esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, we studied the problem of set-based preference learning (SPL) for privacy-preserving recommendation, which is important but has been studied little. Observing that the user's preference for the set is usually leveraged by a few items but not the whole set, we proposed the intuition of subset learning to address the problem of SPL. Our solution is the _self-attentive subset learning model_ (SaSLM), which consists of a _self-attentive policy network_ (SaPN) that learns user subset selection policy. To stabilize the training and ensure the smoothness when fitting set ratings, we also use personalized positional weights to achieve weighted rating aggregation. Unlike existing SPL methods, SaSLM learns personalized and flexible subsets, which largely reduces the gap between set ratings and item ratings. Experiments on real-world datasets demonstrated the effectiveness of SaSLM in the task of SPL.  
Since SaSLM mainly focuses on the users' item-level preference disaggregation in a privacy-preserving way, we only evaluated the preference prediction of items contained in sets. In practice, it is important that the item-level preferences can be further propagated to items out of sets. Instead of zero direct item-level supervision, it is also interesting to study the problem when few item-level ratings are available. In future, we plan to perform further research to generalize the definition of SPL and extend SaSLM accordingly.  
Conceptualization, methodology, formal analysis, writing--original draft preparation K.L.; investigation, writing--review and editing, Y.C.; supervision, L.L.; project administration and funding acquisition, J.T. and H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2020 AAA0108800), the National Natural Science Foundation of China (71971212), and the Postgraduate Scientific Research Innovation Project of Hunan Province (CX20200067).  
Not applicable.  
Dataset in this study can be fetched from [[https://grouplens.org/datasets/learning-from-sets-of-items-2019/](https://grouplens.org/datasets/learning-from-sets-of-items-2019/)]([https://grouplens.org/datasets/learning-from-sets-of-items-2019/](https://grouplens.org/datasets/learning-from-sets-of-items-2019/)), [[https://music.163.com](https://music.163.com)]([https://music.163.com](https://music.163.com)), [[https://www.yousuu.com/](https://www.yousuu.com/)]([https://www.yousuu.com/](https://www.yousuu.com/)), all accessed on 28 December 2022.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
###### Abstract  
We present an occlusion-aware unsupervised neural network for jointly learning three low-level vision tasks from monocular videos: depth, optical flow, and camera motion. The system consists of three different predicting sub-networks simultaneously coupled by combined loss terms and is capable of computing each task independently on test samples. Geometric constraints extracted from scene geometry which have traditionally been used in bundle adjustment or pose-graph optimization are formed as various self-supervisory signals during our end-to-end learning approach. Different from prior works, our image reconstruction loss also takes account of optical flow. Moreover, we impose novel 3D flow consistency constraints over the predictions of all the three tasks. By explicitly modeling occlusion and taking utilization of both 2D and 3D geometry relationships, abundant geometric constraints are formed over estimated outputs, enabling the system to capture both low-level representations and high-level cues to infer thinner scene structures. Empirical evaluation on the KITTI dataset demonstrates the effectiveness and improvement of our approach: (1) monocular depth estimation outperforms state-of-the-art unsupervised methods and is comparable to stereo supervised ones; (2) optical flow prediction ranks top among prior works and even beats supervised and traditional ones especially in non-occluded regions; (3) pose estimation outperforms established SLAM systems under comparable input settings with a reasonable margin.  
monocular depth; camera pose; optical flow; joint learning; occlusion-aware; scene geometry
# Chitosan Films with Caffeine and Propolis as Promising and Ecofriendly Packaging Materials
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM)  
The surface attributes of the films, as examined via scanning electron microscopy, are presented in Figure 3.  
The studies revealed that the film samples A (chitosan prepared in acetic acid) and C (chitosan prepared in citric acid) were characterized by a smooth surface structure in most areas, without obvious holes or bubbles, with all major areas being smooth and homogeneous. This is evidence for a great dispersion of chitosan in both used acids. A homogenous appearance is an indication of the structural stability of the chitosan/acid systems. The inclusion of CAF in the tested chitosan film samples (AC and CC) caused the emergence of unequally sized irregular clusters, probably due to the aggregation of caffeine particles on the surface. In the case of samples AC and CC, agglomeration caused a decrease in the sample elasticity (decrease in elongation). Also, the addition of EP to chitosan films caused the emergence of irregular clusters on the surface (samples ACP and CCP). This agglomeration reduced the mechanical properties of samples ACP and CCP and caused a deterioration of the OTR, as presented in Section 3.3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Chitosan-Based Films** & \(m\) **(kg/kg)** & \(K\) & \(k\) & \(w\) & \(R^{2}\) \\ \hline A & 0.5609 & 0.3341 & 0.9205 & 1.1920 & 0.9999 \\ AC & 0.1216 & 0.5106 & 0.7994 & 2.0868 & 0.9974 \\ ACP & 0.0984 & 0.5612 & 0.7439 & 2.7808 & 0.9967 \\ C & 0.5419 & 0.0110 & 0.8006 & 30.5221 & 0.9882 \\ CC & 0.5237 & 0.0105 & 0.7632 & 32.0767 & 0.9855 \\ CCP & 0.4108 & 0.0102 & 0.8042 & 33.5309 & 0.9892 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Estimated coefficients of the sorption GDW model at 25 \({}^{\circ}\)C for chitosan-based films.  
Figure 3: SEM images of the chitosan-based films samples: (A) (acetic acid/no additives), (AC) (acetic acid/CAF), (ACP) (acetic acid/CAF/EP), (C) (citric acid/no additives), (CC) (citric acid/CAF), (CCP) (citric acid/CAF/EP).
# Proactive Service Caching in a MEC System by Using Spatio-Temporal Correlation among MEC Servers
## 2 Related Works
### ConvLSTM Model  
The ConvLSTM model is proposed to solve a spatio-temporal sequence forecasting problem by combining the fundamental principles of the LSTM model and the CNN model [17]. The LSTM model has a memory cell storing state information. The information flow through a memory cell is controlled by three gates, named as an input gate, a forget gate, and an output gate. By efficiently handling temporal correlations in the sequence data, the LSTM model has effectively solved real-life sequence modeling problems. However, since the LSTM model uses 1D tensor, it is not adequate for spatial data.  
The ConvLSTM model resolves the issue by encoding the spatial information and incorporating a convolutional structure into the LSTM model. The ConvLSTM model resolves the issue by encoding the spatial information and incorporating a convolutional structure into the LSTM model. In Figure 1, we show the operation of a ConvLSTM cell at time step \(t\). The notations used in this figure are summarized in Table 3.  
In the ConvLSTM model, all inputs \(X_{t}\), cell outputs \(c_{t}\), hidden states \(h_{t}\), and the outputs of all gates (\(i_{t}\), \(f_{t}\), \(o_{t}\)) are 3D tensors. The main process in a ConvLSTM cell is described as  
\[\begin{split} i_{t}&=\sigma(W_{xi}*X_{t}+W_{hi}*h_{t- 1}+W_{ci}\circ c_{t-1}+b_{i})\\ f_{t}&=\sigma(W_{xf}*X_{t}+W_{hf}*h_{t-1}+W_{cf} \circ c_{t-1}+b_{f})\\ c_{t}&=f_{t}\circ c_{t-1}+i_{t}\circ\tanh(W_{xc}*X_{t }+W_{hc}*h_{t-1}+b_{c})\\ o_{t}&=\sigma(W_{xo}*X_{t}+W_{ho}*h_{t-1}+W_{co}\circ c _{t}+b_{o})\\ h_{t}&=o_{t}\circ\tanh(c_{t}),\end{split} \tag{1}\]where the operator \(*\) represents the convolutional operation, and \(\circ\) denotes the Hardmard product. The ConvLSTM model has been used to predict the next frame in a video [18; 39].
# End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe
## 1 Introduction
### State of the Art and the Contribution of the Present Study
This paper reports the results of a cradle-to-grave LCA of three light-duty commercial vehicles (LDCV) configurations: diesel, compressed natural gas (CNG), and battery electric vehicle (BEV). For each configuration, the environmental benefits and burdens of the EoL stage were evaluated. This is one of the few studies to have considered a type of vehicle different from the passenger car and to have focused on the transport of goods rather than passengers. Urban freight transport (UFT), in fact, is a growing market sector, especially due to e-commerce [40]. Moreover, the COVID-19 pandemic has highlighted the importance of urban logistics since it further accentuated the expansion of e-commerce and instant deliveries [41]. Another aspect of the novelty of this study concerns the investigation of the recycling effect on its impact under two different allocation methods, which we will refer to as the EoL scenarios. In fact, the environmental impacts calculated through the avoided burden method were compared to the ones calculated with the circular footprint formula (CFF). While the avoided burden method is the most used approach in the literature, the CFF is highly relevant because it was developed within the PEF method of the European Commission, with the aim of standardizing the allocation of burdens and benefits in the EoL stage [42]. Unlike many other studies, a significant number of primary data have been used in this paper: the bill of materials (BOM) of the vehicles, the data of the manufacturing plant, and the data of the supplier locations have been provided by the vehicle manufacturer. Materials and methods are reported in Section 2, which is divided into three paragraphs. First, a brief description of the LCA methodology is reported in Section 2.1. Second, for each vehicle configuration, the waste management scheme is described in Section 2.2. Finally, the two EoL scenarios are explained in Section 2.3.
## 2 Sample and Methods  
### Preparation of Coal Samples and Selection of Gases  
The briquette has similar isotropic mechanical properties, which avoids the issue that the raw coal has a large dispersion of research results due to the difference in pore fracture development. Therefore, briquettes are selected in the test. During the test, the sample was acquired from the 15# coal seam of Yuquan Coal Mine in Yangquan, Shanxi Province. The 15# coal seam has a high risk of outburst with a gas content of 4.71 m\({}^{3}\)/t. The basic parameters of coal samples from 15# coal seam are shown in Table 1. The coal sample preparation process is shown in Figure 1. Firstly, after crushing the coal, the coal powder with the particle size of 80-120 mesh (120-180 um) was sieved. Subsequently, 260 g of coal powder and 20 g of water were mixed homogeneously. After that, the mixture was poured into a mold. Under a molding pressure of 8 MPa and a pressing time of 30 min, a cylindrical sample with a size of 50 mm \(\times\) 100 mm was prepared with an error of \(\pm\)1 mm. Finally, the treated sample was wrapped with plastic film and placed in a drying oven at 80 \({}^{\circ}\)C for 24 h. Before attaching the strain gauge, the surface of the coal sample was wiped with alcohol. The strain gauge was attached along the axial and radial directions of the coal sample with glue. During the pasting process, the flatness of the strain gauge was ensured.  
To study the deformation and damage of the coal body under different gas parameters, the instantaneous pressure relief experiments were carried out under different pressure conditions with CH\({}_{4}\), CO\({}_{2}\), and N\({}_{2}\) gases separately. The purity of each gas was 99.99%.
# Multi-Agent Simulation Approach for Modular Integrated Construction Supply Chain
## 3 Proposed Agent-Based Model
### Model Implementation Using AnyLogic
#### 3.4.1 Why AnyLogic?  
AnyLogic is a powerful simulation software that is well suited for modeling complex systems involving multiple interacting agents, such as the MiC supply chain [23]. The choice of AnyLogic is justified by several key factors. Firstly, AnyLogic supports various simulation methods, including discrete event simulation (DES), system dynamics (SD), and agent-based modeling (ABM) [27]. This flexibility allows for the comprehensive modeling of complex systems, enabling the integration of different approaches to accurately reflect real-world processes.  
Additionally, AnyLogic's robust agent-based modeling capabilities are particularly useful for simulating the interactions between different entities in the MiC supply chain. Each agent can be programmed with specific behaviors and rules, allowing for detailed and dynamic simulations of the logistics process. Moreover, AnyLogic can integrate real-time data into the simulation model, which is essential for creating an accurate and responsive digital twin of the MiC supply chain [52]. This capability ensures that the model can adapt to changing conditions and provide up-to-date insights for decision-making.  
Furthermore, AnyLogic is designed to handle large-scale simulations [53], making it suitable for modeling the extensive and complex interactions within the MiC process. Its performance and scalability ensure that the model can manage the high volume of data and interactions required. AnyLogic also provides powerful visualization tools that allow users to see the simulation in action, providing valuable insights into the dynamics of the system. The ability to visualize interactions and outcomes helps in understanding the system's behavior and identifying areas for improvement. Lastly, AnyLogic can be integrated with other software and systems, such asGIS, MES, and BIM, enabling a seamless flow of information and enhancing the model's accuracy and utility [23]. This integration is crucial for creating a comprehensive and realistic simulation environment.
###### Abstract  
With increasing energy demands and depleting oil accessibility in reservoirs, the investigation of more effective enhanced oil recovery (EOR) methods for deep and tight reservoirs is imminent. This study investigates a novel hybrid EOR method, a synergistic approach of nonionic surfactant flooding with intermediate CO\({}_{2}\)-based oil swelling. This study is focused on the efficiency of surfactant flooding and low-pressure oil swelling in oil recovery. We conducted a fluorescence-based microscopic analysis in a microchannel to explore the effect of sodium dodecyl sulfate (SDS) surfactant on CO\({}_{2}\) diffusion in Texas crude oil. Based on the change in emission intensity of oil, the results revealed that SDS enhanced CO\({}_{2}\) diffusion at low pressure in oil, primarily due to SDS aggregation and reduced interfacial tension at the CO\({}_{2}\) gas-oil interface. To validate the feasibility of our proposed EOR method, we adopted a'reservoir-on-a-chip' approach, incorporating flooding tests in a polymethylmethacrylate (PMMA)-based micromodel. We estimated the cumulative oil recovery by comparing the results of two-stage surfactant flooding with intermediate CO\({}_{2}\) swelling at different pressures. This novel hybrid approach test consisted of a three-stage sequence: an initial flooding stage, followed by intermediate CO\({}_{2}\) swelling, and a second flooding stage. The results revealed an increase in cumulative oil recovery by nearly 10% upon a 2% (_iv_/_v_) solution of SDS and water flooding compared to just water flooding. The results showed the visual phenomenon of oil imbibition during the surfactant flooding process. This innovative approach holds immense potential for future EOR processes, characterized by its unique combination of surfactant flooding and CO\({}_{2}\) swelling, yielding higher oil recovery.  
Keywords:huff-n-puff; EOR; chemical EOR; carbon capture and storage; surfactant flooding; reservoir-on-a-chip +
Footnote †: journal: applied sciences
## 3 Results and Discussion
### Spike Recovery Experiment  
Spike recovery experiments largely reflect the accuracy of a measurement method. As can be seen from Table 4, the average recovery rate of three groups of experiments were 86.47%-91.98% for RES. At the same time, the relative standard deviation (RSD) value were less than 3%, indicating a good experimental repeatability.
# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
## 4 Experimental Verification
### Case Study 1: Application in CWRU Datasets
#### 4.1.2 Diagnostic Results and Analysis  
The three datasets in Table 2 are applied to verify the model, and the results are illustrated in Figure 6. For each block in the matrix square, the horizontal side means the predicted category label and the vertical side means the actual category label; the values of the dark-colored left diagonal line are the number of samples correctly predicted by the model for each sample. It is clear that a larger value reflects the superior classification of the model. In Dataset A, all samples were identified correctly. In Dataset B, three samples in BF014 were identified incorrectly. In Dataset C, a total of 12 samples of BF007 and BF021 were identified incorrectly. In summary, the overall identification accuracy of the model on the two unbalanced datasets is 99.25%, of which the average identification accuracy for the minority class samples is 99.16%. From the experimental results, we can conclude that the improved model obtains more excellent accuracy for the minority class samples in the unbalanced fault diagnosis of CWRU-bearing datasets.  
Figure 5: Vibration signals of bearing: (**a**) normal; (**b**) ball fault (007); (**c**) ball fault (014); (**d**) ball fault (021); (**e**) inner fault (007); (**f**) inner fault (014); (**g**) inner fault (021); (**h**) outer fault (007); (i) outer fault (014); (j) outer fault (021).
## 3 Results and Discussion  
This section provides simulation results for the presented WHT-based joint NOMA and IRC in comparison with the conventional NOMA scheme using perfect and imperfect SIC conditions. The results are discussed in terms of BER, and subsequent user throughput performance. The simulations are carried out in MATLAB using two case scenarios, i.e., (1) investigating the performance of SU when it pairs with an interfering MU in terms of BER and throughput performance using conventional NOMA and WHT-NOMA, (2) performance of SCUs and SEUs in terms of user throughput and BER, considering WHT-NOMA with IRC to suppress the ICI effects. Both scenarios incorporate the ideal and nonideal SIC conditions for NOMA, where the nonideal condition includes noise and channel effects.  
As NOMA has not been standardized yet, the simulation parameters in this work follow the LTE standard as stated in the 3 GPP specifications. For the same reason, the basic signal waveform used for the simulations is also taken from the LTE standard. The simulation parameters are defined in Table 3. For simplicity, a single SBS is considered, deployed under an MBS. For the WHT matrix, the same order is used as for the used modulation scheme, e.g., for binary phase shift keying (BPSK) modulation, the order of the WHT matrix is 2 and for QPSK modulation, the order of the WHT matrix is 4. The results according to the scenarios are given as follows.
# Optimizing Foreign Language Learning in Virtual Reality:
## 5 Case Study
### Case Study Two: Rosetta Stone VR
#### 5.2.2 Comparison with Duolingo VR  
When placing Rosetta Stone VR and Duolingo VR side by side, a tapestry of contrasts and parallels unfurls, offering a panoramic view of the burgeoning realm of VR-driven language instruction platforms.  
Both platforms adeptly harness the enveloping nature of virtual reality, but their content delivery strategies must be revised. Duolingo VR, celebrated for its gamification of learning modules, champions a more animated and hands-on approach. Users frequently immerse themselves in scenarios reminiscent of mini-games, marrying entertainment with education. Conversely, Rosetta Stone VR gravitates toward authenticity, embedding learners in genuine-feeling environments. It offers users an enriched cultural dive by endeavoring to mirror actual interactions and settings.  
Their foundational pedagogies further set them apart. Duolingo VR extends snack-sized, modular lessons, empowering users with the autonomy to gravitate toward topics that pique their curiosity. This modularity aligns with the inclinations of contemporary learners for instantaneous and tailor-made content. Rosetta Stone VR, meanwhile, adheres to a more orchestrated and sequential learning trajectory, drawing inspiration from its longstanding commitment to all-encompassing linguistic instruction.  
From a user interface and experience standpoint, both platforms champion straight-forwardness and user-friendliness. Nonetheless, Rosetta Stone VR's incorporation of voice recognition tech, furnishing real-time pronunciation critiques, provides an advantage in interactive instruction.  
Amidst these disparities, a unifying aspiration binds them: their zealous endeavor to utilize VR's transformative potential to amplify the allure and efficacy of language acquisition. Although their techniques and focal points might veer in different directions, both Duolingo VR and Rosetta Stone VR stand as monumental strides in the digital language teaching space, each catering to a spectrum of learner proclivities and requisites.
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### Algorithm-Based Methods
Future work should address these limitations, test the model in varied warehouse setups, and consider integrating IoT and other smart technologies for enhanced decision-making. The algorithm-based methods for trajectory prediction, as outlined in the discussed studies, provide a diverse range of solutions tailored to warehouse security and efficiency. Referring to Table 7, it is evident that a significant portion of the research relies on simulations for validation. The diverse evaluation methods used in these studies, especially the inconsistencies arising from comparing results obtained in controlled simulations, underscore the challenges of directly comparing their outcomes and effectiveness.