esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Algorithms for Contact Sequence Temporal Graphs +
## 2 Materials and Methods
### Algorithms for Acyclic TRG_Ours
#### 2.3.2 Minhop Paths Algorithm on Acyclic TRG_Ours  
Similar to the algorithm for fastest paths on acyclic TRGs, here again, we iterate over the nodes in their topological order. Meanwhile, we also keep track of the number of hops, and as we visit the nodes, we update their neighbors.  
Algorithm 4 is the minhop algorithm for TRG_Ours. The inputs are the same as those for Algorithm 3 and similar to that algorithm, we use the vector \(minhop[]\) to record the distances in the original graph and \(distance[]\) to record the distances in the TRG graph. We initialize the \(minhop[s]\) and \(distance[(u_{s},t_{s})]\) for \((u_{s},t_{s})\in V_{out}(s)\) to 0 and all the other distances to infinity. The for loop in lines 5-23 iterates over the nodes of TRG in their topological order. Lines 11-14 go through the immediate chain neighbor of \((u,t)\), namely \((u,\bar{t})\). Here, we treat this node a little differently from the other neighbors. We only update \(distance[(u,\bar{t})]\) if \(\bar{t}\leq t_{end}\). The reason is that \((u,\bar{t})\) is only useful if it can update the distance of its neighbors, and that is only possible if the arrival time at its neighbors is within the pre-defined range. The for loop in lines 15-22 iterates over the non-chain neighbors of \((u,t)\) and updates the distance of each neighbor.  
```
1:function AcyclicMinhopPaths(\(G\), \(s\), \(tp\_list\), \(t_{start}\), \(t_{end}\)) \(\triangleright\)\(G=(V,E)\) is TRG_Ours graph, \(s\) is the source node and \(tp_{list}\) is the list of nodes in topological order
2:return Array \(minhop[]\) of minhop path durations
3: Initialize \(minhop[s]=0\), all other distances to infinity
4: Initialize \(distance[(u_{s},t_{s})]\) to 0 for all \((u_{s},t_{s})\in V_{out}(s)\) where \(t_{start}\leq t_{s}\leq t_{end}\)
5:for\((u,t)\in tp\_list\)do
6:if\(distance[(u,t)]\) equals infinity then
7: continue
8:endif
9:\(minhop[u]\leftarrow min(minhop[u],distance[(u,t)])\)
10:if\(t>t_{end}\)then continue
11: Let \((u,\bar{t})\) be the next node in sorted order of \(V_{out}(u)\)
12:if\(\bar{t}\leq t_{end}\)then
13:\(distance[(u,\bar{t})]\leftarrow min(distance[(u,\bar{t})],distance[(u,t)])\)
14:endif
15:for\((u^{\prime},t^{\prime})\in\) neighbors((u,t)\)do
16: Let \(w_{link}\) be the weight of link between \((u,t)\) and \((u^{\prime},t^{\prime})\)
17:arrival_time \(\gets t+w_{link}\)
18:if\(arrival\_time>t_{end}\)then
19: continue
20:endif
21:\(distance[(u^{\prime},t^{\prime})]\leftarrow min(distance[(u,t)]+1,distance[(u^{ \prime},t^{\prime})])\)
22:endfor
23:endfor
24:endfunction
```  
**Algorithm 4** Minhop paths algorithm for acyclic TRG_Ours
# An Alternative Series Solution for Free Vibration Analysis of Asymmetric L-Shaped Membranes
## 3 Results
### Convergence for the Symmetric Case  
The data provided in Table 2 represent the square of the first ten eigenvalues for a symmetric L-shaped membrane, analyzed at different truncation indices \(N\). The truncation indices used are 10, 60, 120, 1000, 1600, and 1800. Table 2 shows that the values converge rapidly for most eigenvalues as \(N\) increases. This can be observed because the values stabilize significantly as \(N\) approaches 1000. The first eigenvalue, \(k_{1}^{2}\), starts at 9.6558 for \(N=10\) and converges to 9.6397 for \(N>1000\). This indicates a minor deviation initially, which diminishes as \(N\) increases. The second eigenvalue, \(k_{2}^{2}\), exhibits minimal deviation across all truncation indices, indicating that it converges faster than others.  
The fifth eigenvalue, \(k_{S}^{2}\), shows a noticeable change between \(N=10\) and \(N=60\), decreasing from 31.9521 to 31.9164. Further increases in \(N\) result in values that stabilize around 31.9127, indicating that higher-order eigenvalues may require a larger \(N\) for accurate convergence. Eigenvalues such as \(k_{6}^{2}\), \(k_{7}^{2}\), and \(k_{10}^{2}\) also exhibit the trend of gradually stabilizing values as \(N\) increases. However, the changes are relatively small after \(N\) reaches 120.
# A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis Model
## 5 Experiment Results  
### General Settings  
The main function of the LFA model is to decompose the scoring matrix of missing data into two latent factor matrices P and Q, the user latent factor matrix and item latent factor matrix, and finally calculate the product of the two latent factor matrices \(PQ^{T}\) to obtain a complete scoring matrix. After gaining the complete scoring matrix, we remove the items users have already rated, and then rank the remaining items according to their predicted scores. Naturally, the items with high scores will be recommended first. As a result, accurate item ratings are the key to the recommendation system. The ultimate purpose of the LFA model is to gain missing data in the user-item matrix, so the main method to judge the recommendation effect of a recommendation model is to evaluate the difference between its predicted value and real value. At present, the methods commonly used are root mean square error (RMSE) [34] and mean absolute error (MAE) [35]. In Formula (20), \(r_{ui}\) and \(\hat{r_{ui}}\) denote the real score and predicted score, respectively, and \(|R|\) denotes the amount of data.  
\[\left\{\begin{array}{l}RMSE=\sqrt{\sum_{(u,i)\in R}(r_{ui}-\hat{r_{ui}})^{2 }/|R|},\\ \\ MAE=(\sum_{(u,i)\in R}|r_{ui}-\hat{r_{ui}}|_{abs})/|R|.\end{array}\right. \tag{20}\]  
According to the formulas of the two methods, it can be concluded that the smaller the \(RMSE\) and \(MAE\) value of this model, the better its recommendation effect.
## 4 Experiments
### Comparison between FastSLAM and FastSLAM-PSO
#### 4.2.3 Square Scenario  
In this section, the performance variability of FastSLAM, FastSLAM-PSO, and FastSLAM-DE algorithms in a square-shaped environment was examined. As indicated in Figures 6 and 7, FastSLAM-PSO exhibited stable performance, whereas FastSLAM-DE did not fare as well, suggesting that algorithm applicability, parameter settings, and environmental complexity significantly impact SLAM outcomes.  
The mapping results for the square scenario visually presented in Figures 6 and 7 highlight the distinct performances of the FastSLAM, FastSLAM-PSO, and FastSLAM-DE algorithms. FastSLAM-DE, while showing promise in some aspects, struggled with distortions and inaccuracies, particularly along the \(Y\)-axis, which may be attributed to its optimization framework's difficulty in adapting to the regular patterns and repeated structures present in square environments. This challenge is further exacerbated by the lack of fine-tuned parameters that could enhance its adaptability to such settings. The comparative analysis of the three algorithms in Figures 6 and 7 underscores the importance of selecting an optimization strategy that complements the specific characteristics of the SLAM problem at hand. FastSLAM-PSO's superior performance is indicative of the effectiveness in enhancing SLAM algorithms, especially in challenging environments.  
Figure 4: Visual comparison of SLAM results between FastSLAM, FastSLAM-PSO and FastSLAM-DE in the large-scale scenario scene_2.  
Figure 5: Comparative results of FastSLAM, FastSLAM-PSO and FastSLAM-DE in the large-scale scenario scene_2.  
Figure 6: Visual comparison of SLAM results between FastSLAM, FastSLAM-PSO and FastSLAM-DE in the large-scale scenario scene_3.
# Design and Additive Manufacturing of TPMS Heat Exchangers
## 1 Introduction
The material used in the printing had relatively low thermal conductivity. [PERSON], [PERSON] et al. explored metal TPMS heat exchangers, which were used to transfer heat from air to liquid, and investigated the gyroid's geometric features including the network type, thickness, unit cell size, and aspect ratio. [PERSON], [PERSON] et al. found that the LPBF-fabricated gyroid heat exchangers outperformed the conventional design [15]. TPMS HXs have a variety of applications where size, efficiency, weight and cost play important roles. These areas include space aircraft, defense, high-power electronics, nuclear energy, and waste energy recovery. For example, in space aircraft, TPMSs will resolve future challenges related to increased electrically powered subsystems and ultra-high bypass geared turbofan engines, as well as the possible advent of electrified or nuclear propulsion [16; 17]. In high-power electronics, high-efficient cooling and flexible thermal management solutions meet critical demands with the continuous miniaturization and rapid increase in heat flux of electronic devices [18; 19]. In waste energy recovery, there is a strong need to develop low-cost advanced heat exchangers that increase heat transfer coefficients and can withstand corrosive environments in heat recovery systems [20]. In this paper, a systematic investigation is undertaken, from design to manufacturing, on metal TPMS HXs, in consideration of material thermal conductivity, TPMS lattice types, lattice unit length, and wall thickness. Section 2 details the design and modeling of TPMS HXs. Section 3 describes TPMS HX fabrication with AM and its testing results. Section 4 discusses our conclusions. Figure 1: Various types of triply periodic minimal surface (TPMS) lattices.
# Three Rapid Methods for Averaging GPS Segments
## 4 Experiments  
We use the datasets described in ([[http://cs.uef.fi/sipu/segments](http://cs.uef.fi/sipu/segments)]([http://cs.uef.fi/sipu/segments](http://cs.uef.fi/sipu/segments))) [4]. The data include intersections taken from the open street map (OSM) and trajectories between those intersections, selected from four different data collections: Joensuu 2015 [11], Chicago [15], Berlin [16], and Athens [16]. Ground truth segments are also included as obtained from OSM. The evaluation is done by comparing the resulting average segments against the ground truth. The sets were grouped together and then randomly divided into 10% for training (100 sets) and 90% for testing (901 sets). There is a total of 10,480 trajectories consisting of 90,231 points. More details of the data extraction are available in [4].  
For evaluating the accuracy of the segments, we use hierarchical cell-based similarity measure (HC-SIM) as proposed in [4]. It extends C-SIM measure ([[http://cs.uef.fi/mopsi/routes/grid](http://cs.uef.fi/mopsi/routes/grid)]([http://cs.uef.fi/mopsi/routes/grid](http://cs.uef.fi/mopsi/routes/grid))) [11] to multiple zoom levels. C-SIM uses a grid of \(25\times 25\) m and counts how many cells the resulting segment average share with the ground truth segments from OSM relative to the total number of cells they occupy. HC-SIM uses six layers with the following grid sizes, \(25\times 25\) m, \(50\times 50\) m, \(100\times 100\) m, \(200\times 200\) m, \(400\times 400\) m \(800\times 800\) m. In case of the [0, 1] scale, the following sizes are used, 0.5%, 1%, 2%, 4%, 8%, 16%.
###### Abstract  
Background: This study compared the influence of three different radial spacers (60,90, 120 microns) on the marginal gap adaptation by using computer-aided manufacturing (CAD-CAM) for producing monolithic zirconia reinforced lithium silicate (ZLS) ceramic crowns. Methods: A total of 45 abutent acrylic teeth were divided into three groups of different radial spacers (60, 90, and 120 microns). In each group 15 teeth were scanned by Omnicam intra oral scanner and ZLS crowns were ground. For each unit the marginal gap was evaluated at four regions of interest by scanning electronic microscope (SEM). To compare the marginal gap between the three groups a one-way ANOVA with post-hoc Bonferroni test was preformed (\(\alpha=0.05\)). Results: The marginal gap for a 60 microns (162.99 \(\pm\) 16.25 \(\upmu\)m) radial spacer was found significantly higher than 90 (41.85 \(\pm\) 3.57 \(\upmu\)m) and 120 (41.85 \(\pm\) 5.3 \(\upmu\)m) microns radial spacers (\(p<0.05\)). Between 90- and 120-micron radial spacers no difference was obtained. (\(p<0.05\)). Conclusions: A radial spacer of 60 microns showed a significantly higher marginal gap compared to 90 and 120 microns and was not clinically accepted (>120 microns). For both 90 and 120 microns the marginal gap was clinically accepted (<120 microns) with no difference between the groups. The radial spacer which should be optimum for CELTRA(r) DUO crowns is 90 microns.  
CAD-CAM; radial spacer; digital spacer; ZLS; marginal fit; marginal gap; CEREC; marginal discrepancy; SEM +
Footnote †: journal: Applied Sciences
limit is the upper acceptable limit in the literature, although some studies are less tolerance and set the upper limit to less than 100 \(\upmu\)m [7; 8; 9].  
In the past, when using a die model for fabricating metal restorations the ideal spacer was 30-40 microns and was fit for zinc-phosphate cement [10; 11]. Today, when fabricating restorations by CAD-CAM technology the spacer is set in the digital software according to the manufacture guidance for each material. In the CELTRA(r) DUO brochure there is no recommendation for the recommended radial spacer, which is one of the reasons for conducting this research.  
After a comprehensive review of the literature, we were not able to find studies which compared the influence of different radial cement spacers on the marginal gap adaptation of zirconia reinforced lithium disilicate (CELTRA(r) DUO). The null hypothesis was that no difference would be found between three different cement spacers.
## 5 Experimental Results
### Results Using the Dataset with Pathologies
#### 5.1.3 Results for Gradient Boosting with Evolutionary Feature Selection  
The results presented below are obtained with GB using evolutionary FS.  
**GA with _Stud_ selection for Gradient Boosting (GB-S):** In this case, the selection method reduced the number of variables used from 37 to 19, keeping its classification rate, and even obtaining a slight improvement, reaching an average accuracy of 0.7382, as shown in Table 2. Three of the variables selected were age, sex, and apnea, these  
Figure 3: Mean accuracy evolution graph with RFECV.  
being the most frequently chosen. Other variables to be highlighted are those related to smoking, education, earning, and adherence to the Mediterranean diet.  
Figure 4 shows the graph corresponding to the Gradient Boosting model with _Stud_ selection for the dataset with pathologies. The first variables that appear are age, sex, and apnea (as in the previous cases). In the case of education, it is shown that a lower level of education increases the probability of being overweight or obese. On the other hand, those individuals who suffer from metabolic syndrome are also more likely to be overweight or obese, but as in the case of apnea, if the individual does not suffer from this pathology, the variable does not have such a strong impact. Again, this can be seen in that the blue dots are clustered next to the zero point, while the red dots are spread over the positive values.  
**GA with tournament selection for Gradient Boosting (GB-T):** Using the tournament selection method with selection pressure of five, 23 variables were selected by the GA, achieving an average accuracy of 0.7332, as can be found in Table 2. Similar to the previous case, among the features selected, some of the most common ones are sex, age, adherence to the Mediterranean diet, and some new additions, which were variables related to heart disease and different types of cancer. Other variables to note are the appearance of distilled/fermented beverages, education, earnings, and stress.
## 3 Overview of LPWAN
### Low Power Consumption  
Low power consumption is considered as one of the significant features attributed to LPWANs. LPWA networks devices are typically battery powered and are usually effective and reliable for a very long term without human interference. The authors in [13] explained that LPWANs have the capacity to reduce the amount of energy consumed by utilizing sleep mode. In such modes, the transceiver devices are responsive only when data are to be transmitted or received.
## 3 Results and Discussion
### Signal-to-Interference Ratio  
Figure 6 shows the SIR simulation results for the proposed CSK-based UM-VLC channel model. In Figure 6a-c, the SIR for the red, green, and blue PD is shown over the simulation space for the proposed model. It is clear that in LOS-dominant sectors of the environment, the SIR remains fairly constant, with the blue PDs experiencing around 10 dB of interference, whereas the green and red PDs experience around 12.5 and 13.5 dB of interference, respectively. This can be easily corroborated by looking at the ECDF presented in Figure 6d, with an almost vertical cumulative distribution. The difference in interference values across the three colors can be explained by the fact that the PD spectral responsivity selected favors red and green light significantly more than blue light. The fact that the interference appears practically constant in the LOS-dominant sectors of the scenario can be explained by the fact that the refractive index of coal dust is fairly flat on the visible range, meaning that the attenuation caused by dust is not particularly color-selective, i.e., the three colors attenuate at roughly the same rate.  
Figure 6: Signal-to-interference ratio. (**a**) Red PD. (**b**) Green PD. (**c**) Blue PD. (**d**) Empirical CDF for SIR at red, green, and blue PDs.  
In the NLOS-dominant sectors, i.e., where the LOS link is shadowed, the SIR changes very abruptly, with the blue PD experiencing much more interference than in the LOS-dominant sectors, while the red PD experiences much less. This can be explained by the fact that the wall reflectance used in this simulation favors red and green wavelengths over blue ones.
## 4 Results and Discussion
### Discussion
Although this article has obtained a probabilistic description of the health status of a flight control system for 60 flights, there are still the following aspects that need improvement: Firstly, there are fewer data samples. In future research researchers can consider using more data samples to obtain more accurate results. Secondly, this study analyzed the uncertainty of the parameters that affect the health of a flight control system from a macro perspective, and did not specifically analyze the performance degradation of the key components of a flight control system. Future research can analyze the influence of parameter uncertainty of these components on the evaluation results.
## 1 Introduction
They can also be used to transmit large amounts of data in real--timee.g., video in the highest resolution. For the purpose of the analysis presented in this paper, bands at 750 and 3600 MHz were considered as the EM wave source, which should be representative for two middle frequencies of 5G systems. The simulations of a human head exposed to an electromagnetic wave was performed for: (1) the case where no cover was used and (2) the model of applying an improvised conductive cover (\"infoil hat\"). For the 3600 MHz band, a frequency selective shielding structure based on vertical wires was also presented. As a result of the simulations, no significant reduction in power density was observed in the area of the head covered with an improvised electromagnetic shield in the case of exposure to the wave emitted toward the face. The introduction of the shield slightly changes the power density distribution, which is illustrated by the data obtained for the eye region. The reduction of the average value of energy absorbed by tissues was observed. The dipole-based structure proposed in this paper performs comparable to the trifoil-like structure, but its design makes it much more comfortable for potential use.
## 5 Experimental Setup  
Eclipse and MATLAB are used to validate MFHS.  
MATLAB generates the fuzzy outputs to determine the data priority and the resource allocation based on fuzzy rules.  
Eclipse simulates the process environment (Edge, Mist, Fog, and Cloud) and calculates the power consumption, allocation time, and processing time.  
In the experiment, six sensors are used at the Edge layer: two for the body temperature, two for the heart rate, and two for the glucose level. Two Mist nodes and one Mist broker are used at the Mist layer. Six Fog nodes and one Fog broker are used at the Fog layer, and three central Clouds are utilized at the Cloud layer. Table 4 summarizes the tools employed in the experiments and Figure 13 shows the process sequencing, with the node notation specified in Table 5.  
Figure 12: Fog node clusters.  
Figure 13: Work sequence.  
In Figure 13, six Fog nodes are formed into two clusters based on the computational capacity (CC). Each cluster consists of several Fog nodes. Cluster one (C1) represents Fog nodes with a higher CC, and cluster two (C2) represents Fog nodes with a lower CC.  
For an illustration, let us assume in Table 6 that we have six Fog nodes, namely F1, F2, F3, F4, F5, and F6, with their respective CC. Based on our clustering principle, C1 will consist of F1, F4, and F6, while C2 will contain F2, F3, and F5. Accordingly, C1 is responsible for receiving the medium-priority data from the Fog broker, and C2 is responsible for receiving the low-priority data from the Fog broker.  
The Fog clusters continue to process data until the CC of any Fog node goes below a threshold value of 25% of its capacity; when that happens, the data will be offloaded to the Cloud to avoid damaging the node.
## 2 Materials and Methods
### Investigation on S2 Waveform Changing Modulated by Respiration  
To observe the changing visually, S2s are firstly segmented from the heart sound signal using the envelope method proposed by [PERSON] et al. (2016). The respiratory phases associated to the S2s are looked up from the respiratory phase signal with the reference of the S2s' timing. The S2s are re-ordered according to respiratory phase in ascending order. Then, the S2s are aligned in time domain and formed into a two dimensional matrix. The horizontal direction is in the time domain and the vertical direction is in the respiratory phase domain. These matrixes enable the changing to be observed clearly. This preprocessing is repeated for the twelve subjects and the contour plots of these matrixes are shown in Figure 6. A common phenomenon can be seen in that the S2s of each subject have two parts. The first parts are aligned in time domain, whose waveforms have almost no change; however the timing of the second parts varied with respect to respiratory phase. That is to say, the splits between the two parts are modulated by the respiratory phase. The split decreases in expiration where the phase is from \(-\pi\) to zero and the split increases in inspiration where the phase is from zero to \(\pi\). The authors believe that the first parts are the aortic components and the second parts are the pulmonary components.  
Figure 5: Illustration of respiratory phase calculation. (**a**) Respiratory signal collected from a belt sensor. (**b**) Respiratory phase. Inspiration corresponds to the phase from zero to \(\pi\) and expiration corresponds to the phase from \(-\pi\) to zero.