esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
bottom walls are made of ductile cast iron, which provides better shielding against gamma photons. Meanwhile, metal-concrete structures of CONSTOR(r) RBMK-1500 are better neutron shielding materials (see Figures 6 and 7). Contributions of neutrons and gammas to the total dose rate also vary with time. For instance, neutron and gamma dose rate contributions to the total dose rate on the side wall (see Figure 6) of the cast iron (CASTOR(r)) container are 50%/50%, whereas those for the metal-concrete (CONSTOR(r)) container are 2%/98% at the beginning of storage. After 300 years of storage, these contributions are 99.5%/0.5% for CASTOR(r) and 81.5%/18.5% for CONSTOR(r). Decay half-times of the main \(\gamma\)-emitters are shorter than neutron-emitting isotopes; therefore, gamma emissions over time decrease more rapidly than neutron emissions. Consequently, the total dose rates for extended storage periods are mainly determined by neutrons. Figure 4: Energy groups that determine the neutron dose rate (low-energy neutrons are efficiently shielded). Figure 5: Neutron (**left**) and gamma (**right**) inputs to the total dose rate: top of the containers. The variation of the total dose rate with storage time for cast iron and metal-concrete containers is presented in Figure 8. As the figure demonstrates, at the beginning of the storage when the intensity of radiation from the spent fuel is highest, the total dose rates at any surface point of both containers are less than 1 mSv/h. The total dose rates on the surface of the metal-concrete container are less than those for the cast iron container in all cases. The smallest difference between the total dose rate values occurs at the top of the containers, and this difference is constant throughout the storage period. The lids of both containers are made from steel, but the overall thickness of the lids for the metal-concrete CONSTOR(r) RBMK-1500 container is slightly greater, and this results in lower dose rates at the top of the container. More significant differences in dose rate values can be observed at  
Figure 6: Neutron (**left**) and gamma (**right**) inputs to the total dose rate: side of the containers. Figure 7: Neutron (**left**) and gamma (**right**) inputs to the total dose rate: bottom of the containers. the side and bottom walls of the containers. Moreover, these differences vary with storage time, i.e., the total dose rates range from 2-7 times for storage periods up to 50 years, whereas, for extended storage periods, the difference grows to 60 times. This is mainly due to two reasons. Firstly, gamma emission intensity over time decreases more rapidly than neutron emission. Secondly, the materials of the container side and bottom walls provide different properties for gamma and neutron shielding. For instance, a metal-concrete structure absorbs neutrons more effectively than ductile cast iron.
## 3 Results
The system (B) in conditions ranging from 22% to 35% of the idle work time was characterized by fuel consumption from 1.252 \(\pm\) 0.0117 l\(\cdot\)h\({}^{-1}\) to 1.279 \(\pm\) 0.0115 l\(\cdot\)h\({}^{-1}\). A 13% increase in idle operation time resulted in a 2.2% increase in fuel consumption. This result showed that the tested system, along with the increase in idle time, was characterized by an increase in fuel consumption. However, this consumption was lower than for the system (A). System (C), as assumed, was the most susceptible to changes in idle/load work time ratio in conditions ranging from 22% to 35% of idle work time. It was characterized by fuel consumption from 0.951 \(\pm\) 0.0509 l\(\cdot\)h\({}^{-1}\) to 0.515 \(\pm\) 0.0432 l\(\cdot\)h\({}^{-1}\). A 13% increase in idle work time reduced fuel consumption by approximately 45.9%. This result indicated that the adaptive system (C) showed the highest drop in fuel consumption under the presented conditions. The values of the standard deviation indicated that the system (B) was the least susceptible to changes in the amount of fuel consumption in the tested operating conditions and was on average \(\pm\) 0.0123 l\(\cdot\)h\({}^{-1}\). System (A) was characterized by an increase in the average standard deviation of 357.7% and amounted to \(\pm\) 0.0563 l\(\cdot\)h\({}^{-1}\). For system (C), it increases by 286.2% and was equal to 0.0475 l\(\cdot\)h\({}^{-1}\). On this basis, one can indicate the tendency of systems (A) and (C) to shift fuel consumption as a result of different work conditions. In system (A), the centrifugal mechanism reacted to load changes, while in system (C), the device responded to change of the rotational speed need of the drive. This contrasted to system (B) which during the operation was not susceptible to any of the above regulations. A detailed analysis of the systems focused on their comparison is presented in the next chapter. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Operating Conditions**} \\ \hline  & \multicolumn{3}{c}{**Percentage of Time for the Different Operating States: Chipping/Idle Work**} \\ \cline{2-4}
**Test No.** & **78\%/22\%** & **71\%/29\%** & **65\%/35\%** \\ \cline{2-4}  & \multicolumn{3}{c}{**Average Fuel Consumption [l h\({}^{-1}\)]**} \\ \hline AVG & 2.126 & 2.053 & 1.998 \\ SD & 0.0670 & 0.0506 & 0.0513 \\ I & 0.1492 & 0.1128 & 0.1143 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of fuel consumption of the cylindrical wood chipper with carburetor fuel supply system (A) in periodically-variable exploitation conditions (3); AVG–arithmetic average, SD–standard deviation of the mean, I–incertitude limits using the Student’s t distribution for a 95% confidence level.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.13 Path Planning II  
In [81], the authors propose an autonomous navigation technique for a mobile robot in an unknown environment. This involves using a LiDAR sensor to collect data, converting these data into a 2D grid map, and then training a neural network through DRL. This work is significant because it addresses the increasing need for autonomous robots to function in uncertain conditions when extensive environmental data are lacking. The approach aims to provide a system that helps robots in crucial scenarios that require navigation and route planning. It is also beneficial in conditions where sudden disturbances or low light levels could affect visual sensors.  
The authors in [81] used the DQN RL algorithm to train a mobile robot to navigate in an unfamiliar setting. As shown in Figure 13, this approach uses a 2D grid map generated from LiDAR data as input to the neural network. The network focuses on increasing rewards for successful exploration and imposing higher penalties for collisions. The authors used an ROS for communication between hardware and software components, data transmission, DQN algorithm processing, and vehicle movement control. The authors conducted experiments to compare the effectiveness of using a LiDAR grid map as input for the DQN compared to raw LiDAR data. The results show that the use of the grid map was more resilient and reliable in the investigation of the route, especially in the presence of obstacles. The raw LiDAR data method faced difficulties in real-world applications, with the grid map model avoiding collisions in complex environments, while the unprocessed data approach often struggled to navigate obstacles.  
Figure 13: ROS nodes and topics, hardware, and software architecture of the embedded robot. For more information, please refer to Table 4.  
The above-mentioned work concludes that the use of the LiDAR grid map picture method offers a more adaptable and resilient method for path discovery. Considering hybrid strategies that combine the strengths of both methods, such as converting raw LiDAR data into grid map representations in real time, could enhance the method's adaptability.
## 6 Conclusions  
This study investigated the influence of risk factors on the severity of rural SV crashes across different vehicle types (passenger car, motorcycle, pickup, and truck). To accommodate the cross-road segments heterogeneity, a novel SRP-logit model accounting for both unobserved heterogeneity and spatial correlation under a Bayesian framework was proposed.  
Five years of SV crash data in a rural area were used to calibrate the proposed model. Three candidate logit approaches--MN-logit model, RP-logit model, and RI-logit model--were established and compared with the SRP-logit model. The model comparison shows that the SRP-logit model exhibits the best fit performance, followed by the RP-logit model, RI-logit model, and MN-logit model. This finding suggests that it is necessary to capture the unobserved heterogeneity in the rural SV crash analysis, which can initially improve the fitting performance of the crash model. The model fit can be further improved by simultaneously accounting for the unobserved heterogeneity across individual observations and the spatial correlation among adjacent crashes.  
Several risk factors are significantly associated with FS injuries in rural SV crashes, and the impacts of risk factors on crash severity varies across different vehicle types. Specifically, in passenger car and pickup crashes, there is a positive association between male drivers and FS injuries; however, a significant negative correlation is shown in motorcycle collisions. Non-clear weather maintains a significant negative effect on FS injuries in passenger car, pickup, and truck crashes and a significant positive effect on FS injuries in motorcycle crashes. Similar findings can be found in the coefficients of season and early in month.  
In addition, there is a significant positive correlation between weekend, dark (without street lighting) conditions and collision with fixed object and FS injuries. Collisions with pedestrians are not expected to cause serious injury to drivers. These findings are shared across different vehicle types.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 5 Simulation Results
### Simulation 2: Effect of Duty-Cycling Operation  
Figure 11 shows the effect of the duty cycle \(D\) and the reflection coefficient \(\alpha\) on the BER and harvesting probability (or activation probability of RF tags) which is defined by \(p(\mathcal{E})=\mathbb{E}[a_{n}]\) for \(n=1,\cdots,N\). The duty cycle \(D=\frac{K_{1}}{K}\) is controlled by the signal dimension \(K_{1}\) and related to both the energy harvesting efficiency and data transmissions. The case of \(D=0.1\) is equivalent to the TD-AmBC system which shows 100% harvesting at tags (i.e., all tags can harvest sufficient energy required for their data transmissions) but exhibits very high BER, thereby limiting the communication range of AmBC. As the duty cycle of tags increases, the BER performance improves since the backscatter signals are conveyed over multiple OFDM carriers, as we confirmed at the previous simulation. However, due to frequent activation of RF circuits, the harvesting probability diminishes for large values of \(D>0.5\) and \(\alpha>-5\) dB. Fortunately, the energy loss in the duty-cycling operation can be effectively avoided by proper tuning of \(D\) and \(\alpha\) to satisfy the EH constraint in Equation (21). In addition, to design'sparse' codes, a small duty cycle, or equivalently a small \(K_{1}\) is preferred for low-complexity detection at the reader [23]. Hence, under these settings, the MSC-AmBC demonstrates superior detection performance to the TD-AmBC, while increasing the communication range of AmBC.  
Figure 10: Effect of the pilot power ratio \(\zeta\) and the reflection coefficient \(\alpha\) for detection performance at the reader. (If \(\zeta\approx 0.08\), equal power is allocated among data and pilot subcarriers). (**a**) BER performance in MSC-AmBC (\(K=10\), \(K_{1}=3\), \(M=2\)). (**b**) BER performance in TD-AmBC (\(K=10\), \(K_{1}=1\), \(M=2\)).
# Antibacterial Ceramics for Dental Applications
## 3 Results and Discussion
### Antibacterial Properties of Strontium
In addition to the mechanical properties, the high silica concentration is also responsible for a reduced dissolution rate. Replacing Ca ions with Sr ions was found to be remarkably effective in bone regeneration and angiogenesis [6,60]. The investigated concentrations of SrTiO\({}_{3}\) in 855 bioslass were 1, 3, 5, 10, 20, and 30 wt%. In this study, unmodified bioglass showed slight antibacterial activity against _E. coli_ and _S. aureus_ (Figure 14) compared to the control sample. Figure 13: Images of _E. coli_ bacteria grown, (**a**) blank, (**b**) BG, (**c**) 5 Sr-BG, (**d**) 10 Sr-BG, (**e**) 5 Zn-BG, (**f**) 10 Zn-BG [61]. Antibacterial activity further increased with an increase in the SrTiO\({}_{3}\) content in the bioglass up to 10 wt% of STO (STO_10). Interestingly, samples with higher amounts of STO in the bioglass (STO_20 and STO_30) exhibited a significantly lower antibacterial effect compared to STO_10. STO_20 and STO_30 have antibacterial activity compara-ble to that of unmodified or only slightly STO-modified bioglass [6]. One can understand the behavior in terms of the release rate of Sr cations from the bioglass. At an STO content of up to 10 wt%, the Sr cations released from the glass increased the pH value of the environment, resulting in protein denaturation, cell membrane destruction, and DNA as well as RNA disruption. All these processes hinder vital cell functions, including enzyme activity and energy regeneration, ultimately leading to bacterial death. The adhesion of SrTiO\({}_{3}\) particles to the cell surface can also hinder the transportation of vital nutrients, which can lead to a rupture in the cell membrane and cell death [6]. At amounts higher than 10 wt%, the Sr cation release rate was significantly reduced due to slower dissolution of the composites, resulting in a decline in the pH value, so that the mechanism leading to cell death described above occurred to a lesser extent. The antibacterial activity of the samples was demonstrated for 7 days; therefore, long-term measurements are required. The cytotoxicity investigation showed that the samples STO_20 and STO_30 exhibited toxicity to red blood cells, but more detailed studies on the toxicity and concentration of SrTiO\({}_{3}\) are required.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
###### Abstract  
Orthogonal Time-Frequency Space (OTFS) is an innovative modulation method that ensures efficient and secure communication over a time-varying channel. This characteristic inspired us to integrate OTFS technology with underwater acoustic (UWA) communications to counteract the time-varying and overspread characteristics of UWA channels. However, implementing OTFS in UWA communications presents challenges related to overspread channels. To handle these challenges, we introduce a specialized OTFS system and offer frame design recommendations for UWA communications in this article. We propose a Doppler compensation method and a dual-domain joint channel estimation method to address the issues caused by severe Doppler effects in UWA communication. Additionally, we propose an OTFS system detection approach. This approach incorporates an iterative detection process which facilitates soft information exchange between a message passing (MP) detector and a low-density parity check (LDPC) decoder. By conducting simulations, we demonstrate that the proposed UWA OTFS system significantly outperforms Orthogonal Frequency-Division Multiplexing (OFDM), Initial Estimate Iterative Decoding Feedback (IE-IDF-MRC), and two-dimensional Passive Time Reversal Decision Feedback Equalization (2D-PTR-DFE) in UWA channels.  
orthogonal time-frequency space; underwater acoustic communication; channel estimation; iterative decoding feedback +
Footnote †: journal: Marine Science and Engineering  
0
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 4 Materials and Methods
### Experimental Design  
In this research, an efficient health monitoring model is proposed to monitor heart patients. The model is based on a deep CNN as shown in Figure 4. In addition, LSTM, RNN, and multilayer perceptron (MLP) are also employed. The MLP model consists of an input layer, output layer, and hidden layer. The hidden layer is the processing layer and connects the input and output layers. The sum of input values with respect to weight and output is shown in Equation.  
\[y_{j}=f(\sum w_{ij}*O_{i}), \tag{4}\]  
where \(O\) is the hidden layer and \(w\) is the weight value.  
Experimental results are calculated on multiple-layer structures, but hidden-layer structures give suitable outcomes. Features of a heart failure dataset are provided as input to the hidden layer structure. The parametric values can be set such that the number of epochs is 25, the learning rate is 0.01, the batch size is 256, and the dropout is equal to 0.2. Another performance evaluation technique has been implemented based on the CNN approach to test its performance. A total of 8 layers are included in the CNN-based approach. The initial parametric values of the CNN-based approach are presented in Table 5.  
The heart failure dataset is provided as the input in the first stage. Thirteen features of the heart failure dataset are used to train the data to provide more accurate predictions. Acute heart failure patients need early diagnosis and proper treatment to increase patients' survival rate.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Embedding dimension & 300 \\ Pooling & 2 \(\times\) 2 \\ Optimizer & Adam \\ No. of filters & 5 \(\times\) 64 \\ Epochs & 25 \\ Max\_Sequence\_length & 11 \\ Function & Binary cross entropy \\ Batch size & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parametric values for CNN.  
Figure 4: Architecture of deep learning CNN model.
# A Conceptual Design Methodology for e-VTOL Aircraft for Urban Air Mobility
## 4 Sensitivity Analyses for Design Space Exploration
### Current Powertrain Technology
This is due the fact that MTOW decreases as wing loading increases; therefore, assuming to keep the aspect ratio of both wings unvaried, also wingspan decreases. Propeller clearance (Figure 10c) depends on both parameters. It is worth noting that this chart is very helpful from the aircraft design perspective since a proper clearance is prescribed by regulations as well as it is required to avoid detrimental effects on propeller performance. As shown in the general example of Figure 10c, the region of tip propeller clearance lower than 0.1 m is limited, so a wide design area can be exploited. As shown in Table 8, speed and range have been considered as variables within prescribed intervals, which allows to perform sensitivity analyses for these inputs. The main results are reported in Figures 12 and 13. Each figure depicts four wing loading-disk loading charts: speed changes from left to the right, whereas range is varied from top to bottom. Furthermore, contour lines, which represent the constraints detailed in Table 9 have been superimposed to the chart; it allows to obtain the actual design space (the highlighted area) and select one or more configurations according to the design criteria. Figure 12: \(W\)/\(S\) vs. \(T\)/\(A\) charts of MTOW (current technology) varying speed and range. Each plot shows a highlighted area, which identifies the design space compliant with the constraints. Figure 12 shows that the design space reduces as range and speed increase and, in addition:  
* the MTOW constraint (<3175 kg according to Table 8) does not affect the boundaries of the design space, since it is well above the values reached in the feasible area;
* the \(T/A\)-\(W/S\) region in which the minimum propeller clearance constraint is fulfilled, is not affected by range and speed variations. This is due to the fact that both wingspan and propeller diameter changes in the same manner, so no relevant variations can be noted for the propeller clearance;
* as the range or speed increases, the needed wingspan increases as well; therefore, the limitation curve moves towards higher \(W/S\) and lower \(T/A\), reducing the design space. In particular, the maximum wingspan constraint moves to the right since the energy demand to accomplish the mission increases as speed (or range) increases; consequently, higher wing loading is necessary to reduce wing surface and wingspan;
* since energy demand increases with speed and range, also battery mass, whose trends are shown in Figure 13 increases, providing higher MTOW values, as Figure 12 shows.
## 6 The Correlation between Planning Investment Intensity and Level of Development  
### Use the HDI to Represent the Country's Level of Development  
HDI (Human Development Index, HDI) was developed by the United Nations Development Programme (UNDP) since 1990 [28]. According to the UN, the creation of HDI was based on the fact that although economic development of human society is necessary, it should not be the only focus; other aspects of social development should be taken into consideration. Therefore, other criteria should be considered to evaluate the development of a society. The HDI can also be used to question countries' policy choices and to test, for example, how two countries with the same level of GNI per capita result in different human development outcomes. Such comparisons can fuel debate about the priorities of government policy.  
The HDI has compiled three groups of indicators into a single composite index to more accurately reflect a society's achievements in terms of health, education, and economy. HDI is an aggregated measure of average achievement in key aspects of human development: whether people in a country or region are living long, healthy, knowledgeable, and well-off. The HDI value is the geometric average of normalized indicators for each of the three dimensions. The health dimension is assessed by life expectancy at birth, while the education dimension is measured by years of schooling for adults aged 25 and over and the expected years of schooling for school-age children. The standard of living is measured by GNI per capita. The HDI uses the logarithm of income to reflect the declining importance  
Figure 4: Planning research input and urbanization path choice [30]. Source: based on data from Web of Science.  
of income as GNI increases. Then, the scores of the three HDI dimensional indexes are aggregated into a composite index using geometric averages.  
Therefore, HDI can largely represent the level of development in a country, because the development is not only the economic development, but also the health and longevity of people, knowledgeable and good living standards.
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 3 System Model  
In this section, we first introduce the data transmission architecture of TSIN, then we propose the space-time topology model of TSINs, and, constrained by the intermittent connectivity, the hybrid routing problem will be formulated.