 2 Materials and Methods
### Methods
#### 2.4.2 Radar Precipitation Products Processing  
Generally, spatially distributed grid-based rainfall data are represented as images in a specific coordinate system and file format. The RAR data utilize the Lambert Conformal Conic projection as its projected coordinate system, while the IMERG data use the WGS84 geographic coordinate system. Therefore, for the purpose of HEC-HMS hydrological modeling, several processing steps are required, including (1) data format conversion, (2) geo-referencing for coordinate system transformation, and (3) DSS file generation. In this study, a total of 2 or 3 data processing steps were performed for each image file. NCL (NCAR Command Language) and Python-based script programs were developed and employed for this purpose.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{4}{c}{**Storm Events**} & \multicolumn{6}{c}{**Rain Gauge Precipitation Total (mm)**} & \multicolumn{6}{c}{**Discharge**} \\ \hline \# & **Period** & **GB** & **JG** & **CC** & **SJ** & **BG** & **JC** & **AC** & **Areal** & **Peak** & **Total** \\ \hline
1 & 17 August 2014 & 13:00–2014.08.20 12:00 & 97 & 114 & 128 & 100 & 121 & 100 & 101 & 108.3 & 1339.9 & 74.2 \\
2 & 24 August 2014 & 01:00–2014.08.28 24:00 & 90 & 71 & 64 & 106 & 57 & 87 & 101 & 82.2 & 906.0 & 67.6 \\
3 & 8 August 2015 & 13:00–2015.07 11 2:00 & 51 & 37 & 47 & 77 & 59 & 61 & 69 & 57.3 & 269.7 & 21.7 \\
4 & 1 July 2016 & 13:00–2016.07.03 12:00 & 119 & 128 & 153 & 125 & 101 & 124 & 115 & 122.9 & 729.7 & 31.9 \\
5 & 16 September 2016 & 13:00–2016.09 19 24:00 & 147 & 129 & 158 & 148 & 134 & 145 & 162 & 146.1 & 517.3 & 38.3 \\
6 & 10 September 2017 & 01:00–2017.09.12 24:00 & 69 & 77 & 78 & 73 & 73 & 57 & 78 & 71.8 & 341.4 & 29.7 \\
7 & 25 August 2018 & 13:00–2018.08.03 12:00 & 245 & 286 & 331 & 316 & 288 & 269 & 285 & 286.3 & 2192.9 & 168.5 \\
8 & 30 August 2018 & 13:00–2018.09.02 12:00 & 84 & 110 & 98 & 84 & 69 & 100 & 61 & 86.0 & 1127.8 & 68.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Storm events (eight independent events) selected for study area, showing total precipitation and discharge (historical hourly maximum flow; 5519.2 m\({}^{3}\)/s) amounts.  
Table 5 outlines the overall data processing steps for spatially distributed grid-based rainfall data, specifically focusing on the procedures for RAR and IMERG datasets. The RAR dataset adopts the \"Albers Equal-Area\" map projection, while the IMERG dataset utilizes the \"ITRF2000\" coordinate system, both as the Standard Hydrologic Grid (SHG) for the HEC-HMS model. In the final stage of data processing, the \"asc2 dssGrid.exe\" file provided by the USACE's HEC-GridUtil is utilized to extract \".dss\" files, which are directly used as precipitation input data for the HEC-HMS model.