esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Survey and Critical Analysis
### Literature Collection and Filtering Methodology  
The search process carried out is depicted schematically in Figure 1. The source of the bibliographic search was SCOPUS [35], using as search terms \"multi-objective job shop scheduling\", without any type of limitation. The search query using these terms returned a total of 772 results, over which a first filter was applied: duplicated works were removed, as well as those contributions that did not relate at all to industrial and/or manufacturing processes. Likewise, works that were not peer-reviewed were excluded from the search results, so as to retain only journal articles and conference papers. This first filtering exercise reduced the amount of returned documents to 420 results.  
A second filter selected those papers that had terms such as job, shop, job shop, flow shop, job-shop, and industry-focused and with at least two objectives, one of them focused on energy. After this filtering, we were left with 61 references.  
Figure 2 shows the number of papers published per year until 2020. We can observe an almost linear growth since 2017, indicating that the field of multi-target JSPs considering energy has been gaining momentum in recent years. This suggests the need for a reflexive compendium of the research activity reported in these studies, which is the motivation of the literature analysis presented in the subsections following hereafter.  
Figure 1: Search and filtering methodology flowchart.  
Figure 2: Number of publications per year for the multi-objective JSP problem with energy as one of the targets.
# Improving Entity Linking by Introducing Knowledge Graph Structure Information
## 5 Experiments
### Ablation Study  
In order to study the role of each module of the model, an ablation study was also performed in this research, and the experimental results are shown in Table 4. We utilize the following variants:  
1. _KgEL_ is our proposed method, which includes three modules: Local model, WikiEmbs Global model, and KGEmbs Global model.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **AIDA-B** & **MSNBC** & **AQUAINT** & **ACE2004** & **CWEB** & **WIKI** & **Avg** \\ \hline AIDA & - & 79 & 56 & 80 & 58.6 & 63 & 67.32 \\ GLOW & - & 75 & 83 & 82 & 56.2 & 67.2 & 72.68 \\ RI & - & 90 & **90** & 86 & 67.5 & 73.4 & 81.38 \\ PBoH & 87.6 & 91 & 89.2 & 88.7 & - & - & - \\ Deep-ED & 92.22 & 93.7 & 88.5 & 88.5 & **77.9** & 77.5 & 85.22 \\ Ment–Norm & 93.07 & 93.9 & 88.3 & 89.9 & 77.5 & 78 & 85.5 \\ DCA-SL & **94.64** & **94.57** & 87.38 & 89.44 & 73.47 & 78.16 & 84.6 \\ DCA-RL & 93.73 & 93.80 & 88.25 & 90.14 & 75.59 & **78.84** & 85.32 \\ KGEL(ours) & 93.47 & 94.26 & 88.11 & **90.54** & 77.21 & 78.40 & **85.7** \\ \hline \hline \end{tabular}
\end{table}
Table 3: F1 scores on AIDA-B and five out-domain test sets. The last column is the average of F1 scores on the five out-domain datasets. The best results are in bold.  
As can be seen in Table 4, when the KGEmbs Global model is removed, the results on four datasets and the average result on the out-domain datasets drop dramatically. This proves the validity of the KGEmbs Global model, i.e., the necessity of introducing knowledge graph structural information. Similarly, we can find that the results on each dataset drop more significantly when the WikiEmbs Global model is removed, indicating that using only the structural information in the knowledge graph is insufficient because there is a certain sparsity in the knowledge graph, i.e., not every pair of entities has a clear relationship with each other, so the structural information of the knowledge graph has a certain guiding effect on the linking of entities, but cannot be used independently. After removing the Local model based on -_WikiEmbs_, we find that the results on each dataset have further decreased, which illustrates the necessity of the local model. Thus, the entire ablation experiment shows that all modules of the model are valid.
# Optimizing Roller Design to Improve Web Strain Uniformity in Roll-to-Roll Process
## 4 Experiment
### Comparison of Experimental and FEA Results  
In the DOE-based FEA, the tension was 10 MPa. However, the experiment was conducted under 5 MPa of tension due to the maximum load limitation of the dancer roller. In addition, reference markings for measuring the MD strain deviation were already engraved under a tension of 3 MPa. This is because the web is transferred to the patterning section. Therefore, in the verification experiment, the reference position of each mark was measured under 3 MPa of tension, and the strain was calculated by measuring the position of the mark after increasing the tension to 5 MPa. To compare the MD strain deviations of the experiments and FEA under the same conditions, an FE model with a tension of 5 MPa was constructed. In the 200 mm span, the printing lines were set at distances of 50 and 150 mm from the contact point of the roller and the web. The mechanical properties used in this FEA are shown in Table 9. Figure 17 shows the FEA results of the strain distribution in the MD for these conditions.  
The strain deviations of the conventional and optimized barrel rollers were 0.00357% and 0.0005%, respectively. For the FEA model, the relative displacements were 7.14 and 1 \(\upmu\)m, respectively. Based on these results, the compensation performance was approximately 85%.  
There are a few explanations for the mismatch between the results of the experiment and the FEA. The reference marks were not placed exactly 50 mm from the central axis of the roller in the actual experiment. Additionally, uniform tension could not be applied with the dancer roller. Moreover, the reference marks may not have been symmetrically arranged around the center of the CD of the web, the laser mark array and the central axis of the rollers were misaligned, and the properties applied in the FEA were not perfectly accurate.  
Figure 17: Comparison of FEA between the existing roller and barrel roller derived by calculating the MD strain distribution under 3 MPa of tension and then changing the tension to 5 MPa.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Young’s Modulus** & **Mass Density** & **Poisson’s Ratio** \\ \cline{2-4}  & **E (GPa)** & \(\rho\) **(kg/m3)** & **-** \\ \hline Roll (aluminum) & 71 & 2,770 & 0.33 \\ Silicon rubber (skin) & 0.00285 & 1,200 & 0.5 \\ Web (PET) & 3.5 & 1,320 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Mechanical properties applied to FEA to simulate the MD strain deviation of the web under the same tension conditions as the experiment.
## 4 Simulation Results and Analysis
### The Radio Wave Propagation Problem  
In this example, radio wave propagation in a tunnel is simulated. The structure and dimensions of the tunnel are shown in Figure 11a, and the permittivity and conductivity for the tunnel are \(\varepsilon_{r}=4\) and \(\sigma=0\,\mathrm{S/m}\), respectively. A schematic diagram of the source and observation point positions is shown in Figure 11b. The excitation source is a Gaussian pulse. An FDTD algorithm based on a CPU and GPU is used to calculate the x-component electric field (Ex) of the observation point, as shown in Figure 12, which exhibits good consistency. Similar to the two other examples, the calculation time, speedup, and parallel efficiency under the conditions of a CPU, a single GPU, dual GPUs at a dual node, and four GPUs at a dual node are tabulated in Table 3, which proves the efficiency of the proposed method.  
Figure 11: Radio wave propagation in a tunnel. (**a**) The three-dimensional structure of the tunnel. (**b**) A schematic diagram of the source and observation point positions.  
Figure 12: The waveform of Ex at the observation point in the tunnel.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hardware**} & \multirow{2}{*}{**CPU**} & \multirow{2}{*}{**Single GPU**} & \multicolumn{2}{c}{**Dual GPUs at Dual Node**} & \multicolumn{2}{c}{**Four GPUs at Dual Node**} \\ \cline{3-6}  & & & **Without NCCL** & **With NCCL** & **Without NCCL** & **With NCCL** \\ \hline Time (s) & 2540 & 203 & 105 & 102 & 55 & 52 \\ Speedup & 1 & 12.6 & 24.2 & 24.9 & 46.2 & 48.8 \\ Efficiency & \(\backslash\) & 100.0\% & 96.0\% & 98.8\% & 91.7\% & 96.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation time, speedup, and efficiency under different hardware conditions of example 4.3.
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 2 Solid-State Single Photon Counting Imaging Background
Binary black and white imaging is not inherently practical for many imaging applications, therefore spatio-temporal oversampling is employed to create gray levels [14; 19]. SPAD-based image sensors based on analogue counting techniques first appeared in [33] and have recently been demonstrated with 8 to 15 \(\upmu\)m PP commensurate with CCD, EMCCD and sCMOS image sensors, and fill factor (FF) as high as 26.8% [15; 16; 34]. These sensors achieve time-gating comparable to gated photo-cathodes in the nanosecond [18] and sub nanosecond range [34]. Analogue-based SPAD imagers employ conventional CIS readout techniques and so, to aid comparison with CCD and CIS, equivalent metrics may be applied such as:  
* Sensitivity, of the counter circuit to one SPAD avalanche event in mV/SPAD event, equivalent to CVF (or CG). * Maximum number of SPAD events equivalent to full well. * Input referred RN normalising voltage RMS RN to one SPAD event instead of one photoelectron. These equivalencies are used throughout this paper. SPAD-based image sensors are the first solid-state imaging technology to have demonstrated sub 0.15 e\({}^{-}\) RN, and as such provide a look-ahead to the signal and noise characteristics of DSERN image sensors in CMOS and other technologies.
## 4 Results
### Influence of Urea Crystallization Risk under Structural Factors
#### 4.2.4 Influence of the Spray Cone Angle on Crystallization Characteristics  
The variation of maximum urea film thickness with time under different spray cone angles is shown in Figure 27. The maximum urea film thickness under different spray cone angles gradually increases with the extension of spray time at different growth rates. At 200 ms ASOI, the maximum urea film thickness corresponding to each spray cone angle is between 200 and 400 \(\upmu\)m, indicating that changing the cone angle of a single mist beam will have a certain impact on the maximum urea film thickness. Before the 30 ms ASOI moment, there was the apparent difference in the maximum urea film thickness between the five groups of spray cone angles. Before the 130 ms ASOI moment, the maximum urea film thickness corresponding to a spray cone angle of 11\({}^{\circ}\) was basically consistent with the maximum urea film thickness corresponding to a spray cone angle of 13\({}^{\circ}\). Beyond the 130 ms ASOI moment, the former always grew at a stable rate, while the latter first lagged behind the former and then approached the former at a faster rate. The variation trend of the maximum urea film thickness corresponding to a spray cone angle of 15\({}^{\circ}\) is similar to that at 13\({}^{\circ}\). The maximum urea film thickness values fluctuate during the increase process, with only differences in the maximum urea film thickness values. The maximum urea film thickness corresponding to a spray cone angle of 17\({}^{\circ}\) and 19\({}^{\circ}\) did not show a significant difference before the 120 ms ASOI moment. After exceeding the 120 ms ASOI moment, the maximum urea film thickness of the former was relatively large, and the difference between the two remained stable.  
Extract urea and UWS film mass accumulation data under different spray cone angles at 200 ms ASOI time, and obtain the variation of urea and UWS film mass accumulation with spray cone angles, as shown in Figure 28. The accumulation of urea film mass shows a  
Figure 27: Variation of maximum urea film thickness with time under different spray cone angles.  
growth trend with the increase of the single mist beam cone angle. The reason is that while increasing the cone angle of the single mist beam, the area where urea droplets collide with the wall of the porous tube increases, resulting in more urea droplets transforming into a urea film. As the cone angle of the single mist beam increases, the variation amplitude of urea film accumulation gradually decreases. The cone angle of a single mist beam can simultaneously affect the maximum urea film thickness and urea film accumulation, and the two exhibit a \"trade-off\" relationship. Therefore, when determining the cone angle of the spray, a \"balance\" point should be found to minimize the risk of crystallization. In addition, it is also necessary to achieve collaborative control of crystallization risk by reasonably matching the parameters of spray cone angle and single hole spray angle.
###### Abstract  
This paper proposes a beam model integrating the Timoshenko beam theory with Vlasov beam theory to capture the coupled behavior of bidirectional bending, torsion, and axial vibration in thin-walled beams subjected to axial loads. Our model incorporates the effects of shear deformation, rotational inertia, and axial loads, offering a comprehensive approach to complex dynamic behaviors. By utilizing [PERSON]'s principle, we derived a complete set of coupled dynamic equations and boundary conditions. The highlight of this model is its capacity to accurately predict the dynamic response of thin-walled beams under multifaceted loading conditions, surpassing traditional models by integrating coupled axial vibrations. This research significantly advances the understanding of the dynamic behavior of thin-walled beams, providing a precise analytical tool for structural design and safety assessment. The robustness and accuracy of the proposed model were validated through extensive theoretical analysis and empirical validation, equipping engineers with critical insights to optimize the design of engineering structures subjected to complex dynamic loads.  
thin-walled beam; coupled vibrations; axial loads; natural frequency; dynamic behavior Article
## 5 Factors That Influence the Effectiveness of In-Vehicle Messaging
### Driver Characteristics
#### 5.1.2 Technology Experience and Acceptance  
The degree to which drivers engage vehicle automation and other modern technologies may be determined in large part by their experience with and acceptance of the technology. For example, surveys about driver acceptance towards automated vehicle technologies have shown that drivers tend to vary in comfort in using adaptive cruise control and active lane-keeping across driving situations [97]. In specific, drivers were more comfortable with automation during free-flowing traffic than in stop-and-go traffic situations [97]. The degree to which drivers utilize these technologies is also affected by their level of priorexperience. Prior research has shown that drivers who are accustomed to adaptive cruise control tend to be faster at responding to event notifications during automated driving [98].  
Older adults are a demographic of particular interest, when considering technology acceptance, as they stand to benefit greatly from vehicle automation. Some research has shown that adaptive cruise control and lane-keeping systems can improve overall vehicle safety [99; 100]. However, older drivers tend to exhibit less trust in vehicle automation, likely due to a general lack of experience and technical support [101]. Compared to younger and middle-aged drivers, older drivers also have greater adaptation to roadside signage. This experience could have an impact on how older drivers adapt to any transition from roadside displays to in-vehicle displays. Furthermore, research has found that older drivers engage in more conversational secondary tasks during automated driving while younger drivers tend to be immersed in electronic device use [46]. Considering in-vehicle delivery of signage information during automated driving, how secondary activities impact the use of in-vehicle information communication remains an open and important research question.  
For in-vehicle messaging content to be effective, this means considering individual differences including age differences with appropriate notification times to allow drivers to respond and intuitive interface design to promote adoption. The implementation of in-vehicle messaging of signage and interface design should consider existing guidelines and the standards for designing in-vehicle communication systems [7; 62; 66].
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
## 3 The Proposed Method
### Content Popularity Inference  
Each vehicle has a request counter table (Figure 5) to count how many times a content is requested to infer the content's popularity. It involves both requests from inside the cluster and the requests from other clusters that pass this cluster. Interest packets and data packets contain a timestamp. This timestamp, together with the content name, serves as a unique Request ID. When a vehicle receives one of these packets, it checks whether the Request ID in this packet is fresher than the one in the corresponding entry of the table, and if affirmative, it increases the counter by 1 and updates the Request ID. In this way, the counter of the content reflects its popularity.  
Algorithm 2 is the pseudo-code for updating content's popularity.  
```
1 ProcedureUpdateContentPopularity Data: Input: contentName, requestTimestamp ifDoes the vehicle's request counter table have the entry for contentName?then
2 timestampInTable \(\leftarrow\) the timestamp recorded in the table ; ifIs requestTimestamp never than timestampInTable?then
3 Increase the popularity of contentName by 1 ; Update the timestamp in the table entry with requestTimestamp ;
4 end if
5 else
6 Create an entry for contentName with its request timestamp requestTimestamp ;
7 end if
8
9 end for
10
11 end for
```  
**Algorithm 2**Updating content's popularity
# The Effect of Porous Media on Wave-Induced Sloshing in a Floating Tank
## 5 Conclusions  
From this study, some conclusions can be drawn:  
1. Porous media can accelerate the energy transmission in the FSI process, enhance the damping capability of the sloshing fluid, and reduce the sloshing velocity and deformation.
2. The sloshing fluid will create a control force on the body. Under certain circumstances, it will cancel the wave loading. When it happens, the work done by incident waves is transferred to the fluid in the water tank (meanwhile, the wave-induced sloshing behavior appears), and then dissipated by the damping mechanism of porous media.
3. The mechanical model produces linear sloshing reactions, which affects the accuracy of the amplitude of the body's pitch motion for the large-wave case. However, it still captures the oscillatory frequencies very well. For the small-wave case, the difference is insignificant. When the tank is subject to pitch motion, the first modal sloshing response dominates.
4. In the near-resonance tests, the average reduction in the RMS pitch motion by the porous-media TLD was 85%. It did not produce an effective control force for the heave motion. Moreover, the force cancellation in the horizontal motion was weak. Therefore, the application in surge mitigation is uncertain. Compared to the freshwater TLD, the mistuning effect was alleviated.
5. Porous media can effectively decrease the hydrodynamic pressure by up to 45%. The integrity of the liquid container and the stability of the liquid carrier can be improved.  
Conceptualization, W.-H.T. and C.E.K.; methodology, W.-H.T. and C.E.K.; software, W.-H.T.; formal analysis, W.-H.T.; investigation, W.-H.T. and Y.-C.C.; data curation, W.-H.T.; writing--original draft preparation, W.-H.T. and Y.-C.C.; writing--review and editing, W.-H.T., Y.-C.C., C.E.K. and L.M.; visualization, W.-H.T.; supervision, C.E.K. and L.M.; funding acquisition, C.E.K. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by internal research funding from the Center for Computation and Technology and the College of Engineering at Louisiana State University.  
Figure 36: The frequency responses of the local sloshing behavior in the water tank on a floating body for the small-wave case: (**a**) maximum dynamic pressure on the wetted wall; (**b**) normalized height of pressure concentration.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Comparison with the State-of-the-Art
Compared with our method, they have reported higher precision and recall results on a large dataset i.e., PETS2007 along with a self-collected set of videos. However, seeing that their model is trained for one action only and was trained on the PETS2007 dataset, which is significantly larger than our dataset of activities related to loitering, our results are satisfactory. Moreover, our detection approach avoids the work involved in trajectory calculations, analysis, and handcrafting. [PERSON] et al. (2017) have used a 3D-CNN to achieve 89% accuracy on the Hockey-DS. [PERSON] et al. (2018) have developed a histogram intersection kernel (HIK) framework using the Space-Time Interest Points (STIP) and Motion SIFT (MoSIFT) action descriptors. The method has been trained and tested on Hockey-DS and Movies-DS with an accuracy of 91% and 89%, respectively. [PERSON] et al. (2020) proposed a multi-level fusion approach that integrates local motion patterns using features from sequences of input frames. They use a 2D-CNN in combination with a Wide-Dense Residual Block (WDRB) to learn feature maps. The model was trained on four datasets i.e., Hockey-DS, Movies-DS, Crowd Violence, and BEHAVE separately to achieve high accuracy results. However, they have not reported the precision and recall values for their model. Hence, considering the overall results for the joint detection of three behaviors on the combined dataset, our approach yields relatively better results. The overall performance of our model has been positively affected due to pretraining on a fairly large activity dataset, i.e., Kinetics, as well as through the use of an enhanced dataset based on insight into specifics of each abnormal behavior.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 2 Materials and Methods
### The Dual-Polarization, Doppler Radar Located on Monte Lema at an Altitude of 1625 m
#### 2.3 The Metallic Tower on Cimetta at an Altitude of 1633 m: A Particularly Bright Target!  
As stated in the introduction, at the Cimetta range (~18.14 km, at the center of the radar bin), the radar resolution cell is 83.33 mm along-range and 314 m across-range (elevation and azimuth). The picture in Figure 1 shows the mountainous backscattering environment that characterizes the radar bin analyzed in this study. The view is from East to West (an azimuth of ~260\({}^{\circ}\)) so that it is practically orthogonal to the direction of the incident electromagnetic radar ray (the Azimuth of the radar bin at hand is 349\({}^{\circ}\)), which, consequently, comes from the left side of the picture. The trees on mountain were chopped down centuries ago, so that the land is basically used for grazing, with just a few sparse trees remaining. There are some small summer cottages inside the bin, plus a (larger) hut that is located at 30 m from the tower (East direction). The tall metallic tower is by far the largest scatterer in the radar bin; it is orthogonal to the incident electromagnetic energy (angle of Elevation of \(-0.2^{\circ}\)). It is as large as 12 \(\times\) 12 m at the base and is covered by solar panels (not visible in the picture, since they are behind the hut). There are all sorts of telecommunication and broadcasting antennas at heights of between 6 and 12m, and then the tower becomes more slender and reaches a height of 90 m above the ground (\(\sim\)1633 m altitude). This point target exhibits a very large radar cross section and acts as a dominant target in a background of randomly distributed scatterers.