esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Overall Comparison: PILLAR vs. DUO vs. TRIbot
#### 5.4.3 Last Choice: DUO (Dex and Scoot)  
DUO was ranked third as the least favored robot embodiment across the US and JP participants. Overall, participants reported their primary concern about the DUO, specifically _Dex_, being the installation and limitations of the tracks that must be installed into the ceiling [PU-18,21,24,31,32,33,40,41, PJ-08,37,38]. One JP participant explained that \"the ceilings in Japanese houses are thin, and construction is difficult\", thus DUO would not be a choice [PJ12]. Others explained that the extensive installation would be difficult [PU26, PJ-32,38,48] and that if you did not install tracks in every room then the DUO would be limited to where the tracks were installed [PU-04,07,21,23,34,36,37, PJ-32,43]. Several participants brought up the prospective issue of the resale value of homes and damages from the installation of the DUO. Relating the robot installation to other stability support devices that can be installed in the home, participants explained, \"So often you see people who had the chair that went up the stairs installed in their house, and then, when they moved on it's still there and it's kind of awkward\" [PU41]. Others explained that they simply had \"no interest in construction\" [PU40] on their homes or to have their ceilings \"messed up\" [PU24].  
The most appealing aspect of the DUO was the ability for a care receiver to sit on the freestanding part, _Scoot_ [PJ02, PU-07,09,26,35,38,39,41,42,43]. One participant stated that it would be \"very convenient to have a chair-type robot when my parents wake up in the morning\" [PJ02] as this could be a way to transport OAs from the bed to the bathroom, kitchen, laundry room, or other areas of the home [PU10]. Other participants spoke on the simple benefit of having a seat available [PU-35,39,42]. One stated that her father used to have a \"walker, one that had a seat built into it. And he found that very helpful. I mean, it wasn't a robot but having a chair nearby is always helpful\" [PU43]. Some participants wanted to know if the DUO would have further abilities \"in addition to the arms helping you if the seat does any kind of pushing or if there's assistance from both the seat and the arms\" [PU25]. Several participants enjoyed the idea of Scoot so much, that they wished it could be applied to the other embodiments; \"So it'd be nice if TRIbot had like a little seat\" [PU07] or that the seat could be an \"additional accessories that could be used\" with TRIbot [PU-06,42]. Another participant added, \"If PILLAR came with a folding chair, it would expand its versatility\" [PJ04].  
Furthermore, individuals specified that DUO was best suited for individuals with individuals who are bedridden and have less ability to move around [PU42, PJ-12,22,23], need full support [PJ-15,28], or have trouble getting out of chairs [PU33]. Participants were able to recognize 2 assistive devices that served as inspiration: the patient ceiling track systems for _Dex_, and chairs with power lift assistance for _Scoot_.
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 3 Methodology
### Spatial-Temporal-Semantic Feature Extraction
#### 3.1.3 Semantic Feature Matching  
Fixed points in the line should be represented in a way that the model can recognize. The OSM map data stores the semantic tags of each road in the network, as shown in Table 2. The unique ID of the road could be used to represent the line. Semantic tags of the fixed points are assigned by the ID of the road, \(tag\ of\ n_{si}=roadID_{i}\).  
After the spatial-temporal-semantic feature extraction, the trajectory is transformed into a location sequence represented by the road ID, \(T_{STS}=\{loc_{i}|loc_{i}=(roadID_{i},\ timebin_{id}.i)\}\). A running example is shown in Figure 9. A trajectory from a courier in Beijing, China is used to illustrate the process. The result of STS feature extraction is similar to the result of map matching algorithms [25; 26]. However, map matching only focuses on projecting the track points to the nearest road and the STS transfers trajectory into location sequence that prediction models can recognize.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tag (ID)** & **Name** & **Type** & **...** & **Coordinates** \\ \hline
391182785 & Zhong Guançun East Rd. & Motor &... & [116.330, 39.982], [116.330, 39.983],... \\
391182778 & Bao Fusi Rd. & Bike &... & [116.329, 39.983], [116.328, 39.983],... \\ \hline \hline \end{tabular}
\end{table}
Table 2: Semantic tags of the road networks.  
Figure 8: Process of the geographic feature mapping. (**a**) Projection of each track point to the nearest line; (**b**) calculated fixed points on the line by \(\mathit{Threshold}_{d}\); (**c**) mapping track points to the nearest fixed points.
# Cause Analysis of Pier Inclination of An Expressway Interchange Bridge: Case Study
## 5 Numerical Analysis
### Rigid Body Deformation of Pier  
Piers P27, P28, P29, P33, P34, and P35 underwent rigid body deformation when the pier construction was completed but the superstructure was not. There are four types of forensic engineering evidence:  
1. Pier P27: The forensic engineering investigation showed that the inclination of the top of Pier P27 is 3.0/4.5 cm, which means that the measured inclination value is 3.0 cm for one column and 4.5 cm for the other in the double-column pier, and the supports on the pier show no obvious slip or shear deformation. Meanwhile, the superstructure between Piers P27 and P30 does not move, so the pier did not deform after the superstructure was constructed.
2. Piers P33 and P35: Due to the restraint of the cap, the displacement of the column under non-torsional load should be the same in the double-column pier. However, in the forensic field investigation, the inclination of the top of Pier P33 is 2.6/7.6 cm, and that of Pier P35 is 0.4/5.0 cm. The difference in inclination between the two columns is up to 5 cm, so the pier deformed before the cap was constructed.
3. Piers P28 and P29: In the forensic field investigation, the inclination of the top of Pier P28 is 6.0/4.8 cm, and that of Pier P29 is 2.0/1.4 cm. Firstly, if the pier inclination occurs after the consolidation of the pier and superstructure, the deformation of the consolidated pier would induce the movement of the superstructure. However, when actually monitored the superstructure between Piers P27 and P30 did not move. Secondly, since the consolidated piers are constrained by the same superstructure, the deformation of the top of the pier would not differ too much, but the field data show a 4 cm difference between Piers P28 and P29.  
* Pier P34: In the forensic field investigation, the inclination of the top of Pier P34 is \(-11.5/-9.9\) cm. Firstly, if the pier inclination occurs after the consolidation of the pier and superstructure, the deformation of the consolidated pier would drive the superstructure along the direction of the pier number decreasing, resulting in a narrower expansion joint on Pier P33 and a wider one on Pier P35. However, the expansion joint on Pier P33 is actually wider. Then, no concrete cracking was found on the surface of Pier P34 during the field inspection. Thus, the pier inclination occurred before the consolidation of the pier and superstructure.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 16 Enhancement Prospects  
3 GPP and many industrial players are involved in ongoing discussions for Release 16 enhancements. The agenda includes the following objectives with their corresponding solutions.
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 3 Configuration Optimization
### Improved NSGA-III Algorithm
#### 3.3.3 Catastrophe  
The intrinsic properties of genetic algorithms render them vulnerable to the situation of \"premature convergence,\" particularly when confronted with multi-objective optimization problems. When a specific individual or its close relatives exert a dominant influence within the population, the algorithm may become trapped in a local optimum too early. As iterations progress, the diversity among individuals in the population decreases, leading to stagnation in evolution and hindering optimization performance--a challenge that the NSGA-III algorithm also encounters.  
To address this issue, this study proposes a catastrophe approach to intervene in premature convergence. When the number of duplicate individuals in the population exceeds a certain threshold, the subset of these duplicate individuals is reinitialized.  
The Euclidean distance of the normalized population to determine whether individuals are duplicate individuals is written as follows:  
\[D(x,y)=\sqrt{\sum_{i=1}^{n}{(x_{i}-y_{i})}^{2}} \tag{17}\]  
where \(i=1,2,3,4,5\) is the number of decision variables and \(x=[x_{1},x_{2},x_{3},x_{4},x_{5}]\) and \(y=[y_{1},y_{2},y_{3},y_{4},y_{5}]\) are normalized decision variable vectors of two different individuals. In this paper, we set the threshold as 0.05; if \(D(x,y)\leq 0.05\), we consider both as duplicate individuals.  
This strategy not only disrupts the premature convergence trend but also enables the algorithm to escape the current solution space, thereby facilitating continued exploration towards the global optimum.  
The flowchart of the improved NSGA-III algorithm is shown in Figure 5, below.  
Figure 4: Cosine-like mutation distribution index.  
Figure 5: Improved NSGA-III algorithm.
# Estimation of Association between Healthcare System Efficiency and Policy Factors for Public Health
###### Abstract  
_Objective:_ To assess the association between the healthcare system's efficiency and policy factors (the types of healthcare systems and various health policy indicators). _Methods:_ In this study, a data envelopment analysis (DEA) with bootstrapping was applied to the healthcare system's efficiency to correct the bias of efficiency scores and to rank countries appropriately. We analyzed data mainly from the OECD (Organization for Economic Co-operation and Development) Health Data from 2014. After obtaining the efficiency score result, we analyzed which policy factor caused the inefficiency of the healthcare system by Tobit Regression. _Results:_ Based on five types of healthcare system classification, the result suggested that the social health insurance (e.g., Austria, Germany, Switzerland) showed the lowest efficiency score on average when compared to other types of systems, but evidence of a statistically significant difference in healthcare efficiency among four types of healthcare systems was not found. It was shown that the pure technological efficiency of the healthcare system was negatively influenced by two main factors: user choice for basic insurance coverage and degree of decentralization to sub-national governments. _Conclusions:_ Our findings suggest that countries with relatively low healthcare system efficiency may learn from countries that implement policies related to a low level of user choice and a high level of centralization to achieve more economical allocation of their healthcare resources.  
Keywords:healthcare system efficiency (HSE); DEA bootstrap technique; Tobit model; health policy; OECD countries +
Footnote †: journal: Applied Sciences
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 7 Conclusions  
Based on the results obtained in Section 6, different conclusions may be obtained:  
* Regarding the usability of the PHY modes without convolutional coding, the results show that the transmission modes without convolutional encoding are not valid for practical use. The consequences of not using convolutional encoding are particularly severe in situations of impulsive or frequency selective channels, as the channel erases or distorts one of the carriers, and it is not possible to decode any PPDU without any kind of error correction.
* PRIME standard shows high robustness against tough channel conditions (frequency selective channel or channel with abrupt phase change), being the decoding sensitivity usually impacted by less than 3 dB.
* The presence of synchronous impulsive noise and its duration or waveform are very important for analyzing the PRIME communications sensitivity.  
These conclusions lead to a better understanding of the PRIME standard behavior to individual channel effects. First, the obtained results can be used as reference values in field deployment once the main effects of these deployments are identified. Second, the understanding of the results also open the door to future improvements in designing and development of equipment.  
**Acknowledgments: This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (project TEC2015-67868-C3-1-R), the Basque Government (project IT 683-13) and the University of the Basque Country (UPV/EHU) within the program for the specialization of the postdoctoral researcher staff.  
Figure 13: Relation FER-SNR with abrupt phase shift channel.  
**Author Contributions:** [PERSON] has developed the simulation tool, defined the analysis methodology, run the tests and analyzed the results; [PERSON] has contributed in the definition of channel models and in the analysis of the results; [PERSON] has validated the workbench according to PRIME standard; [PERSON] and [PERSON] have led the research work.  
**Conflicts of Interest:** The authors declare no conflict of interest. Similar PLC studies to this one are being carried by some of the authors of this article using other PLC technologies, such as ITU-T G.9903 (G3-PLC) and IEC 61334 (SFSK). Other technologies like ITU-T G.9902 (G.hnem) will be considered for future PLC studies.
# Analytical Calculation Model of the TV3-117 Turboshaft Working Regimes Based on Experimental Data
## 2 Engine Presentation  
The analytical calculation model is applied for the TV3-117 MT gas turbine engine. According to the manual [17], TV3-117 MT is a free turbine turboshaft engine, produced by the Klimov manufacturer [18], which was installed on 90% of helicopters developed by the Mil and Kamov Engineering Centre. The TV3-117 MT engine from Figure 2 is a two-spool turboshaft, formed by a gas generator module and a free power turbine module. The gas generator consists of 12 stages of axial compressor, of which four stator stages are with variable geometry, an annular chamber, and two stages of axial turbine. The free power turbine module consists only of two stages of axial turbine and a transmission module.  
Figure 1: (**a**) Gas Turb Program graphical interface; (**b**) Gas Turbine Simulation Program (GSP) graphical interface.  
At I.S.A. sea level conditions, the engine provides a power of 2225 HP at a maximum engine rotational speed of 19.500 rpm and at an overall pressure ratio of 9.45:1, for an air mass flow of 8.75 kg/s and a specific fuel consumption of 0.230 kg/HP/h.  
The fuel engine control system is defined by constant free turbine speed law, adjusted between 96 \(\pm\) 100%, depending on the engine regime. The gas generator speed at 100% is 19.500 rpm, and at 100%, the free turbine speed is 15.000 rpm.  
In Table 1 is presented the operating regimes, at sea level condition, defined by the values of some main engine parameters extracted from the engine manual [17].  
The engine manufacturers present the engine constructive configuration delimited by a series of stations marked with numbers. Usually, these stations are reference points of the thermodynamic model for design point calculations [20]. In Figure 3 are presented the stations of TV3-117 MT turboshaft, defined by the inlet and the outlet of engine rotor components. In this case, station 1 is the compressor inlet, station 2 is the compressor outlet, station 3 is the gas generator turbine inlet, station 4 is the gas generator turbine outlet and also the power turbine inlet, and station 45 is the power turbine outlet.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Engine Regimes** & **P [HP]** & **SFC [kg/HP/h]** & **NG [\%]** & **NTP [\%]** & **T3t [\({}^{\circ}\)C]** & **F\({}_{\text{f}}\) [kg/h]** & \(\eta_{\text{te}}\) [-]** \\ \hline Takeoff & 2225 & 0.230 & 97.6 \(\pm\) 0.5 & 98 \(\pm\) 1 & 975 & 511.8 & 0.2673 \\ Takeoff (L) & 1950 & 0.240 & 96.1 \(\pm\) 0.5 & 98 \(\pm\) 1 & 935 & 468.0 & 0.2561 \\ Nominal & 1700 & 0.250 & 94.7 \(\pm\) 0.5 & 100 \(\pm\) 2 & 900 & 425.0 & 0.2459 \\ Cruise 1 & 1500 & 0.270 & 93.5 \(\pm\) 0.5 & 100 \(\pm\) 2 & 870 & 405.0 & 0.2277 \\ Cruise 2 & 1200 & 0.290 & 91.5 \(\pm\) 0.5 & 100 \(\pm\) 2 & 830 & 348.0 & 0.2120 \\ Idle & 200 & 0.83 & 73 \({}^{+6}\) & 45 \({}^{+15}\) & 700 & 165.0 & 0.7450 \\ \hline \hline \end{tabular}
\end{table}
Table 1: TV3-117 MT operating regimes.  
Figure 3: TV3-117 MT engine stations.  
Figure 2: TV3-117 MT turboshaft engine [19].
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 2 Materials and Experimental Methods
### Experimental Methods  
#### 2.2.1 Determination of Optimum Proportion (SL/S) between SL and Intact Soil  
The proportion of improved soil was determined by its compaction performance, boundary water content characteristics and bearing capacity characteristics, which were examined through a compaction test, liquid-plastic limit combined measurement test and California bearing ratio (CBR) test.  
(1) Compaction test  
According to the Chinese specification (JTG 3430-2020), the maximum dry density and maximum water content can be determined by using the method of T 0131-2019. For each mixed proportion, five samples with an interval of 2% water content were first prepared in which each specimen was made by 98 compaction times. After compaction and demolding, the mass and water content of the specimens were measured, and then the maximum dry density curve under different water contents was determined.  
(2) Liquid-plastic limit combined measurement test  
According to the Chinese specification (JTG 3430-2020), the liquid-plastic limit combined measurement test in T 0118-2007 was used. Before the test, the samples were passed through a 0.5 mm sieve to prepare three specimens, in which the water contents were controlled at the liquid limit (point a), slightly greater than the plastic limit (point c) and the intermediate state (point b), respectively. A cone with a mass of 100 g was used in the test, where the cone penetration depth of point a was (20 \(\pm\) 0.2) mm, and the cone penetration depth of point c was less than 5 mm. Based on the test results, the cone penetration depth versus water content referring to points a, b, and c could be drawn on double logarithmic coordinates. Points (_a_, \(b\) and _c_) had to be in a straight line. Otherwise, they had to be adjusted to a hyperbola _h\({}_{p}\)-w\({}_{L}\)_ to obtain the corrected direct line (_ad_). The liquid limit corresponded to the penetration depth of 20 mm, and the plastic limit was determined according to the double curve specified in the specification. The process is briefly presented in Figure 4.  
(3) CBR test  
The specimens were prepared by the same forming method as the compaction test. For each proportion, the specimens were molded in the state of maximum dry density and optimum water content. After immersion for 96 h, the immersion height and water absorption mass were measured, and then a CXY-128B pavement material strength tester was used for testing and automatically recording the rod penetration depth and corresponding data. Thus, the corresponding testing values when penetrated at 2.5 mm and 5.0 mm were obtained.  
Figure 4: The process of determining the liquid and plastic limit of test soils.
###### Abstract
A random phase shift for each transmitted OFDM packet is implemented to assess the effectiveness of the neural network. * This work uses three DL optimization techniques and the crossentropyvex loss function to determine the most accurate and robust estimator with unknown channel statistical properties and a limited number of pilots. * A comparative study was conducted with three DL optimization algorithms, namely SGDm, RMSProp, and Adam, on 64, 8, and 4 pilots, respectively, to investigate the efficiency of the proposed estimator. The suggested DL peephole LSTM estimator performance was compared with its competitors from previous works, LSTM [4] and gated recurrent unit (GRU) estimators [5]. * Examine the performance of the suggested framework for CE in various scenarios. The symbol error rate (SER) is specifically modelled to examine the accuracy of CE. * The simulation's final findings show the following:  
Outlines of the article: Section 2 discusses related work. Section 3 introduces the channel estimation methods and system model. The results are discussed in Section 4. The paper concludes in Section 5.
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 3 Clustering in Stable Instances
### Warm-Up: Phase 2 of Single-Linkage Algorithm
#### 3.1.2 First Improvement (Combining Low and High Cost Operations)  
Using Lemma 2, we can sort the elements in an array using only \(O(n\log n)\) low-cost operations, and obtain a sequence with maximum dislocation \(d=O(\log n)\). Having sorted the array in this way, we can restrict to the first \(m=k-1+d\), elements (since we know that the \(k-1\) heaviest elements are in this interval). By applying the previous Min Heap strategy to these \(m\) elements, we can find the true \(k-1\) heaviest edges. This strategy leads to the following slightly more general result, which we shall use below as a subroutine:  
**Lemma 4** (find top-\(t\) elements).: _Given any set of \(n\) elements, and any confidence parameter \(\Delta\), with probability at least \(1-\frac{1}{n^{\Delta}}\), we can extract the top-\(t\) (largest or smallest) elements using \(O(t+\Delta\log t\log n,n\log n)\) high-low cost operations._  
Proof.: According to Lemma 2, with probability at least \(1-\frac{1}{n^{\Delta}}\), we can obtain a sequence where every element has dislocation at most \(d=O(\Delta\log n)\) using \(O(n\log n)\) low-cost operations only. To find the \(t\) largest elements, we consider the first \(m=t+d\) elements and apply the Min Heap strategy to these only. This takes \(O(t+(m-t)\log t)=O(t+d\log t)\) high-cost operations only as observed above. The overall cost is thus  
\[O(0,n\log n)+O(t+d\log t,0)=O(t+\Delta\log t\log n,n\log n)\.\]  
The previous lemma yields the following result.  
**Theorem 2**.: _Phase 2 of Single-Linkage algorithm can be implemented using_  
\[O(k+\log k\log n,n\log n)\]  
_high-low cost operations, and its success probability is at least \(1-\frac{1}{n}\)._