esa-sceva/satcom-chunk-collection huggingface dataset:
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 5 Proposed Authentication and Billing Scheme
### Registration and Authentication Phase
Assuming the EV energy demand can be fully covered by the DWC lane owned by \(PO_{i}\), the message exchange sequence between the different entities up to this step is shown in Figure 4.  
```
1:Given\(P_{rated_{i}}\) and \(v_{i}\) for \(PO_{i}\) are provided to the CSC by the CMCS. 2:Input\(E_{pk_{C}}(m_{3}\):\(\{L_{e},reqE,\text{SIG}_{gk_{e}}\})\) from EV \(e\). 3:Decrypt\(E_{pk_{C}}(m_{3})\) using the private key of the CSC. 4:Calculate the number of charging segments, \(N\), required to provide \(reqE\) using Equation (2). 5:if\(N\leq\mathcal{S}_{i}\)then
6: Select a pseudonym \(p\in\mathcal{P}_{i}\) for EV \(e\). 7: Send \(m_{4}\) to EV \(e\) with pseudonym, \(p\), and session key, \(k_{p}\). 8: Activate countdown timer, \(T\). 9:while\(T>0\)do
10: Listen for incoming messages from EV \(e\) and other nearby EVs. 11:if\(E_{pk_{C}}(m_{5})\) is received from \(e\)then
12: Randomly generate \(h_{0}\). 13: Run the hashing algorithm to generate the hash chain, \(\mathcal{H}_{i}^{N}(h_{0})\). 14: Send the \(N\) hash keys back to the EV in \(E_{pk_{e}}(m_{6})\). 15: EV \(e\) is now authenticated and ready to communicate with \(PO_{i}\) and the \(N\) segments for charging activation. 16:endif
17:endif
18:endif
19:endif
20:endif
21: Calculate maximum energy that can be provided from the \(S_{i}\) segments connected to \(PO_{i}\). 22: Communicate the remaining EV energy demand with CMCS to recommend nearest available DWC lane. 23: Send charge insufficiency flag, \(f_{nc}\), to EV \(e\) with the ID of the new PO, \(PO_{j}\), shared by the CMCS. 24: Calculate the number of segment to be activated from the DWC lane owned by \(PO_{j}\), \(N_{j}\). 25: Upon EV approval, share the pseudonyms and session keys to register EV \(e\) with \(PO_{i}\) and \(PO_{j}\). 26: Repeat steps 6-18 for \(N_{j}\) segments belonging to \(PO_{j}\). 27:endif
```  
**Algorithm 1** Proposed EV registration and authentication algorithm at the CSC.
## 7 Conclusions and Limitations  
Our review paper provides a comprehensive analysis of ML-powered information systems (ISs) in multi-objective healthcare management, systematically categorizing and evaluating existing research. We identified six distinct categories, namely diagnostic systems, treatment-planning systems, patient monitoring systems, resource allocation systems, preventive healthcare systems, and hybrid systems, and assessed the investigated papers based on critical parameters, including their main ideas, advantages, disadvantages, simulation environments, and datasets used. Adaptability emerged as the most frequently emphasized parameter, reflecting its crucial role in enabling ML-powered systems to meet dynamic healthcare demands. Additionally, our analysis revealed trends in publication years, with a significant focus on recent studies (2022-2023), and noted that MDPI was the most frequent publisher. Python was identified as the dominant programming language, showcasing its versatility and preference in simulation-based healthcare research.  
Despite the insights provided, our review has certain limitations. First, the scope of our analysis was confined to papers published in select years and databases, which may have excluded other relevant works. Second, while our evaluation included key parameters, such as accuracy, sensitivity, and computational efficiency, future research could expand these criteria to include cost-effectiveness and implementation scalability. Lastly, the lack of standardized benchmarking in many of the reviewed studies poses a challenge to direct comparisons across systems. Addressing these limitations will be essential for advancing research in ML-powered healthcare management. This synthesis lays a foundation for future work in this critical domain, providing a roadmap for both researchers and practitioners to develop innovative, adaptive, and impactful solutions in healthcare management.  
Conceptualization, M.B. ([PERSON]), M.B. ([PERSON]), S.A., M.S.P., P.M. and Y.L.; methodology, M.B. ([PERSON]); software, M.B. ([PERSON]); validation, M.B. ([PERSON]), M.B. ([PERSON]), S.A., M.S.P., P.M. and Y.L.; formal analysis, M.B. ([PERSON]); investigation, M.B. ([PERSON]); resources, M.B. ([PERSON]) and Y.L.; data curation, M.B. ([PERSON]); writing--original draft preparation, M.B. ([PERSON]); writing--review and editing, Y.L.; visualization, M.B. ([PERSON]), M.B. ([PERSON]), S.A., M.S.P., P.M. and Y.L.; supervision, Y.L.; project administration, M.B. ([PERSON]) and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to ethical reasons.  
**Conflicts of Interest:** Author [PERSON] was employed by the company China Huadian Corporation Ltd. (CHD). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Research on Vibration Reduction in the Transmission System of Plastic Centrifugal Pump
## 3 Study of Vibration Reduction in the Pump Shaft
### Vibration Reduction Method and Design
A comparison of the pump shaft transmission system's parameters before and after vibration reduction through the silicone oil damper is summarized in Figure 7.9 The table indicates a significant decrease in both torsional vibration frequency and amplitude after the addition of the damper, highlighting the system's overall effective vibration reduction.
## 2 Materials and Methods
### Discharge Tests  
The discharge testing of the TES systems based on granular material and alumina blocks was carried out starting with the same initial conditions. To that end, the TES systems were heated up using the electric heaters for at least 3 h to an initial temperature of 300-320 \({}^{\circ}\)C, until a homogeneous temperature distribution was reached along the height of the TES system. Once the desired initial temperature was reached and its distribution along the height was uniform, the electric heaters were switched off and the corresponding air flow rate was set. During the discharge process, the evolution of the temperature measured by each thermocouple and the drop in air pressure were registered.  
Considering the difference in size of the TES systems based on granular material and alumina blocks, the air flow rate of each system was selected proportional to the cross-section. Specifically, the volumetric flow rate of air used for the discharge of the TES system based on granular material operated either as a fluidized or as a confined bed was 700 lpm, measured at room temperature, whereas for the TES system based on alumina blocks, the discharge tests were carried out with a volumetric flow rate of air of 135 lpm, also measured at room temperature, as a result of the smaller cross-section of this TES system. Accordingly, the total energy stored by each system, determined as the product of the mass of storage material, either silica sand or alumina, its specific heat, and the temperature difference between the initial and the ambient temperature were also selected for analysis of air flow rates, for a fair comparison of the systems. For the granular material TES system, the 55 kg of silica sand used stored around 3.6 kWh of heat for a temperature difference of 300 \({}^{\circ}\)C,while the 7.5 kg of alumina employed in the solid block TES system stored approximately 0.7 kWh of heat for the same temperature difference, resulting in a ratio of 0.19 for the energy stored by the two TES systems, similar to the ratio of the volumetric flow rates of air. The specific value of 700 lpm for the granular material TES system, corresponding to a gas velocity of 0.35 m/s at room temperature, was selected to avoid severe slugging in the fluidized bed system, which would lead to poor quality of fluidization.  
In addition, the open area for the HTF of the granular material TES system corresponded to the porosity of the silica sand used as storage material, which was measured at 0.36 for the confined bed [25], while the ratio between the channels and the total cross-section of the alumina blocks was 0.31. Therefore, the similar open area, proportional volumetric flow rate of air, and the energy stored in the granular material and solid block TES systems allowed a fair comparison of the discharge processes of these two alternative sensible heat energy storage systems.
# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
###### Abstract  
Nowadays, interactive recommendation systems (IRS) play a significant role in our daily life. Recently, reinforcement learning has shown great potential in solving challenging tasks in IRS, since it can focus on long-term profit and can capture the dynamic preference of users. However, existing RL methods for IRS have two typical deficiencies. First, most state representation models use left-to-right recurrent neural networks to capture the user dynamics, which usually fail to handle the long and noisy sequential data in real life. Second, an IRS always needs to handle millions of items, leading to a large discrete action space in RL settings, which has not been fully addressed by the inefficient existing works. To bridge these deficiencies, in this paper, we propose attention-based tree recommendation (ATRec), an efficient tree-structured policy with attention-based state representation for IRS. ATRec uses an attention-based state representation model to effectively capture the user's dynamic preference hidden in the long and noisy sequence of behaviors. Moreover, to improve the learning efficiency, we propose an efficient tree-structured policy representation method, in which a complete tree is devised to represent the policy, and a novel parameter-sharing strategy is introduced. Extensive experiments are conducted on three real-world datasets and the results show the proposed ATRec obtains 42.3% improvement over some of the state of the arts methods in the hit rate and 21.4% improvement in the mean reciprocal rank of the top 30 ranked items. Additionally, the learning and decision efficiency can also be improved at an average of 35.5%.  
reinforcement learning; deep learning; interactive recommendation system +
Footnote †: journal: Applied Sciences
## 3 Numerical Simulations
### Boundary Conditions and Assumptions  
To study natural ventilation conditions in the model, eight cases were considered in the simulations with changes in the ventilation and space geometry (with and without a partition wall), as can be seen in Table 3.  
The enclosure model dimensions were as follows: width = 3.8 m, length = 9 m, height = 2.75 m, and wall thickness = 0.2 m, with the wall being made from concrete. The dimensions for the partition wall were as follows: length = 1.6 m, height = 2.75 m, thickness = 0.2 m; the wall was made from brick, and the distance from the entrance door was 3.6 m. Two windows of the same geometry had individual ventilation factors of 0.85. The door ventilation had a factor of 2.83. The boundary conditions at the windows and door were treated as open vents. Eight thermocouple trees were placed at the front, middle, and rear of the enclosure, with separation heights of 0.5 m, 1.5 m, and 2.4 m (Figure 5). After assigning the boundary conditions, the mesh calculation was established in auto-size mode with a cell size of 0.2 \(\times\) 0.2 \(\times\) 0.2. However, to achieve the exact result, the mesh of the region surrounding the ventilation openings was refined with a cell size of 0.1 \(\times\) 0.1 \(\times\) 0.1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Vent—Partition Wall** & **Ventilation Conditions** & \(A_{\omega}\sqrt{H_{o}}\left[\sqrt{m^{\ast}}\right]\) \\ \hline
2 & Window 1 & Window 1 open all the time & 0.85 \\  & Partition wall & Window 2 and door closed & 0.85 \\ \hline
3 & Window 1 & Window 1 open at 100 s & 0.85 at 100 s \\
4 & Window 1 & Window 1 open at 100 s & 0.85 at 100 s \\  & Partition wall & Window 2 and door closed & 0.85 at 100 s \\ \hline
5 & Window 2 and door & Window 2 open at 120 s & 0.85 at 120 s \\  & Window 1 closed & & 3.68 at 200 s \\ \hline
6 & Window 2 and door & Window 2 open at 120 s & 0.85 at 120 s \\  & Partition wall & Door open at 200 s & 3.68 at 200 s \\  & Window 1 closed & & \\ \hline
7 & Window 1; window 2; door & Window 2 open at 150 s & 1.70 at 150 s \\  & & Door open at 400 s & 5.38 at 400 s \\ \hline
8 & Window 1; window 2; door & Window 1 open all the time & 0.85 \\  & Partition wall & Window 2 open at 150 s & 1.70 at 150 s \\  & & Door open at 400 s & 5.38 at 400 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ventilation conditions.  
The environment temperature was set to 20 \({}^{\circ}\)C, and the pressure was equal to the atmospheric pressure. To create the HRR curve, the published cone calorimeter and furniture calorimeter experiment data were investigated.  
The fire ignition occurred at 10 s from the start of the simulation. The fuel polyurethane had an HRR of 1500 kW, a t-square curve (\(HRR_{max}\)_at_\(t=179\) s), a fast growth rate phase (\(\alpha=0.0469\)), a steady state of 20 s, and a medium decay rate phase (\(\alpha=0.01172\)), as represented by Figure 6. The total HRR was 297 MJ [17; 18].
# CuInSe\({}_{2}\)-Based Near-Infrared Photodetector
## 3 Results and Discussion  
### CuInSe\({}_{2}\) Thin Film with Low Bandgap  
A CIGS-based photodetector with a wider spectral range extending into the NIR region could be a candidate for an NIR photodetector. For higher sensitivity in the NIR region, a high-quality photodetector operating in the NIR region using low-bandgap CISe thin film must be realized. The bandgap of CIGS (1.04-1.67 eV) is still too high for use in the NIR region. To achieve a bandgap of about 0.95 eV with a cut-off wavelength of 1305 nm, CISe thin films were grown by the full three-stage process with different durations for the second stage. CISe thin films with Cu/In composition ratios of 1.27 to 0.87 were fabricated.  
Figure 2 shows top-view SEM images of CISe thin films grown on the Mo/SLG. The surfaces of CISe films with Cu/In ratios of 0.87 to 0.95 exhibit sharp polyhedral-shaped morphology. The well-faceted polyhedral-shaped grains imply that CISe thin film has a chalcopyrite phase. The grain sizes of CISe films increase as the Cu content increases. For the Cu/In ratios of 0.87 and 0.95, the approximate grain sizes were determined to be about 0.59 and 1.26 \(\mu\)m, respectively. In the Cu-rich state, where the Cu/In ratios are 1.27 and 1.11, the grains exhibited particle-like shapes, and their sizes were 0.91 and 1.14 \(\mu\)m, respectively. The particle-like shapes are ascribed to a large amount of Cu-Se compounds.  
Figure 3a shows the Raman spectra for the CISe layers. The CISe layers with Cu/In ratios of 1.27 and 1.11 exhibit the A1 mode of the Cu-Se compound, which means an excess amount of Cu. As the Cu/In ratio decreased, the E mode of CISe increased. The highest intensity of A1 mode of CISe (with a Cu/In ratio of 1.27) could be due to the effect of the Cu-Se compound. The XRD patterns in Figure 3b indicate that all of the CISe films have a chalcopyrite crystal structure with a preferred orientation of (112). Even the XRD patterns of all samples did not show an apparent intensity difference. The CISe thin film with Cu/In ratio of 0.95 was expected to have the highest crystallinity from the Raman spectra results.  
The refractive index (n) and extinction coefficient (k) values were measured using an ellipsometer. The measured n and k values are shown in Figure 4a. The refractive index and extinction coefficient of CISe layers increased as the Cu/In ratio decreased, which suggests that the bandgap of CISe thin film decreased as the Cu/In ratio decreased. The wavelength-dependent absorption coefficients of the CISe layers were extracted from theextinction coefficients. As shown in Figure 4b, the absorption coefficient increased with the decreasing Cu/In ratio.  
Figure 4: (**a**) Refractive index, n, extinction coefficient, k, (**b**) the absorption coefficients of the CISe layers with variable Cu/In ratios.  
Figure 3: (**a**) Raman spectra and (**b**) XRD patterns of the CISe thin films with different Cu/In ratios.  
Figure 2: SEM images of CISe thin films with Cu/In ratios of (**a**) 1.27, (**b**) 1.11, (**c**) 0.95, and (**d**) 0.87.
## 5 Conclusions  
In this paper, we present a resource allocation technique that is designed to fairly distribute the available time slots among the sensors of a cluster-tree WSN. Our solution is based in convex decomposition and, in particular, it combines the so-called primal and dual decompositions in a single algorithm that provides superior performance in terms of convergence speed. Thanks to the proposed Network Utility Maximization (NUM) formulation, it is possible to choose the most adequate definition of fairness in our application, going from max-min fairness to max-sum rate. As shown in the document, the standard-proposed First Come First Serve policy provides pretty unfair allocations when the traffic load in the network increases and proportional fairness is considered. Moreover, the NUM formulation takes into account the different rate capacities in the network, which are due to different superframe configurations, as well as the radio link qualities by means of the Packet Delivery Ratio.  
Figure 13: Comparison between the CDM and the dual decomposition.  
Throughout the document, we focus our attention on the practical application of the proposed method. On one hand, the conversion form rate (required by the algorithm) to time slots is addressed. On the other hand, the signalling involved in the computation of the global optimal solution is detailed. Specifically, our method requires the double amount of signalling per iteration when compared to dual decomposition. Notwithstanding, the number of iterations can be reduced up to a factor of 1,000 so that the total amount of signalling is reduced about 500 times. Besides, the CDM has no parameter to be adjusted, which is a true impairment in practice.  
In summary, we can conclude that the most remarkable benefits of our solution are:  
* The network can be adjusted to operate with several fairness definitions by fixing the value of \(\gamma\).
* The optimal solution is distributedly computed in the cluster-tree network.
* The price to pay to compute that optimal solution in terms of signalling is minimized with respect to existing techniques.
## 2 Materials and Methods
### Single-Frequency Positioning  
We used a GPS-only single-frequency positioning solution and ignored other GNSS. We decided to choose only GPS constellation and signals because GPS has an almost unchanged constellation of about 30 satellites uniformly distributed over the Earth at medium Earth orbits.  
To estimate coordinates, we used non-smoothed C/A-code pseudo ranges at GPS L1 frequency. The elevation cut-off was 15\({}^{\circ}\). Troposphere delay correction used the Saastamoinen model [29]. To estimate coordinates, we used a typical iterative solution [1].  
We developed software, NAVI [30], that processes RINEX data and calculates geocentric coordinates \(X\), \(Y\), and \(Z\) for single-frequency positioning. In this article, NAVI software used broadcast ephemeris to show a real condition for most users. We had 30 s estimates of coordinates and corresponding positioning errors.
## 4 Evaluation  
### Experimental Methodology  
The experiment involved two basic groups, 68 second year BSc students and 111 factory workers, who assembled freely, without guidance, the customizable modular tablet composed of eight components (as described in Section 3.1). The following setup was used for the experiment: a top-mounted camera that recorded the assembly steps, a table that had the working zone marked with red tape and two images of the tablet (front view and back view) on the left of the area. The tablet's components had their positions marked so that everyone encountered the same setup. Additionally, two laptops were used. One was connected to the camera and one was used for remote access.  
Upon entering the room, the participant was provided with an ID. Since minimal interaction with the subjects was desired, the experiment was controlled from another room. The participants could only listen to a recorded short voice message, which was carefully recorded to prevent any transmission of feelings or emotions. The instructions in the message stated that the tablet should be assembled as indicated in the images and that they were required to use all the components in the assembly process. After the voice message finished playing, the participants started the assembly process. When the subjects considered the product assembled, they made an announcement, so the recording was  
Figure 5: The prediction mechanism of the PPM.  
stopped and they were taken to another room where they filled in a questionnaire. The ID given at the start of the experiment was used in the questionnaire to help link the answers to the recording. There were general questions regarding height, age, gender, dominant hand, highest level of education completed and if they were eyeglass wearers. Other questions were for self-evaluation: \"were you hungry during the experiment?\", \"do you have any prior experience in product assembly?\", \"what was your stress level before the experiment?\", \"how would you describe the state you found yourself in during the experiment (at the beginning, during and at the end of the experiment)\", \"are you under the influence of any drugs that might influence your level of concentration?\", and \"how would you describe the sleep quality of the previous night?\" After answering these questions, they were tasked to complete a perception test.  
After all the assembly steps were encoded, each assembly was ranked with a score that represented the degree of completeness of the tablet. Each correctly placed component was worth 1 point of the final score, with a total maximum of 7. As an example, the score of a subject who assembled all the components, except the screen, was 6/7. Additionally, each assembly had its duration in seconds recorded.  
Two datasets were obtained through the experiment, further denoted as \"Trainees\" and \"Workers\". The \"Trainees\" dataset is composed only of students' assemblies, while the \"Workers\" one contains assemblies made by factory workers.