esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results--Systematic Literature Review  
### Descriptive Statistics  
Figure 1 represents the distribution of papers by year.  
Over the three-year period (2021-2023), the number of published articles grew steadily. Articles published in 2022 more than doubled those from 2021 and five more articles were published in 2023 compared to 2022. This increase was somehow expected, as scientific studies usually take about a year to be published, and thus, studies on COVID-19 probably started being conducted in 2020-2021 but needed time to find their way into publication in 2022 and 2023. This is why more scientific data and information became available in the last years, allowing for more complete research. However, it is important to note that trends in publishing are not always on the rise (at least, not in any scientific discipline); rather, they can be affected by how relevant the topic is. Based on the above, the large number of publications shows that studying COVID-19 impact on logistics and SCs remains a focal point for scholarly questions, and that even if we are now in the post-COVID-19 era, research in this scientific area is far from declining.  
Figure 1: Distribution of paper by year.  
The distribution of articles across scientific sources was analyzed (Table 1). We recall that no filters were set for the journal classification, and therefore, the ranking in Table 1 was obtained based on the number of papers published by each journal, as they resulted from the query. For completeness, a well-known metric of the journal prestige, i.e., the latest available positioning in the Scimago (www.scimagojr.com (accessed on 6 June 2024)) ranking, is also included in the table. Sustainability emerges as the top journal in terms of number of publications, with 10 papers, followed by International Journal of Logistics Management, which is in line with the focus of the query on logistic and SC processes. Following are two journals with five publications, Journal of Global Operations and Strategic Sourcing and Agricultural Systems. The first journal is relevant to the logistics area, like the previous one; the second focuses instead on agriculture, a topic of great current relevance and which is discussed further below. Benchmarking, with four papers, is aimed at analyzing performance and identifying the best practices. Finally, International Journal of Production Research, with three publications, publishes papers related to manufacturing, industrial engineering, operations research and management science, with a specific focus on logistics processes. In general, the journals that emerged as top by number of papers published also have a high Scimago ranking.
# One-Dimensional Modeling of the Pressure Loss in Concrete Pumping and Experimental Verification
###### Abstract  
An accurate formula for calculating the pressure loss in concrete pumping plays a significant guiding role in the design and service process of pump trucks. Based on the flow characteristics of concrete pumping, a straight pipe one-dimensional model for the pressure loss is developed, in which both the viscous force of the mortar in the lubrication layer and the blocking effect of coarse aggregate particles are considered. First, the complex geometrical shapes of the aggregate particles are geometrically reconstructed by using a HandySCAN noncontact scanner and the reverse modeling software Geomagic Design X (v.19.0.2). Then, the equivalent spherical size of nonspherical aggregate particles is calculated according to the equal hydraulic radius principle. The blocking effect of the aggregate particles is converted into the wall roughness. Finally, an explicit expression for the pressure loss in concrete pumping is deduced by using [PERSON]'s equation, [PERSON]'s equation, and [PERSON]'s formula, and the calculated value is compared with the measured value at a corresponding experimental site. The results indicate that the pressure loss values calculated with the one-dimensional flow model are closer to the actual pumping pressure loss values. The relative error between the results and the actual pumping pressure loss value is about 20.2%.  
concrete pumping; pressure loss; one-dimensional flow model; equivalent particle size Article
## 4 Results and Discussion
### Fuel Economy Benefits  
Under low load condition, the PMEP is large for throttled engines due to lower intake pressure. Based on theEMIV, the EIVC strategy is adopted to control the amount of gas charge. Due to unthrottling, the PMEP is significantly reduced under low and medium load operations, as shown in Figure 20. When the engine works in VCD operations with the EIVC strategy, the PMEP of the firing cycle would be reduced further.  
Through the above analysis, the optimal area of the IVO and IVC can be obtained as the engine works under VCD operations. Figure 21 shows the PMEPs of the prototype engine, the EIVC strategy and VCD with EIVC in the same operating conditions. Due to the higher IMEP of the firing cycle, the PMEP of the VCD with EIVC is further reduced when compared to the EIVC strategy. However, the inactive cycle under VCD operation would consume energy, which reduces the improvement of fuel economy achieved by the VCD. Through the optimization of the IVO and IVC, the minimum total energy losses of the inactive cylinder during the transition process have been obtained.  
Figure 21: The PMEPs of the prototype engine, EIVC strategy and VCD with EIVC.  
Figure 20: The PMEP of the prototype engine and the EIVC strategy.  
Finally, the fuel economy achieved by the VCD operations based on the EMIV with the EIVC strategy is illustrated in Figure 22. When the BMEP is lower than 0.3 MPa, the fuel economy achieved by the VCD strategy is better than that achieved by the EIVC strategy. At 1200 and 1600 rpm, the VCD strategy provides a 12.5-16.6% and 9.7-14.6% reduction in BSFC, respectively, compared with the prototype engine when the BMEP ranges from 0.3 MPa to 0.2 MPa.
# Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering
## 3 Experimental Results and Analysis
### Real-Time Evaluation  
In this section, four algorithms, SIFT, SURF, BRISK, and ORB, are compared; 1000 feature points are extracted in two images; and the results of 30 tests are averaged to arrive at a final performance evaluation. The test results are displayed in Figure 6, where the horizontal coordinates indicate the feature point detection time (FPDT), descriptor construction time (DT), descriptormatching time (DMT), and total operation time (OMT) of the algorithms, respectively. The vertical coordinate shows the time consumption of the nonlinear diffusion filtering-based algorithm with the other four algorithms at each stage, which visually compares the efficiency of different algorithms at each processing stage.  
The experimental results reveal that overall, the ORB and the fast image-matching algorithm based on nonlinear filtering presented in this paper are distinguished by their time efficiency in all aspects, surpassing the speed of the other three algorithms. The fast image-matching algorithm employing nonlinear filtering proposed in this article closely matches the ORB algorithm in terms of time consumption for various tasks. Furthermore, the average time consumption of each task is four-five times lower than that of the traditional SIFT algorithm. The total operation time (OMT) is more than six times faster than that of the SIFT algorithm, marginally quicker than the rest of the algorithms.  
Figure 6: Real-time test results of different algorithms.
# A New Algorithm for Computing the Distance and the Diameter in Circulant Graphs
## 4 Diameter Formulas for Circulant Graphs  
Throughout this section, we assume that \(n\geq 5\) and \(2\leq s\leq\lfloor\frac{n}{2}\rfloor\). We express \(n\) as \(n=\lambda s+\gamma\), where \(\gamma\) is an integer satisfying the condition \(0\leq\gamma<s\).  
**Theorem 2** ([4]).: _If \(n=\lambda s\), \(diam(\mathrm{C}_{n}(1,s))=\lfloor\frac{\lambda+s-1}{2}\rfloor\)._  
As we delve into proving the upcoming results, we consider two cases: when \(\lambda>\gamma\) and when \(\lambda\leq\gamma\). We first address the scenario where \(\lambda>\gamma\). Given that we have already discussed, in Theorem 2, the case where \(\gamma\) equals \(0\), our focus now shifts to situations where \(\lambda>\gamma>0\). Within this context, we explore the parity of the integers \(n\) and \(s\) across four scenarios: when both \(n\) and \(s\) are even, when both are odd, when \(n\) is even and \(s\) is odd, and when \(n\) is odd and \(s\) is even. Before delving into this, we introduce the following lemma, which will aid in proving the subsequent results.  
**Lemma 5**.: _Let \(i\) be a vertex of \(\mathrm{C}_{n}(1,s)\). Let \(d\) and \(k\) be two integers such that \(\lfloor\frac{n}{2}\rfloor\leq k<n\). If for each vertex \(i\leq k\), either \(\ell^{1}(i)\leq d\), \(\ell^{2}(i)\leq d\), \(\ell^{3,1}(i)\leq d\), or \(\ell^{4,1}(i)\leq d\), then \(d(i)\leq d\) for all vertices \(i\) of \(\mathrm{C}_{n}(1,s)\)._  
Proof.: Let \(i\in V(\mathrm{C}_{n}(1,s))\) and let \(d,k\in\mathbb{N}\) such that \(\lfloor\frac{n}{2}\rfloor\leq k<n\). We discuss the following cases:  
**Case 1.**\(\ell^{1}(i)\leq d\)**for \(i\leq k\)  
According to Lemma 4, we find that \(d(i)=\min(\ell^{1}(i),\ell^{2}(i),\ell^{1,t}(i),\ell^{2,t}(i),\ell^{3,t}(i),\)\(\ell^{4,t}(i))\). Hence, \(d(i)\leq\ell^{1}(i)\leq d\) for \(i\leq k\). However, considering that \(\lfloor\frac{n}{2}\rfloor\leq k<n\), we can see that \(d(i)\leq d\) for \(i\leq\lfloor\frac{n}{2}\rfloor\). Additionally, we have \(\ell^{1}(i)=r+\lfloor\frac{i}{s}\rfloor=r+\lfloor\frac{n-(n-i)}{s}\rfloor=\ell ^{3,1}(n-i)\), which implies that similarly, by Lemma 4, \(d(n-i)\leq\ell^{3,1}(n-i)\leq d\) for \(i\leq\lfloor\frac{n}{2}\rfloor\), i.e., \(d(i)\leq d\) for \(i\geq\lfloor\frac{n}{2}\rfloor\). Consequently, \(d(i)\leq d\) for all vertices \(i\) of \(\mathrm{C}_{n}(1,s)\).  
**Case 2.**\(\ell^{2}(i)\leq d\)**for \(i\leq k\)  
Utilizing Lemma 4, we derive that \(d(i)\leq\ell^{2}(i)\leq d\) for \(i\leq k\). However, given that \(\lfloor\frac{n}{2}\rfloor\leq k<n\), we note that \(d(i)\leq d\) for \(i\leq\lfloor\frac{n}{2}\rfloor\). We continue in the same manner aswe did in the previous case, noting that this time, we have \(\ell^{2}(i)=1+s-r+\lfloor\frac{i}{s}\rfloor=1+s-r+\lfloor\frac{n-(n-i)}{s}\rfloor= \ell^{4,1}(n-i)\).  
We establish the rest of the cases using the same approach as in Case 1, with the distinction being that in these instances \(\ell^{3,1}(i)=\ell^{1}(n-i)\) and \(\ell^{4,1}(i)=\ell^{2}(n-i)\).
## 3 Results
### Feature Selection and Model Development
#### 3.3.2 The Performance of the Classification Model
For the NMOSD patients, the XGBoost model demonstrated a comparable performance (AUC = 0.918; balanced accuracy = 0.821), with similar sensitivity (85.7%) and slightly lower specificity (78.6%). The different optimal algorithms for each disease group suggest potentially distinct patterns of cognitive-motor integration across these IDD subtypes. A further analysis revealed 27 handwriting features (13.5% of all of the features examined) that significantly differed between the MS and NMOSD patients (\(p<0.05\)). The house-drawing task features accounted for the largest proportion of these differences (40.7%), followed by the connecting line task features (14.8%) and tree-drawing features (14.8%). The most discriminative feature was the air-to-paper-length ratio in the connecting line task (\(p~{}=~{}0.0006\)), which was substantially higher in the NMOSD patients (1.90 vs. 0.83). A consistent pattern emerged across multiple tasks: NMOSD patients exhibited greater variability in velocity and acceleration fluctuations during drawing (e.g., the standard deviation in velocity during house-drawing: 5.86 vs. 4.45; \(p=0.003\)), while the MS patients demonstrated better information processing speeds (E-SDMT scores: 37.78 vs. 27.44, \(p=0.0008\)) and higher pen pressure values. These disease-specific patterns suggest that  
Figure 9: The confusion matrix for the SVM-RBF model using the combined feature set, showing the distribution of the classification outcomes. handwriting features capture both general cognitive dysfunction and disease-specific neurophysiological mechanisms affecting fine motor control and cognitive-motor integration.
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
###### Abstract
This implies that the closer the shadow fraction estimated from the virtual forest is to one from the actual forest, the closer the reflectance simulated from the virtual forest is to that observed by the satellite. Therefore, it is expected that the shadow fraction of the actual forest can be estimated by using a virtual forest with the least reflectance simulation error among the virtual forests with different structural parameters. The reflectance simulation model must account for the shadows of the trees. The objective of this study was to develop a reflectance simulator (Canopy-level Shadow and Reflectance Simulator; CSRS), and to find the optimal canopy shape for estimating the shadow fraction using the reflectance observed by satellite sensors as a constraint. Several challenges were already known of for reflectance simulation approaches [23]. The first is the accuracy of structural parameters other than canopy shape. In this study, parameters such as crown coverage (CC) and TH were obtained by unmanned aerial vehicle (UAV) structure from motion (SfM). The second is using absolute reflectance as a constraint condition. If a parameter of CSRS, such as spectral reflectance of leaves, differs from the actual one, the error will be large, even if other parameters are accurate. Therefore, relative reflectance is used in this study (the ratio of absolute reflectance in two seasons). In case the phenological change is less, as in tropical rainforests [24], it is assumed that the change in reflectance observed by the satellite in the two seasons is affected by shadow due to canopy shape and Sun zenith angle (SZA). The size of the virtual forest should consider the spatial resolution of satellite sensors. If the target sensor is MODIS with a spatial resolution of 500 m, it may result in a heterogeneous forest structure within a pixel. This means the parameter setting is complicated (e.g., cylinder 30% and ellipsoid 70%). Therefore, in this study, Sentinel-2 satellite, which has a high spatial resolution (10-20 m) and is freely available, was selected as the target. We assumed that the forest within one pixel of Sentinel-2 is composed of a single canopy shape. The main objectives of this study using CSRS were: (1) to analyze the effects of canopy shape on simulating reflectance, (2) to obtain the optimal canopy shapes of virtual forests by reflectance simulation, and (3) to evaluate the performance of the optimal canopy shape on shadow fraction estimation.
## Appendix A Simulation Environments
### Fitting the Environment Base Models  
We use ROBAS 2 data to fit the brushing duration model under action 0 (no message). For all model classes, we fit one model per user. All models were fit using MAP with a prior \(w_{i,b},w_{i,p},w_{i,t}\sim\mathcal{N}(0,I)\) as a form of regularization because we have sparse data for each user. Weights were chosen by running random restarts and selecting the weights with the highest log posterior density.  
Fitting Hurdle Models: For fitting hurdle models for user \(i\), we fit the Bernoulli component and the nonzero brushing duration component separately. We use \(D_{i,t}\) to denote the \(i\)th ROBAS 2 user's brushing duration in seconds at the \(t\) time point. Set \(Z_{i,t}=1\) if the original observation \(D_{i,t}>0\) and 0 otherwise. We fit a model for this Bernoulli component. We then fit a model for the normal component to either the square root transform \(Y_{i,t}=\sqrt{D_{i,t}}\) or to inverse-log transform \(Y_{i,t}=\exp(D_{i,t})\) of the \(i\)th user's nonzero brushing duration.  
Fitting Zero-Inflated Poisson Models: For the zero-inflated Poisson model, we jointly fit parameters for both the Bernoulli and the Poisson components. Since the brushing durations in the ROBAS 2 data were integer values, we did not have to transform the observation to fit the zero-inflated Poisson model.  
The fitted parameters for the environment base models can be accessed at: [[https://github.com/StatisticalReinforcementLearningLab/pcs-for-rl/tree/main/sim_env_data](https://github.com/StatisticalReinforcementLearningLab/pcs-for-rl/tree/main/sim_env_data)]([https://github.com/StatisticalReinforcementLearningLab/pcs-for-rl/tree/main/sim_env_data](https://github.com/StatisticalReinforcementLearningLab/pcs-for-rl/tree/main/sim_env_data)) (accessed on 1 June 2022).  
Selecting the Model Class for Each User  
To select the model class for user \(i\), we fit all three model classes using user \(i\)'s data from ROBAS 2. We then chose the model class that had the RMSE. Namely, we choose the model class with the lowest \(L_{i}\), where:  
\[L_{i}:=\sqrt{\sum_{t=1}^{T}(D_{i,t}-\hat{\mathbb{E}}[D_{i,t}|S_{i,t}])^{2}}\]  
Recall that \(D_{i,t}\) is the brush time in seconds for user \(i\) at decision time \(t\). Definitions of \(\hat{\mathbb{E}}[D_{i,t}|S_{i,t}]\) for each model class are specified below in Table 1.
## 4 Simulation Results and Analysis
### Performance of the Globally Optimized Beam-Slot Assignment Algorithm  
In this section, we evaluate the performance of the globally optimized beam-slot assignment algorithm by comparing the beam distributions across time slots before and after applying simulated annealing. Under the simulation settings described in Section 4.1, the maximum number of beams that can be activated over six time slots is 96. Without simulated annealing, the actual number of activated beams is 90, as shown in Figure 3a. After applying the annealing-based iterative optimization, the number increases to 96, as illustrated in Figure 3b.  
The comparison reveals that, without simulated annealing, beam allocation in the first five time slots is based purely on a greedy strategy without global optimization. As a result, the sixth time slot suffers from a lack of available beam positions, significantly reducing the total number of active beams during the BH cycle and degrading overall  
Figure 2: Performance of beam-footprint partitioning algorithm. (**a**) Beam–slot assignment without iterative optimization; (**b**) beam–slot assignment with iterative optimization.  
system performance. In contrast, the inclusion of simulated annealing enables global optimization across all time slots. By introducing random perturbations, the algorithm escapes local optima that may arise from greedy allocation strategies, thereby increasing the number of activated beams from 90 to 96, which corresponds to the theoretical upper bound of system performance.  
The proposed hybrid scheduling algorithm, combining greedy initialization with simulated annealing, exhibits strong scalability. Its computational complexity grows polynomially with the number of candidate beams \(MN\) and is largely insensitive to variations in user density. Experimental results under a large-scale scenario (10 satellites, 240 beams, and 2500 users) show that the algorithm completes in 0.015 s while activating all 240 beams, achieving near-global optimality and effectively avoiding the local optima commonly encountered by purely greedy strategies. Moreover, the simulated annealing phase can be further accelerated through multi-threaded perturbation, enabling the proposed method to scale efficiently to even larger LEO constellation systems.
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Industrial Applications
#### Machinery Health Monitoring  
The objective of machinery health monitoring is to examine the performance of various types of technical equipment and to either detect or predict the occurrence of faults that are obstructive or even catastrophic for their operation.  
In [43], a WSN is developed in order to perform energy usage evaluation and condition monitoring for electric machines. The motor efficiency and health condition are estimated non- intrusively by using wireless nodes that monitor the motor terminal quantities (i.e., line voltages, line currents, and temperature, with no interference with the operation of the electric machines.  
In [44], a WSN based monitoring system of oil and gas pipelines, named REMONG, is proposed. In order to detect the existence of leakages, the system uses wireless sensor nodes which monitor the pressure and temperature of the pipeline fluid at several points of interest on the pipelines which are stretched over large geographical areas.  
In [45] a WSN designed to enhance safety in industrial machinery consisting of a main vehicle and an attached trailer is proposed. A 3D accelerometer and a 3D magnetometer, incorporated in a sensor system device, monitor the trailer operating conditions and the corresponding data are wirelessly transmitted to a processing unit which executes a stability control algorithm. A vibrational energy harvesting system developed, converts kinetic energy from trailer natural vibrations to electrical energy for the system power supply.
## 1 Introduction
### Contact Constraint Method for Three-Dimensional Pavement Roughness
Then, the \(X_{i}\) coordinate of the \(i\)-th wheel at any given moment can be obtained as follows:  
\[X_{i}=X_{i0}+V_{c}T+\frac{1}{2}A_{c}T^{2} \tag{6}\]  
where \(X_{i0}\) is the initial coordinate of the \(i\)-th wheel; \(V_{c}\) is the vehicle speed; \(T\) is the travel time; and \(A_{c}\) is the vehicle acceleration, which is not considered in this study and is set to zero. At any given moment, the vertical displacement of the wheel-bridge contact point \(Z_{i}\), the vertical displacement of the bridge surface at the corresponding contact node \(Z_{Qi}\), and the pavement roughness \(Z_{Ri}\) are related by the displacement coordination equation given as follows:  
\[Z_{i}-Z_{Qi}-Z_{Ri}=0,(i=1,2,3,4) \tag{7}\]  
Figure 4 shows the vehicle-bridge coupling system based on the three-dimensional contact constraint method proposed in this study. The figure provides a detailed explanation of the displacement coordination and force equilibrium relationships between the vehicle model and the bridge model in this method. Point-to-surface contact is established between the wheel-bridge contact points and the bridge model, making it more convenient to achieve force equilibrium. Constraint equations are then established between the wheel bottom nodes and the wheel-bridge contact points according to Equation (7). Figure 4: The principle of the three-dimensional contact constraint method. Figure 3: Three-dimensional pavement roughness samples. The pavement roughness for the left and right wheels is derived from Equation (5). This allows for the convenient establishment of displacement coordination relationships that consider three-dimensional pavement roughness of highway bridges. Compared to the displacement contact method [27], the contact constraint method more easily incorporates three-dimensional pavement roughness. Compared to the displacement coupling method [26], the contact constraint method avoids the issue of complex mechanical coupling within the elements of the vehicle-bridge system. The three-dimensional contact constraint method combines the advantages of both approaches, making it easier to perform three-dimensional vehicle-highway bridge system coupling analysis. The constraint equation is a linear equation that satisfies the relationship between the node degrees of freedom. Therefore, the impact of pavement roughness can be considered by establishing the displacement coordination relationship between the wheel bottom node at the corresponding location and the wheel-bridge contact point.