esa-sceva/satcom-chunk-collection huggingface dataset:
# Pattern Classification and Gear Design of Spatial Noncircular
## 6 Conclusions  
Through the study of pattern classification and gear design of spatial noncircular gear CVT, the following conclusions can be drawn. (1) Under the condition of choosing appropriate parameters, all the five patterns can realize the CVT. Compared with type I, type II has the advantages of a simpler structure, smaller volume, shorter transmission chain, and higher transmission efficiency. However, it is more complex in design, relatively limited by parameters and has a smaller range of transmission ratio. According to the actual working situation and requirements, different patterns can be selected to realize the required CVT;  
(2) The curve gear face pair can realize variable transmission ratios between intersecting shafts. Through the tooth profile classification design method, the curve face gear pairs with straight tooth, helical tooth, and curvilinear tooth can be obtained. Compared with the straight tooth curve face gear pair, the helical tooth and curvilinear tooth curve face gear pair have a more complex design, but a greater contact ratio and load capacity, which can meet the requirements of different working situations;  
(3) The overall transmission ratio of each pattern was simulated, and the simulation errors were within a reasonable range, which verified the correctness of the theoretical analysis of each pattern. Through transmission experiments, the correctness of the tooth profile classification design method of the curve face gear pair was verified, which provided a strong proof for the correctness of the whole design method to a certain extent;  
(4) The CVT principle and transmission ratio characteristics of each pattern were analyzed only from the aspect of kinematics here. However, the dynamic characteristics and performance of each pattern should be further studied in the follow-up work, such as vibration, noise, carrying capacity, and transmission efficiency, which have an important impact on the working performance of the spatial noncircular gear CVT system. Figure 19: Maximum relative error of the transmission ratio experiment. **Author Contributions:** Conceptualization, Y.Y. and C.L. ; data curation, P.X. ; formal analysis, Y.Y. ; funding acquisition, C.L. ; investigation, Y.Y. ; methodology, Y.Y. ; project administration, C.L. ; resources, C.L. ; software, Y.Y; supervision, C.L. ; validation, Y.Y. ; visualization, Y.Y. and P.X. ; writing--original draft, Y.Y. ; writing--review and editing, Y.Y. and C.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by National Natural Science Foundation of China, grant number 51675060. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 4 Proposed Connectivity-Based Clustering Schemes
### Vehicular Connectivity to VANET Graphs  
The dynamic and inconsistent structure of VANET is a big challenge for routing reliability. In the literature, researchers used various parameters to design reliable routing algorithms for optimal data communication [7; 23]. In this paper, we use vehicular connectivity as a metric for designing a cluster-based routing scheme. The performance of linear routing significantly degrades in the ultra-dense network due to excessive iterative messages. The formation of clusters is an appropriate choice to divide the network into manageable groups optimally. In this section, we represent VANET as a graph \(G(E,V,C)\), where \(E\), \(V\), and \(C\) represent the edges, number of the vehicles, and connectivity, respectively. The graphical representation of vehicular topology is given by the following:  
\[A=\begin{cases}c_{ij},&\text{if }\left(V_{i},V_{j}\right)\in C\text{ where }0<c_{ij}<1\\ 1,&\text{if }i=j\\ 0,&\text{otherwise.}\end{cases} \tag{7}\]where \(A\) is the affinity matrix and \((V_{i},V_{j})\in C\) denotes that \(V_{i}\) and \(V_{j}\) are connected to each other. We used three conditions to calculate the adjacency matrix of the vehicular network:  
* The value of link connectivity is added to the \(ij\)th position of adjacency matrix \(Adj\) if vehicle \(V_{i}\) and \(V_{j}\) are connected.
* If a link represents the same connectivity in both directions, i.e., \((i=j)\), then we add 1 value for connectivity.
* The term \"otherwise\" in Equation (7) is considered when the first two conditions failed. When two vehicles are not connected, we add 0 in that case. The adjacency matrix \(Adj\) representing the inter-connectivity of vehicles can be calculated as below:  
\[Adj=\begin{bmatrix}c_{11}&c_{12}&c_{13}&\dots&c_{1n}\\ c_{21}&c_{22}&c_{23}&\dots&c_{2n}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ c_{n1}&c_{n2}&c_{n3}&\dots&c_{nn}\end{bmatrix}\]  
where \(c\) represents the link connectivity of two vehicles.
## 3 Results  
This section aims to provide an extensive evaluation of different aspects concerning the proposed methodology and its extensions. Therefore, it first presents a comparison between the two proposed methods for the estimation of the duration of offshore operations, i.e., the sliding-window approach and the approach using Markov Chains. Afterward, this section presents different sets of numerical simulations to evaluate the effect of loading bay restrictions and the overall extended MPC-based approach. The first set aimed to evaluate the impact of loading bay restrictions without the influence of weather forecast uncertainties. Therefore, only the MILP described in Section 2.4 was used to generate non-incremental, optimal plans. The second set of simulations aim to determine a suitable configuration for the prediction horizon and sampling step size used by the MPC-based approach. Therefore, a single vessel was simulated using a sampling step size \(T\) of one week and half-a week, and prediction horizons of respectively one, two, and three times \(T\) to evaluate the quality of the generated plans. The third set of simulations aims to evaluate the response time of this approach, as response times constitute a critical factor for decision-support systems.  
In all cases, the algorithm and the MILP are implemented in MATLAB r2018b using a problem-based formulation, while the optimization was executed using CPLEX 12.9 on an ordinary quad-core office computer (i7-3770K, 8 GB RAM) using the opportunistic search mode. For solving larger instances of the problem, the optimizer aborts if the solution quality stagnated. Therefore, the maximum optimization time for each run was set to three hours. After a run finished, another run was started, again running for a maximum of three hours using the best previous encumberment as its initial solution. If successive runs were unable to find another encumberment, the algorithm selects the best previous encumberment as the final solution. These restarts were conducted, as CPLEX applies a cut and branch strategy. Therefore, experiments showed, CPLEX makes most of the progress for larger scenarios during the initial cutting stage and the first hours of the branching stage, often resulting in an optimal or near-optimal solution with only a few repetitions. Moreover, the optimizer behaves differently for different initial solutions, which makes it useful to try again with another solution.
## 4 Results  
### Change in Total Costs of Operation  
Table 3 presents TCO savings for ATs compared with HDTs in the logistics industry. Overall, the cost structure of operating a truck has been significantly impacted by the introduction of driving automation. The findings in Table 3 confirmed the overall TCO reductions when replacing HDTs with ATs, although the magnitude of the savings varies depending on which of the five scenarios is considered. Thus, this study concludes that ATs will likely enable a net decrease in TCO from the perspective of freight transport operators; however, the degree of the potential reduction will be uncertain.  
Specifically, the results of the base scenario (scenario 3) indicate that the cost reduction per month was USD 2399 for a 1-ton truck, USD 2891 for a 5-ton truck, USD 3438 for a 12-ton truck, and USD 3892 for a 25-ton truck. Expressed as percent change in TCO for ATs, the monthly savings were 33.17, 26.71, 24.81, and 23.82 percent for 1-, 5-, 12-, and 25-ton trucks, respectively, in the most pessimistic scenario. As shown in Figure 2, the percent change in TCO compared to the most pessimistic scenario increased by approximately 30% for the various types of trucks in the most optimistic scenario.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Scenario** & **1-Ton** & **5-Ton** & **12-Ton** & **25-Ton** \\  & **Truck** & **Truck** & **Truck** & **Truck** \\ \hline \multicolumn{6}{c}{TCO Savings in US dollars} \\ \hline
1 & the most pessimistic & 1415 & 1642 & 1985 & 2222 \\
2 & the pessimistic & 2051 & 2442 & 2915 & 3289 \\
3 & the base & 2399 & 2891 & 3438 & 3892 \\
4 & the optimistic & 2578 & 3142 & 3735 & 4241 \\
5 & the most optimistic & 2795 & 3466 & 4124 & 4706 \\ \hline \multicolumn{6}{c}{TCO Savings in Percent (\%)} \\ \hline
1 & the most pessimistic & 33.17 & 26.71 & 24.81 & 23.82 \\
2 & the pessimistic & 48.08 & 39.73 & 36.43 & 35.26 \\
3 & the base & 56.25 & 47.04 & 42.97 & 41.73 \\
4 & the optimistic & 60.43 & 51.11 & 46.67 & 45.47 \\
5 & the most optimistic & 65.53 & 56.39 & 51.54 & 50.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of the overall total cost of operation reduction due to autonomous trucks in the case study of the route.
# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 5 Explanation and Evaluation of Results
### Specific Case Analysis  
Although Grad-CAM has been an effective visual explanation tool to verify that the 2D-CNN TL solution could identify the fault patterns in the target system via distant source domains, another perspective to explain the improvement gained by the 2D-CNN TL solution was to investigate how it avoided the mistakes that the non-TL baseline method made. This was investigated by looking into the specific cases where the non-TL baseline method predicted a false label while the 2D-CNN TL method predicted it correctly. In doing so, the advantage of the 2D-CNN TL solution over the non-TL method could be found, enriching our understanding of the methods.  
Still working with the scenario where 15 cases exist in the target training set of ECS, the predictions from the non-TL baseline method and from the 2D-CNN TL method using ImageNet as the source were compared with the true label of each case in the ECS testing set. It was found that the most common cases the 2D-CNN TL solution correctly predicted, while the non-TL baseline could not, were the ram air inlet (RAI) blockage cases. There were 19 such cases, and the non-TL baseline predicted them incorrectly as a PHX fault.  
One of the misclassified RAI blockage cases is plotted in Figure 15 (solid red line). To investigate why it achieved incorrect classification, clues could be found by looking into the most similar case in the training set based on which the diagnosis algorithms were trained. From Figure 15, the RAI case in the testing set is actually closer to a PHX fault case in the training set (dashed green line) than the RAI blockage case in the training set (dashed red line). Hence, the non-TL baseline method misclassified the RAI case as a PHX case due to numerical similarity. However, the fact that the 2D-CNN-based TL solution correctly predicted the RAI case in the testing set must imply that it can distinguish it from the PHX case in the training set. Applying Grad-CAM on the two confusing cases revealed such behaviour. As shown in Figure 15, the heatmap for the two confusing cases following the 2D-CNN TL solution has different concentration regions, which means that the TL method detected different patterns in the two images and, hence, correctly identified them as two different classes. Therefore, one advantage of the 2D-CNN TL solution over its non-TL comparison is its ability to overcome numerical similarity and distinguish confusing cases based on minute pattern differences.
## 3 Model
### Modeling of SASCHCS
#### 3.1.2 Absorption Subsystem and Compression Subsystem  
The off-design model of cooling subsystems (the absorption subsystem and the compression one) was developed in our previous study [30]. The model of the absorption subsystem was based on the characteristic equation.  
The relationship of the generator heat transfer and the generator hot water flow rate can be expressed as [31]:  
\[UA=\left(\frac{m_{g}}{m_{g,attad}}\right)^{0.8}(UA)_{ntad} \tag{9}\]  
The cooling output of absorption subsystems is expressed as:  
\[Q_{e,as}=s\cdot\Delta t-\alpha\cdot Q_{loss,as}=s\cdot\Delta t-s\cdot\Delta t \Delta t_{min} \tag{10}\]  
The parameter s is related to UA (multiplication of overall heat transfer coefficient and area in heat exchangers), and \(\Delta t\) is relevant to mean temperature of hot water, cooling water, and chilled water.  
\(\Delta t_{min}\) is calculated as follows [32]:  
\[\Delta\Delta t_{min}=1.9+0.1\Delta t \tag{11}\]  
The COP of absorption subsystems is:  
\[COP_{as}=\frac{\int Q_{e,as,i}dt}{\int Q_{g,i}dt} \tag{12}\]  
The off-design model of compressions is based on the lumped parameter method. As a result, the thermodynamic characteristic of its component is controlled by mass and energy conservation:  
\[\sum m_{i}=\sum m_{o} \tag{13}\]  
\[Q=\sum m_{i}h_{i}-\sum m_{o}h_{o}=mc_{p}\Delta T=h\cdot A\cdot LMTD \tag{14}\]  
The compressor work consumed by the SASHCS is calculated by:  
\[W=m_{cs}(h_{dis,s}-h_{suc})/\eta_{s} \tag{15}\]  
Compared to the reference case (the cooling load is solely met by the compression subsystem), the energy saving of SASHCS is:  
\[E_{sure}=\int_{\Delta t}(W_{ref,i}-W_{i})dt \tag{16}\]The COP of compression subsystems is:  
\[COP_{cs}=\frac{\int Q_{c,cs,i}dt}{\int W_{i}dt} \tag{17}\]
## 2 Biomechanical Feedback in Motor Learning--How Far Is It from a Real-Time Application?  
Wearables in sports are only few years old; however, they have expanded radically, from the real-time monitoring of players' signs of exhaustion or injury while on the field to including perceptual and psychological aspects of professional team sports for enhancing performance [5; 6; 7]. It has already been a public's agreement that wearable technology is leading a revolution in sports [5; 7; 8]. Various sensors are now fitted into sport equipment, wristband, and/or clothing to determine athletic performance, like speed, acceleration, power, distance, heart, and metabolic conditions during training. All the crucial data are sent to the coach and training team instantly, allowing for them to perform an individualized training for increasing athletic competence.  
Nevertheless, a real-time biomechanical feedback training would currently not look so optimistic. A search using keywords in the authority database--Web of Science--reveals the following scenario: When the keyword \"biofeedback training\" is applied, 5588 articles are found. However, when the keyword is changed to \"biomechanical feedback training\", the article number is dramatically dropped to 569. Even more theatrical, when two additional keywords \"real-time\" and \"sport\" are added for a search, the number decreased to 23. To the end of the search, scarcity of articles occurs when the keyword \"sport\" is substituted by \"dancing\", i.e., only one article is found (Table 1). These results would suggest that, when comparing to other biofeedback applications, the real-time biomechanical feedback application lag far behind in sports and arts practice.  
A close look at the types of published papers divulges that the real-time biomechanical feedback training in motor learning is still an infant science, i.e., only two applied studies attempting to reveal its potentials in human motor learning/training (Table 2). When considering the booming popularity of wearables in sports as well as in health-related applications, the number of biomechanical inquiries appears to be disproportionately low. The rarity of this occurrence could be a product of both facts that there is a lack of a general biomechanical model for feedback motor learning and that researchers are still searching for methodological breakthroughs to link biomechanical quantification and human motor learning in real-time.  
\begin{table}
\begin{tabular}{c c c c}
**Biofeedback Training** & \begin{tabular}{c} **Biomechanical** \\ **Feedback Training** \\ \end{tabular} & \
\begin{tabular}{c} **Biomechanical** \\ **Feedback Training \&** \\ **Real-Time \& Sport** \\ \end{tabular} &
\begin{tabular}{c} **Biomechanical** \\ **Feedback Training \&** \\ **Real-Time \& Dancing** \\ \end{tabular} \\
5588 & 569 & 23 & 1 \\ \end{tabular}
\end{table}
Table 1: The result of literature search in all databases of Web of Science on 11 October 2018.
# Survey for Soil Sensing with IOT and Traditional Systems
## 2 Tradicitonal Soil Sensing System
### Spectral Range Selection for Reflectance Spectroscopy  
For reflectance spectroscopy, multiple spectral ranges are accessible, as depicted in Figure 2. Research from [17, 19] outlines the most consistently effective spectral ranges utilized in Reflectance Spectroscopy: Visible, Near-Infrared (NIR), and Mid-Infrared (MIR). Combinations of these spectra, such as Vis-NIR(VNIR) or NIR-MIR, offer potential solutions for detecting various soil properties. Spectra deemed to be either unresponsive or influenced by spectrometer artifacts were excluded before conducting the statistical analysis. The wavelength ranges for Visible, NIR, and MIR are detailed in Table 2. According to the research of [17, 19, 38], five spectral ranges are typically considered and employed in spectral analysis.
## Part B: Network Design for Energy Optimization
### Centralized Approaches  
In one of the methods, that focuses on deployment optimization, a grid manner discretization of the space is performed [7]. Their method tries to find the grid point closest to the target, instead of finding the exact coordinates of the target. In such a setting, an optimized placement of sensors will guarantee that every grid point in the area is covered by a unique subset of sensors. Thus, the sensor placement problem can be modeled as a special case of the alarm placement problem described by [PERSON] [41]. That problem is the following: given a graph \(G\), which models a system or a network, one must determine how to place \"alarms\" on the nodes of \(G\) so that any single node fault can be diagnosed. It has been shown in [41] that the minimal placement of alarms for arbitrary graphs is an NP-complete problem. Clearly, their problem is easier than ours, since they relax the requirement to find exact coordinates of moving objects by just finding the nearest grid point. Since their problem is computationally difficult, this implies the inherently high complexity of our problem. Another indication of the hardness of the problem is the fact that, as shown in [5], the localization problem is NP-hard in sparse wireless sensor networks.  
Also, our problem is related but different to the following other well known approach that focuses on power conservation: in [18] sleep\(-\)awake patterns for each sensor node are obtained during the tracking stage, to obtain power efficiency. The network operates in two stages: the surveillance stage during the absence of any event of interest, and the tracking stage, in response to the presence of moving targets. Each sensor initially works in the low-power mode when there are no targets in its proximity. However, it should exit the low-power mode and be active continuously for a certain amount of time when a target is sensed, or even better, when a target is shortly about to enter. Finally, when the target passes by and moves farther away, the node should decide to switch back to the low-power mode. Our approach is also power aware in the same sense (since we also affect the duration of sensors' operation), but additionally we also control the transmission range (and thus the power dissipation).  
Another centralized approach, is \"sensor specific\", in the sense it uses some smart powerful sensors that have high processing abilities [19]. In particular, this algorithm assumes that each node is aware of its absolute location via a GPS or a relative location. The sensors must be capable of estimating the distance of the target from the sensor readings.
## 3 Results and Discussion
### EAHX Exit Air Temperature: Assessing EAHX Room Load Removal
Figure 7: Timeseries of EAHX exit air temperature (\(T_{\text{AL}}\), monitored data) and EAHX airflow rate (\(\dot{V}_{\text{a}}\), monitored data). Upper and lower room setpoint temperatures and outdoor air temperature data also included. Figure 8 also depicts temperature gradients \(\Delta T_{\text{a0,rs}}\) and \(\Delta T_{\text{a1,rs}}\). Directing the attention to these gradients, given that \(|\Delta T_{\text{a1,a0}}|\) is often approximately zero in the 19 \({}^{\circ}\)C to 25 \({}^{\circ}\)C range, gradients \(\Delta T_{\text{a0,rs}}\) and \(\Delta T_{\text{a1,rs}}\) are approximately equal, overlapping in Figure 8 for this range. As outside air temperature increases, \(|\Delta T_{\text{a1,a0}}|\) also increases; therefore, differences between \(\Delta T_{\text{a0,rs}}\) and \(\Delta T_{\text{a1,rs}}\) become visible: values of \(\Delta T_{\text{a0,rs}}\) are well approximated by a straight line, whilst values of \(\Delta T_{\text{a1,rs}}\) follow a curved line. For outdoor air temperatures of 31 \({}^{\circ}\)C and 29 \({}^{\circ}\)C--room upper setpoints for comfort categories III and I, respectively--temperature gradient \(\Delta T_{\text{a0,rs}}\) becomes 0 K (see point 3 and point 4; use left and right axes for \(\Delta T_{\text{a0,rsIII}}\) and \(\Delta T_{\text{a0,rsI}}\), respectively). When the outdoor air temperature increases to the maximum 40 \({}^{\circ}\)C, temperature gradient \(|\Delta T_{\text{a1,a0}}|\) increases to \(\sim\)9 K, precisely the difference between gradients \(\Delta T_{\text{a0,rs}}\) and \(\Delta T_{\text{a1,rs}}\). With the help of gradients \(\Delta T_{\text{a0,rs}}\) and \(\Delta T_{\text{a1,rs}}\), regions B, C and D are delimited in Figure 8 for room comfort expectancy levels III and I. Figure 8 confirms, once more, that for Category III, the built EAHX avoids region D (\(T_{\text{aL}}<T_{\text{rIII}}^{\text{U}}\)). It provides further insight for the analysis of comfort Category I, showing that the EAHX is incapable of room cooling for outdoor air temperatures exceeding 37 \({}^{\circ}\)C (to the right of point 5, \(T_{\text{aL}}>T_{\text{rIs}}^{\text{U}}\)). Note, however, that according to ASHRAE [38], for Beja (close to where the built EAHX is located), the 0.4%, 1.0% and 2.0% annual cooling dry-bulb design outdoor air temperatures are 37 \({}^{\circ}\)C, 35 \({}^{\circ}\)C and 33 \({}^{\circ}\)C, respectively. This means that for the Category I comfort level, the most demanding design criterion used in \"traditional\" (refrigeration machine) HVAC design--outdoor air temperature of 37 \({}^{\circ}\)C--equals the upper threshold for which the built EAHX is capable of room cooling. For the Category III comfort level, according to Figure 8, the upper threshold exceeds 40 \({}^{\circ}\)C (see point 6, with \(T_{\text{aL}}\leq T_{\text{rIII}}^{\text{U}}\)), allowing EAHX room load removal when outdoor air temperatures exceed the most demanding 0.4% design criterion.