esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Ventilation Rate
#### 3.1.1 Controlled Tests
Finally, it is interesting to notice that the ACH results obtained at different measurement points are fairly consistent, with differences between the maximum and minimum estimated ACH of less than 30% of the mean value, except for test 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Box Model Adjustment Results For the Controlled Tests**} \\ \hline
**Test** & **Monitor** & **ACH [h\({}^{-1}\)]** & **Co\({}_{\textbf{0}}\) [ppm]** & **C\({}_{\textbf{eq}}\) [ppm]** & **RMSE [ppm]** & **\(\overline{\textbf{ACH}}\) [h\({}^{-1}\)]** & **\(s_{\textbf{ACH}}\) [h\({}^{-1}\)]** \\ \hline \multirow{4}{*}{\(1\) (\(10/09\))} & AQ1 & 3.44 & 1744 & 500 & 56.87 & \multirow{4}{*}{3.03} & \multirow{4}{*}{0.77} \\  & AQ2 & 1.88 & 1816 & 350 & 12.16 & & \\  & AC1 & 3.32 & 1690 & 500 & 53.12 & & \\  & AC2 & 3.49 & 1625 & 500 & 51.73 & & \\ \hline \multirow{4}{*}{\(2\) (\(10/09\))} & AQ1 & 12.12 & 2313 & 433 & 74.82 & \multirow{4}{*}{13.16} & \multirow{4}{*}{1.48} \\  & AQ2 & 11.90 & 2189 & 497 & 18.71 & & \\ \cline{1-1}  & AC1 & 13.52 & 2063 & 500 & 28.64 & & \\ \cline{1-1}  & AC2 & 15.11 & 1898 & 488 & 36.34 & & \\ \hline \multirow{4}{*}{\(3\) (\(15/10\))} & AQ1 & 16.70 & 1758 & 500 & 7.83 & \multirow{4}{*}{16.63} & \multirow{4}{*}{1.99} \\  & AQ2 & 15.08 & 1842 & 500 & 23.96 & & \\ \cline{1-1}  & AC1 & 15.32 & 1795 & 500 & 39.87 & & \\ \cline{1-1}  & AC2 & 19.42 & 1915 & 500 & 41.78 & & \\ \hline \multirow{4}{*}{\(4\) (\(15/10\))} & AQ1 & 14.70 & 1507 & 500 & 25.83 & \multirow{4}{*}{14.95} & \multirow{4}{*}{1.68} \\  & AQ2 & 13.38 & 1383 & 500 & 31.17 & & \\ \cline{1-1}  & AC1 & 14.39 & 1496 & 500 & 25.67 & & \\ \cline{1-1}  & AC2 & 17.33 & 1465 & 500 & 43.49 & & \\ \hline \multirow{4}{*}{\(5\) (\(15/10\))} & AQ1 & 9.32 & 1826 & 477 & 35.86 & \multirow{4}{*}{10.09} & \multirow{4}{*}{1.72} \\  & AQ2 & 8.06 & 1645 & 415 & 40.67 & & \\ \cline{1-1}  & AC1 & 11.85 & 1836 & 449 & 77.67 & & \\ \cline{1-1}  & AC2 & 11.13 & 1786 & 500 & 73.92 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Adjustment results for the box model parameters for each test and monitor. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Mean Environmental Conditions During the Controlled Tests**} \\ \hline
**Test** & **T\({}_{\textbf{in}}\) [\({}^{\textbf{c}}\)]** & **RH\({}_{\textbf{in}}\) [\%]** & **T\({}_{\textbf{out}}\) [\({}^{\textbf{c}}\)]** & **RH\({}_{\textbf{out}}\) [\%]** & **_U_** [m/s]** & **\(\varphi\) [\({}^{\textbf{c}}\)] \({}^{\textbf{+}}\)** \\ \hline \(1\) & 18.6 & 67.7 & 14.8 & 96.3
## 2 Case Study: Closely Spaced mmWave MIMO Systems
### Design 2  
The second design is shown in Figure 3. It also consists of two identical monopole antennas sharing a common ground plane and dielectric material. Both monopoles are individually fed via probe feeds. However, here, the monopoles are facing the opposite directions, i.e., \(+z\) and \(-z\). The dimensions and the substrate materials are the same as for Design 1. Figure 4 shows the reflection and transmission coefficients for Design 2. The mutual coupling ranges from \(-9\) to \(-16\) dB within the frequency range of interest (24 GHz to 27 GHz).  
Figure 1: Geometry configuration for Design 1: (**a**) side view, (**b**) perspective view, (**c**) top view, (**d**) bottom view.  
Figure 3: Geometry configuration for Design 2: (**a**) side view, (**b**) perspective view, (**c**) top view, (**d**) bottom view.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 6 Discussion
### Pareto Optimality
The outliers at 80 iterations and 12-14 dB exist due to an early exit criterion stepping in at fewer executed iterations, far less than the maximal allowed number. The number of allowed iterations can be reduced to reflect the Pareto optimal points below 12- and beyond 14 dB.
# Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks
###### Abstract  
Traffic parameter characteristics in congested road networks are explored based on traffic flow theory, and observed variables are transformed to a uniform format. The Gaussian mixture model is used to reconstruct route trajectories based on data regarding travel routes containing only the origin and destination information. Using a bi-level optimization framework, a Bayesian traffic demand estimation model was built using route trajectory reconstruction in congested networks. Numerical examples demonstrate that traffic demand estimation errors, without considering a congested network, are within \(\pm 12\); whereas estimation demands considering traffic congestion are close to the real values. Using the Gaussian mixture model's technology of trajectory reconstruction, the mean of the traffic demand root mean square error can be stabilized to approximately 1.3. Traffic demand estimation accuracy decreases with an increase in observed data usage, and the designed iterative algorithm can predict convergence with 0.06 accuracy. The evolution rules of urban traffic demands and road flows in congested networks are uncovered, and a theoretical basis for alleviating urban traffic congestion is provided to determine traffic management and control strategies.  
traffic network; demand estimation; congested networks; trajectory reconstruction; Bayesian Article
# Key Points' Location in Infrared Images of the Human Body Based on Mscf-ResNet
###### Abstract  
The human body generates infrared radiation through the thermal movement of molecules. Based on this phenomenon, infrared images of the human body are often used for monitoring and tracking. Among them, key point location on infrared images of the human body is an important technology in medical infrared image processing. However, the fuzzy edges, poor detail resolution, and uneven brightness distribution of the infrared image of the human body cause great difficulties in positioning. Therefore, how to improve the positioning accuracy of key points in human infrared images has become the main research direction. In this study, a multi-scale convolution fusion deep residual network (Mscf-ResNet) model is proposed for human body infrared image positioning. This model is based on the traditional ResNet, changing the single-scale convolution to multi-scale and fusing the information of different receptive fields, so that the extracted features are more abundant and the degradation problem, caused by the excessively deep network, is avoided. The experiments show that our proposed method has higher key point positioning accuracy than other methods. At the same time, because the network structure of this paper is too deep, there are too many parameters and a large volume of calculations. Therefore, a more lightweight network model is the direction of future research.  
infrared images; key point location; residual network Article
# High-Fidelity Fin-Actuator System Modeling and Aeroelastic Analysis Considering Friction Effect
## 5 Aeroelastic Analysis Results and Discussion
### Aeroelastic Characteristics of the Fin-Actuator System with Freplay  
#### 5.3.1 Influence of Different Initial Deflection Angle with \(e=0.2^{\circ}\)  
The initial conditions will affect the stability of nonlinear systems. The half width of freeplay at the fin shaft was \(0.2^{\circ}\), and the initial fin shaft deflection angles \(\delta_{0}\) were \(0.5^{\circ}\), \(1^{\circ}\), and \(2^{\circ}\), respectively. The corresponding actuator dynamic stiffness under different \(\delta_{0}\) was calculated, and the results are shown in Figure 14.  
Figure 14: The influence of \(\delta_{0}\) with \(e=0.2^{\circ}\), where (**a**) was the influence of \(\delta_{0}\) on the actuator dynamic stiffness and (**b**) was the influence of \(\delta_{0}\) on the response of the fin deflection angle at different flight speeds.  
Figure 13: The frequency domain flutter results of the system without freeplay and friction.  
It can be discovered from Figure 14a that as \(\delta_{0}\) increases, the gain of the dynamic stiffness also increased greatly, but it was not proportional to the increase in \(\delta_{0}\), and the increase in phase was very small. In Reference [55], the actuator dynamic stiffness was obtained by experiments and simulations in a series of cases including changing the initial preload. The conclusion was consistent with the change of the initial angle in this paper. However, flutter analysis was not carried out in Reference [55]. Actually, \(\delta_{0}\) will also affect the aeroelastic characteristics of the system, which can be seen in Figure 15b. \(\delta_{0}\) will affect the speed at which the fin begins to oscillate. As \(\delta_{0}\) increases, the oscillation occurs at smaller flight speed, and the oscillation amplitude becomes larger. However, different \(\delta_{0}\) have little effect on \(A/e\)--namely, the ratio of oscillation amplitude of the fin to the width of freeplay at the same flight speed. It only has some influence on \(A/e\) at the beginning of oscillation.  
When \(\delta_{0}\) is \(1^{\circ}\), the comparison of the frequency domain and time domain results is shown in Figure 15, which shows that these results were highly consistent and the maximum error did not exceed 1.2%. As the flight speed increased, the oscillation amplitude suddenly increased at a certain speed, which means that flutter occurred. The oscillation frequency increased slowly within a small range with the increase in flight speed.
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
## 3 Materials and Methods  
This section describes the overall architecture and operational mechanisms of the proposed intelligent pre-warning system for fishing vessels. The system is designed to monitor vessel motion and environmental data in real time, as well as evaluate safety risks based on dynamic parameters and provide timely alerts to crew members.
## 3 Results
### SFE Screening Experiments  
Table 3 shows the results of the preliminary SFE runs, performed for screening the effect of temperature (\(T\)), pressure (P), methanol concentration (_MeOH_%), and CO\({}_{2}\) flow rate (\(q_{CO2}\)) on the individual and the total phenolic recovery (TPR) from potato peels. The literature reports the testing of vast ranges for all these parameters, but especially for temperatures (30-120 \({}^{\circ}\)C) and pressures (80-650 bar) [32]. Since no studies specifically on the extraction of potato peels by SFE are available in the literature, the values of these four factors were decided based on works that assessed the extraction of phenolics from other vegetable waste matrices by SFE, such as grapes [33], red pitaya [34], orange [35], lemon [36], and apple [37]. For these materials, it was observed that all the optimized conditions for TPC were usually pinpointed at the lower end of the range of temperatures and pressures employed (40-50 \({}^{\circ}\)C and 100-250 bar). This is likely due to the fact that lower temperatures and pressures result in a lower solvation power of the supercritical CO\({}_{2}\). Higher temperatures, on the other hand, may lead to diminishing returns (i.e., considerable increase in energy consumption resulting in marginal solvation gains), or even the degradation of more thermolabile compounds [38]. Looking at E1 and E2, where the four factors being studied are at their lowest values, it is clear that low CO\({}_{2}\) flow rates were not effective for extracting phenolics from the matrix, even when the \(T\) and \(P\) were slightly increased (E2). In E1, the recovery of caffe acid is only 3%, and the total phenolic recovery, 0.5%. This is probably owing to potato peels having a higher starch concentration than the other fruit matrices mentioned. Due to the more complex arrangement of structural polysaccharides such as fibers and starch, the dissolution of certain compounds in the supercritical fluid, e.g., phenolics and tannins, can be made difficult, especially if there are chemical interactions involved [39]. In this scenario, the supercritical CO\({}_{2}\) may present a lower relative matrix penetration capacity, and this effect is amplified due to the low values of the \(T\) vs. \(P\) binomial, _MeOH_%, and \(q_{CO2}\) employed in these runs, which result in low solvation power, low solvent polarity, and low mass transfer rates from the matrix to the fluid phase, respectively [38]. Solely increasing the solvation power of the solvent (via \(T\) and \(P\)) in E2 does not impart significant effects to the extraction yield or to the phenolic profile, as the only compound recovered in both runs was caffe acid (CFA), and at very low concentrations. From E3 to E5, all the investigated parameters were substantially increased and the extraction efficiency started to show noticeable improvements. In E3, \(T\) was further increased and \(q_{CO2}\) was doubled, which caused a considerable improvement in the extraction yields. Increasing the latter parameter seems to increase the TPR owing to a more efficient
## 4 Snake Robot Ethical Issues in SAR Response Phase
### Society-Robot Relationship in SAR Response Phase  
In the eyes of society, robots are nonliving objects that are given certain tasks to complete. In the future, service robots will be a part of society, and people may view them as companions and develop emotional ties with them. For instance, many people today can no longer survive even for small periods of time without their electronic gadgets. Moreover, some people have an emotional attachment to material possessions (such as toys). It is unclear how society will ultimately see robots in the future. Although society may be open to the idea of robots performing normal tasks, it is not apparent whether this is the case with SAR operations. Will society accept robots to perform tasks? We try to look at society-robot relationship issues by considering the following ethical aspects:  
* Nostalgia and conservatism: People throughout the world hold diverse societal beliefs and ideologies towards the use of technology. In some regions, people are enthusiastic about embracing new technologies like robots. For instance, some people enjoy using services like food cooked by robots, although this may not be the case for other people. In future, robot behaviour can be regarded as a humanlike entity with increased cognitive capabilities [94]. Likewise, the possibility that a particular society also accepts SAR robots in times of disaster is debatable.
* Trust: For society to embrace technology, it must be trusted; even for the human rescue team, complex SAR missions are challenging. Therefore, will society have enough trust in robotic rescuers?
* Expectation: Considering that technology has advanced to the point where robots can be used in SAR and have gained societal acceptance, then there will be a problem with people's expectations of the robot [94]. Robots might have a limited range of capabilities for certain rescue operations, and their decisions may sometimes conflict. It matters what level of expectation the victims can place on robots.
# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
## 4 Evaluation Setup
### Evaluation Metrics and Parameters  
We assessed throughput as the key performance metric, determined by analyzing the end-to-end TCP ACK numbers returned by the receiver. Additionally, we measured download completion time (DLCT) for a 300 MB file download. When calculating the throughput of individual paths, we used the in-built tracing of the user-space framework, which logs packet transmissions and receptions. To evaluate the factors that impact the ability of our multipath solution to achieve throughput aggregation and reduce download times using a particular combination of CC algorithms, we analyzed the impact of the following: (i) end-to-end RTT; (ii) the position of the tunnel proxy; (iii) the size of the MAC-layer bottleneck buffer; (iv) asymmetry in the path delay; (v) the presence of CoDel at the bottleneck.
## 3 Spectrum Auction Structures
### English Auction  
The English auction is the most recognizable format. Intuitively, one thinks of this type of auction as a scenario in which participants compete to purchase a generic item through bidding. Ultimately, the item is awarded to the individual who places the highest bid.  
This auction format may or may not include a reserve price, which is the minimum bid value required to initiate the bidding process. The starting price can be set at either \(p^{0}=0\) or \(p^{0}=X\), the representative price in round zero. Participants who wish to continue competing for the auctioned item must increase their bids to surpass those of their opponents.  
Let \(S_{i}\) be the action space for each player, and let the Cartesian product of these spaces be denoted by \(\mathbb{S}=S_{1},\times\cdots\times S_{n}\).  
An element of \(S_{i}\), representing the player's \(i\) bid is denoted by \(b_{i}=[0,\infty]\in\mathbb{R}_{+}\). This represents the player's action to increase their standing bid such that \(b_{i}^{n+1}=p^{n}+\epsilon\), (\(\epsilon\in\mathbb{R}_{+}\)). Should a player decide to abandon the auction, \(b_{i}^{n}=0\). The Cartesian product of the possible actions (bidding or quitting) is referred to as the strategy space.  
Assume \(V_{1}\) is the maximum valuation among all players \((V_{1},\ldots,V_{j},\ldots V_{n})\), representing each player's willingness to pay for the item. The auction continues until \(p^{n}>V_{j}\). At this point, player \(J\) quits the auction. The auction concludes when only one player remains willing to bid. The winning player is denoted by \(\hat{i}\), and the final price is given by \(p^{*}=p_{\hat{i}}\).  
The utility function of each player (\(U_{i}\)) is defined as the intrinsic value assigned to the object by the player, minus the price paid.  
\[U_{i}=\begin{cases}V_{i}-p_{i},&\text{if }i=\hat{i}\\ 0,&\text{if }i\
eq\hat{i}\end{cases} \tag{1}\]  
It follows that the payment equals zero for all players, except \(\hat{i}\), which is declared the winner.
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 8 Conclusions and Future Work  
### Conclusions  
This paper presents an application of genetic programming for generating routing heuristics for different variants of VRP. The proposed approach falls into the category of hyper-heuristics, because a new heuristic is generated automatically for a given VRP environment and user defined objective measure. The solution is comprised of two parts: the first part is a meta-algorithm (or a schedule generation scheme), which is defined manually. Two meta-algorithms are given which can be used according to the scheduling conditions, under either static or dynamic conditions. The second part is the priority function, used by the meta-algorithm, which serves to match a vehicle to a specific customer. The priority function is created automatically using genetic programming, and can be tailored to specific criteria.  
The results of the presented approach are new priority functions that can be used with an appropriate meta-algorithm, such as the two given in the paper. The priority functions can be evolved to optimise any conceivable user-defined criteria, and multi-objective optimisation is also possible. Once evolved, the priority functions can be used to generate routes in a small amount of time, which makes this approach applicable in on-line scheduling. While the goal of the approach is not finding the optimal solutions, it is an attempt to provide competitive results in variants where heuristics are available, and, what is more important, can help devise heuristics for new problem variants that may arise in everyday practice, where there are no adequate algorithms. The advantages of this approach are the applicability in dynamic as well as stochastic VRP environments since no information about the future of the system is used to construct the routes. The construction method is very fast, with a complexity quadratically dependent on the number of customers, which makes it a viable choice for large scale and highly dynamic VRP instances. Furthermore, the schedules obtained with this method may also serve as initial solutions to improvement heuristics, which may be employed if additional time is available.
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 5 Evaluation
### Model Accuracy and Model Convergence Time  
Figure 1(a),b depicts the scenario of training the ResNet18 model using the CIFAR10 dataset, where the system has 3*1000, 3*2000, 3*3000, and 3*4000 samples for a total of 12 normal data owners and 2*1000, 2*2000, 2*3000, and 2*4000 for a total of 8 malicious data owners, who are mislabeled with data labels.  
Figure 2a represents the convergence accuracy achieved by the global model after 50 rounds of communication, and Figure 2b represents the total time taken to complete the model training after 50 rounds of communication. Similarly, as can be seen from Figure 2a, the QuoTa and QuoTa-NT algorithms both achieve better performance in terms of model accuracy. Due to the limitation of computing capability, some workers with large amounts of training data but poor computing capability may not be selected, so the final convergence accuracy of QuoTa may be slightly lower than that of QuoTa-NT. But, it can be seen from Figure 2b that QuoTa is significantly lower than QuoTa-NT in terms of final time consumption. The RRAFL-variant algorithm ends up with a significantly lower model accuracy than QuoTa and is a bit slower to converge. The main reason is that the data owner selection considers high reputation value per bid price as an indicator. Hence, if a low-reputation data owner makes a relatively low bid, it still has a high probability of being selected, thus affecting the model accuracy.  
Figure 2: The global accuracy and time for ResNet18 with CIFAR-10.  
As the RRAFL-variant algorithm does not take into account model training time by device heterogeneity, it takes longer time to achieve model convergence. The RANDOM algorithm possibly selects malicious data owners in each round of communication due to random selection, so the global model cannot converge.  
Figure 2(a),b depict the scenario of training the LeNet5 model using the FMNIST dataset, where there are 3*500, 3*1000, 3*1500, and 3*2000 samples for in total 12 normal worker data owners in the system, as well as 2*500, 2*1000, 2*1500, and 2*2000 samples for 8 malicious worker data owners, who are mislabeled with all data labels.  
Figure 2(a) represents the convergence accuracy achieved by the global model after 30 rounds of communication, and Figure 2(b) represents the total time taken to complete the model training after 30 rounds of communication. As is similar to Figure 1(a), QuoTa, QuoTa-NT algorithms have achieved better performance in terms of model accuracy. Due to the relatively simple structure of the LeNet5 model, the amount of data has less impact on the accuracy, so the two algorithms converge to the same final convergence accuracy,  
Figure 2(a): The global accuracy and time for LeNet5 with FMNIST.  
and QuoTa is still dominant in convergence time. The RRAFL-variant algorithm is still lower than QuoTa in terms of final model accuracy and has longer convergence time. The global model of the RANDOM algorithm has also failed to converge. And, it takes near time when performing the same rounds of communication with RRAFL-variant.