esa-sceva/satcom-chunk-collection huggingface dataset:
# A Thermodynamic Model for Cryogenic Liquid Hydrogen Fuel Tanks
## 5 Conclusions  
A thermodynamic model was developed to predict the maximum internal pressure of cryogenic liquid hydrogen fuel tanks. The proposed model considers the phase change from liquid to gas hydrogen due to heat transfer from the outside of the tank. The model can perform a transient analysis by applying the fuel consumption rate. The accuracy of the model was verified by dividing it into cases of storing liquid hydrogen and discharging hydrogen to the outside. The proposed model presented good results.  
The pressure in the tank becomes smallest when fuel is consumed immediately after liquid hydrogen fills the tank. However, in the actual operation of a cryogenic fuel tank, a time delay occurs between the completion of charging and the consumption of fuel. Since the behavior of the pressure can be greatly affected by this delay time, it must be taken into account as a major consideration when designing cryogenic liquid hydrogen fuel tanks. The proposed model can consider the operating conditions of fuel tanks. Therefore, it can be concluded that the proposed thermodynamic model is sufficiently usable for predicting the maximum internal pressure and the pressure behavior of cryogenic liquid hydrogen fuel tanks.  
Methodology, D.C.; Validation, D.C.; Formal analysis, D.C.; Writing--original draft, D.C.; Writing--review & editing, S.L. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2022R1A6A1A03056784), and this work was also supported by the 'Space Pioneer Program' grant funded by the Ministry of Science and ICT, Republic of Korea (No. 2021M1A3B9096764).
## 5 Numerical Results of the Spacecraft Attitude Maneuvers  
This section discusses the results of the spacecraft attitude control problem, with the goal of estimating the optimal time required for the SFGIS to complete a reorientation maneuver. Recall that, with reference to the results obtained in the cases of Earth-Venus transfers with constrained clock angles, the reorientation maneuvers were on the order of 60 deg each. Based on that, the minimum settling time \(t_{s}\), defined as the time instant such that \(|e|\leq 0.01\,\)rad for \(t\geq t_{s}\), was estimated with respect to a rotation maneuver of \(1\,\)rad around \(z\). Numerical simulations were performed for values of \(k\) ranging between \(5\times 10^{-8}\,\)rad/s\({}^{2}\) and \(1.2\times 10^{-6}\,\)rad/s\({}^{2}\). To give an idea of the order of magnitude of the numbers used, consider a reorientation maneuver that occurs when the Sun-spacecraft distance is \(r=r_{\oplus}\), assuming \(b=0.2\,\)m, \(\gamma=0.125\), and \(m_{\rm sail}=5\,\)kg. In this case, we obtain \(k\simeq 9.1724\times 10^{-8}\,\)rad/s\({}^{2}\). The simulation results for this particular case are shown in Figure 17, from which it can be seen that the maneuver takes about \(120\,\)min to complete.  
Note that the angle \(\delta\) reached the reference value through a time response having a maximum peak slightly greater than one, while the input to the system revealed that the control law was essentially of bang-off-bang type. These results are in agreement with what Wie found using a nonlinear PID control logic [37]. In all cases analyzed for different values of \(k\), control laws characterized by a limited maximum peak and with bang-off-bang inputs were always found. Figure 18 shows the minimum settling time \(t_{s}\) to perform a rotation of \(\delta_{\rm ref}=1\,\)rad around \(z\) as a function of \(k\).  
Figure 16: Closed-loop control scheme with saturation of the input to the system.  
As might be expected, the time of the optimal reorientation maneuver decreased as \(k\) (and thus control angular acceleration) increased. For example, \(t_{s}\simeq\{136,\,56,\,28\}\) min were obtained when \(k=\{5,\,30,\,120\}\times 10^{-8}\,\mathrm{rad}/\mathrm{s}^{2}\), respectively. Finally, Figure 19 shows the variation in the optimal control parameters of the PDF controller with \(k\).  
Figure 17: Results of the numerical simulation when \(k\simeq 9.1724\times 10^{-8}\,\mathrm{rad}/\mathrm{s}^{2}\).  
Figure 18: Minimum settling time \(t_{s}\) to perform a rotation of \(\delta_{\mathrm{ref}}=1\,\mathrm{rad}\) around \(z\) as a function of \(k\).
# An Energy Centric Cluster-Based Routing Protocol for Wireless Sensor Networks
## 4 The Protocol Details
### Cluster Setup Phase  
Generally, the number of clusters in a network may vary in several factors such as nodes' energy, correlation of data, types of sensor nodes, size of the network, and the distance of the BS in different applications of WSNs [5; 6; 28; 29; 30]. Our proposed network is divided into a number of virtual grids. The nodes deployed in the grids are grouped into clusters that are static. Here, the number of clusters is defined as in Equation (3) according to [6]. The network deployment stage starts at time \(T_{1}\), where, the BS broadcasts a _Hello_Msg\({}_{1}\)_ to all the nodes \({}_{\text{S}}=\{s_{1},s_{2},s_{3},\ldots,s_{\text{N}}\}\) at a certain power level. From the received signal of the message, each node \(s_{i}\) can compute the approximate distance to the BS as \(d(s_{i},BS)\) based on the received signal strength [31]. Then each node broadcasts a _Hello_Msg within the  
Figure 1: Flowchart of energy-centric cluster-based routing (ECCR) operation, including cluster setup, cluster-head election and data transmission.  
transmission range \(R_{\text{d}}\) (diagonal length of a grid) with the following two values: self _id_ and cluster _id_. At the same time, it receives the _Hello_Msgs_ from its neighbor nodes within the same cluster and updates its cluster members' table CMT. The detail procedure of this phase is given by the pseudo-code in Algorithm 1.  
\[\#clusters=\sqrt{\frac{N}{2\pi}}\times\frac{M}{d_{avg}(CHs,BS)} \tag{3}\]  
where \(M\) is the length of the sensor field, \(d_{avg}(CHs,BS)\) is the approximate average distance from the prospective cluster heads to the \(BS\) and \(CH\) is denoted as cluster head.  
```
1:start (information collection)
2:while (\(T_{1}\) has not expired) do
3:\(BS\) broadcasts _Hello_Msg_1
4: Nodes receive the message
5: Measure distance from \(BS\)
6:\(s_{i}\)_state \(\leftarrow\) Normal
7: Broadcast _Hello_Msg_
8: Receive and update CMT
9:end
10:end
```  
**Algorithm 1**Cluster Setup
# Traffic Classification in Software-Defined Networking Using Genetic Programming Tools
## 2 Material and Methods
### Theoretical Framework
#### 2.1.3 ML-Based Traffic Classification Methods in SDN and Related Works
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Authors and Year** & **Challenge** & **ML Model** & **ML Algorithms** & **Type of Traffic** & **Dataset** & **Features** & **Metrics** & **Controller** & **Topology** & **Accuracy** \\ \hline [PERSON] et al. [28, 29] & QG issues & Supervised learning & RF, NNN, MLP, NB, LR, SVM & Voice, video, IoT traffic (ppels) & USW, SVM, LSTM, FP-2016 & Statistical features (post), NN, MLP, packet & TF, FP, NN, accuracy, & Tree and branch & 9\% \\ \hline [PERSON] et al. [26] & [PERSON] et al. [24, 2023] & [PERSON] et al. [24, 2022] & Deep management & Learning & DNN, CNN, LSTM, and deep autoencoder & Real-time apps & NMB, SDN & Low size, protocol, app type, & Accuracy, loss flow duration, and more & Accuracy, loss metrics, run time \\ \hline [PERSON] et al. [2], [2] & [PERSON] et al. [24, 2023] & [PERSON] et al. [24, 2022] & Supervised learning & RF & Chat, email, file transfer, streaming, P2P, WiL, HTTP & ISCC, VPN-2016 & Average packet & F-score, throughput, distribution of packet & F-score, throughput, confusion matrix & RPV & Simple diamond \\ \hline [PERSON] et al. [22] & Application et al. [22] & Application et al. [22] & Supervised learning & KNN, LR, RF, DT, NB, and SVM & WWW, DNS, FT, KGBF, P2P, and VOP & SVM-traffic, KGBF, P2P, and VOP & Learning of flash, Knowledge and frequency transfer products & Accuracy, F-score, RNN, and training time & Straight-forward & 99.8\% \\ \hline [PERSON] et al. [22, 2023] & Application et al. [22] & Deep and security & Learning & LSTM, BLSTM, GRU, and BRCU & Multimedia, VoIP, BLSTM, LSTM message, Attack & InSDN and F3 NNOVYN & 12 features & Accuracy, precision, recall, and F1-Score & & 99.6\% \\ \hline [PERSON] et al. [22], [220] & Network et al. [220] & Network et al. [220] & Unsupervised and management & and supervised ML & RF, RNN, DT, SVM & Four types of traffic & Publicly available & MAC addresses and ports, count, low product count, low product count, and average packet size & Accuracy & RPV & Simple & 96.3\% \\ \hline [PERSON] et al. [24], [2202] & QG issues & Semi-supervised learning & Feed-forward machine & RF, NN, NB, and LR & HTTP, video streaming, FT, P2P, instant messaging & ANT and Kaggle dataset & Packet size, packet & Confusion matrix, prediction, recall, and F-Measure & & & \\ \hline [PERSON] et al. [24] & Application et al. [22020] & Application et al. [22020] & Supervised learning & SVM, NB, nearest centroid & HTTP, small, streaming & Own collected data & suspicion, support, group, data & Accuracy, precision, model, ground, path, road, traffic, training and testing & Accuracy, precision, recall, F-score, training and testing & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: A summary of research works and their findings.
# Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM
## 2 Methodology
### Improved Sparrow Search Algorithm
#### 2.3.1 Improved Tent Chaotic Mapping  
The initial populations of the SSA are randomly generated, leading to an uneven distribution of sparrow populations in the search space. This lack of population diversity can be addressed by using chaotic mapping techniques. Logistic and Tent mapping are algorithm optimization technologies that are highly regarded among researchers. Tent chaotic mapping, renowned for its simple form, tunable parameters, and reversibility, distinguishes itself among a myriad of chaotic mappings. However, the iteration sequence of Tent mapping may contain small cycles and unstable cycle points. Therefore, a random variation factor rand(0,1) was introduced in the Tent mapping strategy to increase the diversity of the initial population according to the literature [31]. Equation (1) is the improved Tent expression:  
\[x_{n+1}=\begin{cases}2x_{n}+\text{rand}(0,1)\times\frac{1}{T},&0\leq x_{n}\leq \frac{1}{2}\\ 2(1-x_{n})+\text{rand}(0,1)\times\frac{1}{T},&\frac{1}{2}<x_{n}\leq 1\end{cases} \tag{1}\]  
The expression after the Bernoulli transform is:  
\[x_{n+1}=(2x_{n})\text{mod}1+\text{rand}(0,1)\times\frac{1}{T} \tag{2}\]  
\(T\) denotes the number of particles in the Tent mapping. Figure 1 displays the initial distribution of chaotic sequences generated by Logistic, Tent, and improved Tent chaotic mapping in a 2D region. It is evident that the improved Tent chaos mapping exhibits better distribution uniformity.  
Figure 1: Initial distribution of chaotic sequences.
# Robustness Analysis of the Collective Nonlinear Dynamics of a Periodic Coupled Pendulums Chain
## 3 Numerical Examples
### Example 2: Three Coupled Pendulums
#### 3.2.1 Deterministic Study  
Deterministic modal and physical responses are illustrated by Figure 11. The symmetry between the first and the third pendulum generates conformity between their physical responses.  
Modal interactions and bifurcation topology transfer between the pendulums' responses, which are driven by the collective dynamics, generate up to six stable solutions at several frequencies in the multistability domain. The obtained stable branches are classified as Double (DM) and Triple mode (TM) solutions. The DM branches correspond to the solutions of the first and third amplitudes (red curves), whereas the TM solutions result from the coupling between the pendulums (blue curves). Three DM solutions (DM-RB, DM-NRB, DM-B1 and DM-B2) and two TM solutions (TM-B1 and TM-B2) are obtained, Figure 11a. A correspondence in term of bifurcation points between the amplitudes with respect to each branch type is observed. This is generated by the bifurcation topology transfer.  
Figure 12 illustrates the contributions of the attractors of the six stable branches in the basins of attraction. The basins are plotted for \(\sigma=-1\), in the Nyquist plane \(\left(\left(a_{1}\right)_{0},\left(b_{1}\right)_{0}\right)\) for arbitrary chosen initial conditions \(\left(a_{2}\right)_{0}=\left(b_{2}\right)_{0}=\ 0.2\) and \(\left(a_{3}\right)_{0}=\left(b_{3}\right)_{0}=\ 0.3\). Figure 12 shows also the responses amplitudes, which can be identified through the color bar.  
Figure 11: Deterministic (**a**) modal responses amplitudes \(|A_{1}|^{2}\), \(|A_{2}|^{2}\) and \(|A_{3}|^{2}\) and (**b**) physical responses amplitudes \(\phi_{1}\), \(\phi_{2}\) and \(\phi_{3}\), of the pendulums.  
The bifurcation topology transfer between the responses generates a correspondence between the attractors in the three basins of attraction. Indeed, when \(\left|A_{1}\right|^{2}\) and \(\left|A_{3}\right|^{2}\) jump between DM-RB, DM-NRB, DM-B1 and MD-B2, \(\left|A_{2}\right|^{2}\) is stabilized on DM. Moreover, a correspondence exists between the TM attractors (TM-B1 and TM-B2) of the three pendulums responses.  
The DM attractors are the most robust since they dominate the basins with 51% of their global size. The contribution of the TM attractors is nearly similar (49%). Focusing on DM, the DM-B2 attractors, occupying nearly 35% of the basins, are the most robust. Similarly, the TM-B2 attractor occupies 33%. The lowest contribution is of the DM-RB attractors representing less than 2% of the global basins size.
## 4 Results and Discussions
### Influence of Structural Parameters
#### 4.2.3 Slant Angle of the Acquisition Nozzle  
With the other parameters are constant, the velocity contours inside the hydraulic collector at different slant angles of the acquisition nozzle (\(\beta\)) are shown in Figure 20. When \(\beta\) varied between 41\({}^{\circ}\) and 53\({}^{\circ}\), the average outlet flow velocity of the conveying channel (\(v_{ao}\)) varies less and was above 3 m/s. How the collection efficiency (\(\eta\)) varied with \(\beta\) is plotted in Figure 21.  
As can be seen from Figure 20, when \(\beta\) was less than 45\({}^{\circ}\), the two acquisition jets, in addition to forming an upward flow after scouring the ground, formed a backward flow at the bottom of the hydraulic collector. As a result, some of the nodules were washed away by this flow before they could be lifted to a sufficient height, which is similar to that shown in Figure 16. This also makes the collection efficiency (\(\eta\)) extremely low when the \(\beta\) was less than 45\({}^{\circ}\), as shown in Figure 21. Figure 20 also shows that, when \(\beta\) was greater than 51\({}^{\circ}\), the reflected upflow begins to move forward, i.e., it is no longer directly toward the conveying channel. This prevents some of the nodules from successfully entering the conveying channel, which, in turn, results in a decrease in \(\eta\), as shown in Figure 21. According to Figure 21, the hydraulic collector has a high collection efficiency when \(\beta\) is between 45\({}^{\circ}\) and 49\({}^{\circ}\).  
Figure 19: Collection efficiency (\(\eta\)) and average outlet flow velocity of the conveying channel (\(v_{ao}\)) vs. the center distance between the front and rear acquisition confluence tubes (\(L\)).  
Figure 20: Velocity contours of the hydraulic collector at different slant angles of the acquisition nozzle (\(\beta\)).
# Weakly Supervised Learning for Object Localization Based on an Attention Mechanism
## 2 Related Work
### Weakly Supervised Object Localization  
Weakly supervised learning (WSL) has recently received a lot of attention in the fields of computer vision and image processing. WSL is a field for training deep learning models with only labels that are cheaper than the labels required for supervised learning. For example, to perform 3D object reconstruction, Voxel or mesh level labels are required in supervised learning, but in WSL, a pixel or bounding box level that is cheaper than the labels required in supervised learning is required. In this field of WSL, Weakly supervised object localization (WSOL) mainly aims to localize a single object in each given image scene. In a previous paper [1] related to the WSOL, object localization was performed by mapping the class score predicted in the inference stage and the feature map of the last layer of the feature extractor. However, this method [1] encountered the suboptimal problem of locating only the main points without locating the entire object. This was because, in supervised learning, the object location is given as a label to perform the object localization task, whereas in WSOL, only the class of the object that exists in the input image is simply given as a label. In order to solve such research issues, in previous papers [8; 9], some improvements were made by adding a preprocessing process for the input data or adding specific layers. In addition to this, previous papers [10; 23; 24] tried to solve the sub-optima problem through various methods.
###### Abstract  
This paper presents a newly designed bistable S-type generator beam. For two typical energy harvesting scenarios in a low-frequency excitation environment (up-and-down vibration excitation and wind-induced vibration excitation), two kinds of energy harvesting devices are designed using this S-type generator beam and tested and analyzed, respectively. The results indicate that the S-type generator beam can make full use of materials to avoid premature fatigue failure caused by stress concentration. The peak stress of the S-type generator beam is 34.3% lower than that of the cantilever beam under the same excitation conditions. Furthermore, when the environmental excitation frequency is equal to the third natural frequency (3 rd mode, 7.45 Hz) of the generator beam, the motion process of the beam surpasses the potential energy barrier and generates inter-well movement (snap-through). The peak output voltage in the two different environments is 14,350 mV and 17,630 mV, respectively. Additionally, the optimal external resistance of the circuit was determined to be 22 k\(\Omega\), with a power output of the energy collector of 0.534 mW and 0.545 mW, respectively.  
low frequency; S-type; bistable; energy barrier Article
# An IoT-Based COVID-19 Prevention and Control System for Enclosed Spaces
## 4 Results and Discussion
### Privacy and Compliance  
Tracking is fundamental to the system. Individuals are worried about violations of their privacy by pandemic prevention and control policies. This inevitably makes many policies impossible to implement in practice. In the system, we try to avoid obtaining data with identity information.  
IBeacon advertisements from iBeacon nodes only include Universally Unique Identifier (UUID), major and minor values. This is unidirectional broadcasting, and there is no mutual communication between an iBeacon node and a mobile phone. Hence, iBeacon devices do not collect any private information from users' devices, acting like a lighthouse. As shown in Figure 2, a mobile phone can get RSSI values from nearby iBeacon nodes, and the corresponding iBeacon node to a received RSSI value is known. The mobile phone forwards the RSSI values to server, and the server then returns a predicted coordinate. The server needs to clarify which device each positioning request comes from, which is a sine qua non for risk assessment. A possible solution is to ask a user to register and log in when using the system, and the system hides identity information. The registered account can be an identifier for each person. The server will store the track and time information of each user; once a user is identified as a COVID-19-positive individual by medical institutions and the information is input on the system by the authorities, the server will send alerts to related people. In the whole process, only medical institutions and the authorities will access private information, and the system only keeps an account, meaning the manager of the enclosed space is not able to access identity data of people. The system is more applicable to some places with strict management, such as airport terminals for entry and exit administration, children's play areas, and factory workshops.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Graph Scale** & **Speedup Ratio** & **Is Parallel Algorithm Faster** \\ \hline (10, 40) & 0.674 & No \\ (50, 200) & \(1.158\) & Yes \\ (200, 1000) & 1.645 & Yes \\ (1000, 5000) & 2.421 & Yes \\ (5000, 25,000) & 2.499 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Speedup effects of different scales of a graph.
## 6 Conclusions  
This work introduces DLMinTC+, a DL-based algorithm designed to address the MinTCover problem on temporal graphs. Our approach centers on two primary goals: \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Graph** & 1E1 & 1V1 & 1T1 & **D** & **DLMinTC+ RAM (GB)** & **FastMinTC+ RAM (MB)** \\ \hline aves-sparrow-social & 516 & 52 & 1 & \(3.90\times 10^{-1}\) & 0.3 & 0.1 \\ aves-wildbird-network & 11,900 & 202 & 5 & \(1.17\times 10^{-1}\) & 0.7 & 0.4 \\ copresence-InvS13 & 394,247 & 95 & 20,128 & \(4.39\times 10^{-3}\) & 7.8 & 24.8 \\ copresence-InvS15 & 12,838,194 & 219 & 21,535 & \(2.50\times 10^{-3}\) & 8.2 & 26.6 \\ copresence-LH10 & 150,126 & 73 & 12,604 & \(4.53\times 10^{-3}\) & 5.5 & 20.5 \\ copresence-IvSchoonol & 6,594,492 & 242 & 3123 & \(7.24\times 10^{-2}\) & 4.6 & 16.4 \\ copresence-SFHH & 1,417,485 & 403 & 3148 & \(5.56\times 10^{-3}\) & 3.8 & 16.7 \\ copresence-Thiers13 & 18,613,039 & 328 & 8937 & \(3.88\times 10^{-2}\) & 8.4 & 19.3 \\ email-dnc & 392,264 & 1892 & 19,382 & \(1.13\times 10^{-6}\) & 6.8 & 18.7 \\ fb-wosn-friends & 1,269,502 & 63,731 & 736,674 & \(8.49\times 10^{-10}\) & 12.7 & 45.2 \\ ia-contacts-dublin & 415,912 & 10,972 & 76,943 & \(8.98\times 10^{-8}\) & 11.4 & 41.6 \\ ia-contacts-hypertext2009 & 20,818 & 113 & 5245 & \(6.27\times 10^{-4}\) & 5.5 & 15.8 \\ ia-diggs-reply & 87,627 & 30,398 & 83,942 & \(2.26\times 10^{-9}\) & 10.8 & 38.0 \\ ia-hospital-ward-proximity & 32,424 & 75 & 9452 & \(1.24\times 10^{-3}\) & 5.3 & 13.4 \\ ia-twenty-pol & 8,157 & 18,470 & 60,509 & \(5.93\times 10^{-9}\) & 11.3 & 39.8 \\ insecta-ant-colomy1 & 111,578 & 113 & 40 & \(4.41\times 10^{-1}\) & 1.5 & 6.1 \\ insecta-partos-pexit & 2,180 & 160 & 40 & \(4.74\times 10^{-1}\) & 1.8 & 8.5 \\ insecta-ant-colomy5 & 194,317 & 152 & 40 & \(4.23\times 10^{-1}\) & 1.5 & 7.3 \\ armalia-tortoise-network-by & 1997 & 24 & 51 & \(1.42\times 10^{-1}\) & 0.6 & 0.4 \\ SFHH-conf-sensor & 70,261 & 403 & 3508 & \(2.01\times 10^{-2}\) & 0.4 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results on real-world publicly available graphs from Dataset3. For each considered graph, we report information on the memory consumption of DLMinTC+ and FastMinTC+. Note that for DLMinNC+, memory is expressed in GB, while it is expressed in MB for FastMinTC+.